Biology subjects

Nair, A. G.

Publications and source records attributed to Nair, A. G..

6 recordsLinked to original sources

bioRxiv2026

Sub-Millivolt Voltage Imaging Reveals Gap Junction-Mediated Bioelectric Contact Inhibition

Sub-millivolt membrane potential (Vm) dynamics in multicellular non-excitable networks have remained largely invisible due to a lack of sufficiently sensitive imaging tools. Here, we introduce rEstus2s, a next-generation genetically encoded voltage indicator that overcomes this barrier by enabling high-resolution Vm imaging across the full physiological resting Vm range (-100 to 0 mV). Using rEstus2s, we uncover bioelectric contact inhibition (BCI), a fundamental biophysical principle where gap junction coupling acts as a passive noise filter to stabilize Vm. We demonstrate that the time-dependent variance of Vm (electrical volatility) relative to the number of cells (n) in a network follows a 1/n scaling law, reflecting a transition from stochastic single-cell behavior to collective electrical stability. While Ca2+-activated oncogenic ion channels, including ANO1 and KCa3.1, promote pronounced electrical volatility in isolated cells, BCI effectively attenuates volatility in electrically coupled networks. Disruption of gap junction coupling in cancer cells abolishes BCI and restores high electrical volatility. These findings establish a unifying biophysical framework for understanding how multicellularity maintains electrical homeostasis in health and disease. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=168 SRC="FIGDIR/small/701308v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@120fcb9org.highwire.dtl.DTLVardef@affa2aorg.highwire.dtl.DTLVardef@150ffa7org.highwire.dtl.DTLVardef@c05392_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

An ultrasensitive genetically encoded voltage indicator uncovers the electrical activity of non-excitable cells

Genetically encoded voltage indicators (GEVIs) are powerful, non-invasive tools for recording action potentials in excitable cells. However, most animal cell types are non-excitable, and yet variations in the membrane potential are biologically relevant in these cells as well. Resolving such small voltage signals demands GEVIs with exceptionally high sensitivity. In this study, we applied structure-guided engineering to the GEVI ASAP3 to generate rEstus, a sensor with optimized brightness, voltage sensitivity, and voltage range. rEstus is most sensitive in the resting voltage range of non-excitable cells, exhibits a 3.6-fold improvement in fast voltage spike detection, and allows for absolute voltage calibration at the single-cell level. Using rEstus, we resolved endogenous voltage fluctuations in several non-excitable cell types and demonstrate that correlation analysis of these optically recorded fluctuations provides an easy, non-invasive, real-time readout of electrical gap-junction coupling. Our work provides greatly enhanced tools and methods for the non-invasive study of electrical signaling in excitable and non-excitable cells.

biophysics↗

bioRxiv2020

Distinct molecular pathways govern presynaptic homeostatic plasticity

Presynaptic homeostatic plasticity (PHP) stabilizes synaptic transmission by counteracting impaired neurotransmitter receptor function through increased presynaptic release. PHP is thought to be triggered by impaired receptor function, and to involve a stereotypic signaling pathway. However, here we demonstrate that different receptor perturbations that similarly reduce synaptic activity result in vastly different responses at the Drosophila neuromuscular junction. While receptor inhibition by the glutamate receptor (GluR) antagonist {gamma}-DGG is not compensated by PHP, the GluR inhibitors PhTx-433 and Gyki induce compensatory PHP. Intriguingly, PHP triggered by PhTx and Gyki involve separable signaling pathways, including inhibition of distinct GluR subtypes, differential modulation of the active-zone scaffold Bruchpilot, and short-term plasticity. Moreover, while PHP upon Gyki treatment does not require genes promoting PhTx-induced PHP, it involves presynaptic Protein Kinase D. Thus, synapses not only respond differentially to similar activity impairments, but achieve homeostatic compensation via distinct mechanisms, highlighting the diversity of homeostatic signaling.

neuroscience↗

bioRxiv2020

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

Neuroscience incorporates knowledge from a range of scales, from molecular dynamics to neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models, using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implemented custom-made MATLAB(R) scripts to perform parameter estimation and global sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. Using this workflow, we can simulate the same model in three different simulators, with a smooth conversion between the different model formats, enhancing the characterization of different aspects of the model. Information Sharing StatementBoth the source code and documentation of the Subcellular Workflow are available at https://github.com/jpgsantos/Subcellular_Workflow and licensed under GNU General Public License v3.0. The model is stored in the SBtab format (Lubitz et al. 2016). Model reduction, parameter estimation and global sensitivity analysis tools are written in MATLAB(R) (RRID:SCR_001622) and require the SimBiology(R) toolbox. Conversion script to VFGEN (Weckesser 2008), MOD and SBML (RRID:SCR_007422) is written in R (RRID:SCR_001905). Conversion to SBML requires the use of libSBML (RRID:SCR_014134). Validations are run in COPASI (RRID:SCR_014260; Hoops et al. 2006), NEURON (RRID:SCR_005393; Hines and Carnevale 1997) and with the subcellular simulation setup application (RRID:SCR_018790; available at https://subcellular.humanbrainproject.eu/model/simulations) that uses a spatial solver provided by STEPS (RRID:SCR_008742; Hepburn et al. 2012) and network-free solver NFsim (available at http://michaelsneddon.net/nfsim/). The medium spiny neuron model (Lindroos et al. 2018) used in NEURON simulations is available in ModelDB database (RRID:SCR_007271) with access code 237653. The FindSim use case model is available in https://github.com/BhallaLab/FindSim (Viswan et al. 2018).

systems biology↗

bioRxiv2019

Regulation of adenylyl cyclase 5 in striatal neurons confers the ability to detect coincident neuromodulatory signals

Long-term potentiation and depression of synaptic activity in response to stimuli is a key factor in reinforcement learning. Strengthening of the corticostriatal synapses depends on the second messenger cAMP, whose synthesis is catalysed by the enzyme adenylyl cyclase 5 (AC5), which is itself regulated by the stimulatory Golf and inhibitory Gi proteins. AC isoforms have been suggested to act as coincidence detectors, promoting cellular responses only when convergent regulatory signals occur close in time. However, the mechanism for this is currently unclear, and seems to lie in their diverse regulation patterns. Despite attempts to isolate the ternary complex, it is not known if Golf and Gi can bind to AC5 simultaneously, nor what activity the complex would have. Using protein structure-based molecular dynamics simulations, we show that this complex is stable and inactive. These simulations, along with Brownian dynamics simulations to estimate protein association rates constants, constrain a kinetic model that shows that the presence of this ternary inactive complex is crucial for AC5s ability to detect coincident signals, producing a synergistic increase in cAMP. These results reveal some of the prerequisites for corticostriatal synaptic plasticity, and explain recent experimental data on cAMP concentrations following receptor activation. Moreover, they provide insights into the regulatory mechanisms that control signal processing by different AC isoforms.\n\nAuthor summaryAdenylyl cyclases (ACs) are enzymes that can translate extracellular signals into the intracellular molecule cAMP, which is thus a 2nd messenger of extracellular events. The brain expresses nine membrane-bound AC variants, and AC5 is the dominant form in the striatum. The striatum is the input stage of the basal ganglia, a brain structure involved in reward learning, i.e. the learning of behaviors that lead to rewarding stimuli (such as food, water, sugar, etc). During reward learning, cAMP production is crucial for strengthening the synapses from cortical neurons onto the striatal principal neurons, and its formation is dependent on several neuromodulatory systems such as dopamine and acetylcholine. It is, however, not understood how AC5 is activated by transient (subsecond) changes in the neuromodulatory signals. Here we combine several computational tools, from molecular dynamics and Brownian dynamics simulations to bioinformatics approaches, to inform and constrain a kinetic model of the AC5-dependent signaling system. We use this model to show how the specific molecular properties of AC5 can detect particular combinations of co-occuring transient changes in the neuromodulatory signals which thus result in a supralinear/synergistic cAMP production. Our results also provide insights into the computational capabilities of the different AC isoforms.

neuroscience↗

bioRxiv2018

Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models

MotivationDynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis in order to give better informed predictions; to point out weaker parts of the model that are important targets for further experiments, as well as give guidance on parameters that are essential in distinguishing different qualitative output behaviours.\n\nResultsWe used approximate Bayesian computation (ABC) to estimate the model parameters from experimental data, as well as to quantify the uncertainty in this estimation (inverse uncertainty quantification), resulting in a posterior distribution for the parameters. This parameter uncertainty was next propagated to a corresponding uncertainty in the predictions (forward uncertainty propagation), and a global sensitivity analysis was performed on the prediction using the posterior distribution as the possible values for the parameters. This methodology was applied on a relatively large and complex model relevant for synaptic plasticity, using experimental data from several sources. We could hereby point out those parameters that by themselves have the largest contribution to the uncertainty of the prediction as well as identify parameters important to separate between qualitatively different predictions.This approach is useful both for experimental design as well as model building.

systems biology↗