Biology subjects

Basu, T.

Publications and source records attributed to Basu, T..

6 recordsLinked to original sources

bioRxiv2023

Loss of PV interneurons in the BLA contributes to altered network and behavioral states in chronically epileptic mice

Psychiatric disorders, including anxiety and depression, are highly comorbid in people with epilepsy. However, the mechanisms mediating the shared pathophysiology are currently unknown. There is considerable evidence implicating the basolateral amygdala (BLA) in the network communication of anxiety and fear, a process demonstrated to involve parvalbumin-positive (PV) interneurons. The loss of PV interneurons has been well described in the hippocampus of chronically epileptic mice and in postmortem human tissue of patients with temporal lobe epilepsy (TLE). We hypothesize that a loss of PV interneurons in the BLA may contribute to comorbid mood disorders in epilepsy. To test this hypothesis, we employed a ventral intrahippocampal kainic acid (vIHKA) model of chronic epilepsy in mice, which exhibits profound behavioral deficits associated with chronic epilepsy. We demonstrate a loss of PV interneurons and dysfunction of remaining PV interneurons in the BLA of chronically epileptic mice. Further, we demonstrate altered principal neuron function and impaired coordination of BLA network and behavioral states in chronically epileptic mice. To determine whether these altered network and behavioral states were due to the loss of PV interneurons, we ablated a similar percentage of PV interneurons observed in chronically epileptic mice by stereotaxically injecting AAV-Flex-DTA into the BLA of PV-Cre mice. Loss of PV interneurons in the BLA is sufficient to alter behavioral states, inducing deficits in fear learning and recall of fear memories. These data suggest that compromised inhibition in the BLA in chronically epileptic mice contributes to behavioral deficits, suggesting a novel mechanism contributing to comorbid anxiety and epilepsy. Significance StatementPsychiatric illnesses and epilepsy are highly comorbid and negatively impact the quality of life of people with epilepsy. The pathophysiological mechanisms mediating the bidirectional relationship between mood disorders and epilepsy remain unknown and, therefore, treatment options remain inadequate. Here we demonstrate a novel mechanism, involving the loss of PV interneurons in the BLA, leading to a corruption of network and behavioral states in mice. These findings pinpoint a critical node and demonstrate a novel cellular and circuit mechanism involved in the comorbidity of psychiatric illnesses and epilepsy.

neuroscience↗

bioRxiv2023

Semi-automated seizure detection using interpretable machine learning models

Numerous methods have been proposed for seizure detection automation, yet the tools to harness these methods and apply them in practice are limited. Here we compare four interpretable and widely-used machine learning models (decision tree, gaussian naive bayes, passive aggressive classifier, stochastic gradient descent classifier) on an extensive electrographic seizure dataset collected from chronically epileptic mice. We find that the gaussian naive bayes model achieved the highest precision and f1 score, while also detecting all seizures in our dataset and only requires a small amount of data to train the model and achieve good performance. We use this model to create an open-source python application SeizyML that couples model performance with manual curation allowing for efficient and accurate detection of electrographic seizures. Author SummarySeizure detection based on electrographic recordings is critical for epilepsy diagnosis and research. However, the current gold standard for seizure detection is manual curation, which is biased, costly, incredibly laborious, and requires extensive training and expertise, prohibiting advances in epilepsy diagnosis and research. Here we demonstrate that fast, simple, and interpretable machine learning (ML) models are sufficient to detect all seizures in an extensive dataset collected from a well-established mouse model of epilepsy. Importantly, we created an open-source python application, SeizyML, that integrates the most precise model tested here with human curation of the detected events. This semi-automated approach greatly enhances efficiency and precision of seizure detection while also being transparent. We believe that the adoption of semi-automated and transparent technologies is indispensable for understanding ML model predictions, improving their reliability, and fostering trust between ML models and neurodiagnostic professionals.

neuroscience↗

bioRxiv2022

Anti-Biofilm Potential of Nanonized Eugenol against Pseudomonas aeruginosa

This study dealt with nanonization of eugenol, a major phytochemical present in basil leaf, which has pharmacological potential as an anti-bacterial agent. Eugenol nanoparticle (ENP) was synthesized by simple ultrasonic cavitation method through emulsification of hydrophobic eugenol into hydrophilic gelatin. Thus, the nanonization process made the water-insoluble eugenol to water-soluble nano-eugenol, making the nano-form bioavailable. The average size of the ENPs was 20-30 nm. Entrapment efficiency of eugenol within gelatin cap was about 80% of the eugenol, that was used as precursor in the nanonization reaction. In vitro release of eugenol from gelatin cap was slow and sustained over a period of five days. The ENP had higher anti-biofilm potency than eugenol for both formation and eradication of biofilm, formed by clinically relevant pathogen Pseudomonas aeruginosa. Minimal biofilm inhibitory concentration and minimal biofilm eradication concentration of ENPs were 2.0 and 4.0 mM respectively. In addition, the measurement of P. aeruginosa biofilm biomass, biofilm pellicle formation, biofilm thickness, amount of biofilm-forming extra-polymeric substance, cell surface hydrophobicity, cell swarming and twitching efficiencies, cellular morphology and biofilm formation in catheter demonstrated that the anti-biofilm efficacy of nano-eugenol was 30-40% higher than that of bulk eugenol. Thus, ENP can be used as a potential drug against pneumonia, a chronic infection in lung caused by P. aeruginosa, which is difficult to treat with antibiotics, due to natural intrinsic resistance of biofilm-formed cells to most antibiotics. The overall actions of ENP have been presented in the figure 1. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/521144v1_fig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@12445e2org.highwire.dtl.DTLVardef@6813aforg.highwire.dtl.DTLVardef@e4779borg.highwire.dtl.DTLVardef@1688a9a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Graphical abstract showing overall anti-biofilm action of ENP that is capable to inhibit each and every step of biofilm formation. ENP also carries the potential to destroy EPS and kill the biofilm residing cells by membrane disruption. Bacterial colonization has been found to be prevented significantly on ENP-coated catheter. C_FIG HighlightsO_LINano-formulation of eugenol, an important phytochemical, by ultrasonic cavitation method, which was simple, time-saving, low-cost and eco-friendly. C_LIO_LINanonization made water-insoluble eugenol into water-soluble form with enhanced therapeutic efficacy. C_LIO_LIThe eugenol nanoparticle (ENP) could inhibit formation of biofilm as well as facilitate eradication of pre-formed biofilm of P. aeruginosa. C_LIO_LIBiofilm formation was found to be prevented significantly on ENP-coated catheter. C_LIO_LINano-eugenol may be used as a potential drug against bacterial diseases, caused by pseudomonal biofilm, which are difficult to treat by antibiotics. C_LIO_LINano-formulated eugenol may also be used as an effective anti-fouling agent for biomedical devices like contact lens, pace-maker, materials for organ transplantation etc. to prevent bacterial colonization. C_LI

microbiology↗

bioRxiv2022

Hypothalamic-pituitary-adrenal axis dysfunction worsens epilepsy outcomes and increases SUDEP risk

Epilepsy is often comorbid with psychiatric illnesses, including anxiety and depression. Despite the high incidence of psychiatric comorbidities in people with epilepsy, few studies address the underlying mechanisms. Stress can trigger both epilepsy and depression and significant accumulating evidence, from both human and animal studies, support that hypothalamic-pituitary-adrenal (HPA) axis dysfunction may contribute to the comorbidity of these two disorders1. Here we directly investigate the contribution of HPA axis dysfunction to epilepsy outcomes and psychiatric comorbidities. We generated a novel mouse model (KCC2/Crh mice) that lacks the K+/Cl- co-transporter, KCC2, in corticotropin-releasing hormone (CRH) neurons, which exhibit stress- and seizure-induced hyperactivation of the HPA axis.2,3 Here we demonstrate that HPA axis dysfunction in KCC2/Crh mice is associated with increased vulnerability to behavioral deficits in chronically epileptic mice and more severe neuropathological features associated with chronic epilepsy (e.g. increased mossy fiber sprouting). Despite equivalent seizure burden, HPA axis dysfunction in these chronically epileptic mice increased the incidence of sudden unexpected death in epilepsy (SUDEP). Suppressing HPA axis hyperexcitability in this model using a pharmacological or chemogenetic approach decreased SUDEP incidence, supporting HPA axis dysfunction in SUDEP vulnerability. We also observed changes in neuroendocrine markers in individuals that died of SUDEP compared to people with epilepsy or individuals without a history of epilepsy. Together, these findings describe a novel, nongenetic mouse model of SUDEP which will benefit preclinical SUDEP research to gain a better understanding of the underlying pathological mechanisms contributing to SUDEP. HPA axis dysfunction increases vulnerability to psychiatric comorbidities in epilepsy and may be a risk factor for SUDEP.

neuroscience↗

bioRxiv2021

Decisive Role of Polymer-BSA Interactions in Biofilm Substrates on Philicity and EPS Composition

Formation of extracellular polymeric substances (EPS) is a crucial step for bacterial biofilm growth. Dependence of EPS composition on the growth substrate and the conditioning of the latter is thus of primary importance. Here, we present results of studies on the growth of biofilms of two different strains each, of the Gram negative bacteria Escherichia coli and Klebsiella pneumoniae, on four polymers used commonly in indwelling medical devices - Polyethene, Polypropylene, Polycarbonate, and Polytetrafluoroethylene immersed in Bovine Serum Albumin (BSA) for 24 hrs. The polymer substrates are studied before and after immersing in BSA for 9 hrs and 24 hrs, using contact angle measurement (CAM) and Field Emission Scanning Electron Microscopy (FE-SEM) to extract, respectively, the philicity (defined as{phi} {equiv} sin ({theta}-90{degrees}), where{theta} is contact angle of the liquid on the solid at a particular temperature and ambient pressure) and spatial Hirsch parameter H (defined from the relation, F(r) ~ r2H, where F(r) is the mean squared density fluctuation at the sample surface). H =, <0.5 or >0.5 signifies no correlation, anti-correlation, and correlation, respectively. The substrates are seen to transform from large hydrophobicity to near amphiphilicity with the formation of BSA conditioning surface layer, and the H-values distinguish the length scales of ~ 100 nm, 500 nm, and 2000 nm, with the anti-correlation increasing with length scale. Biofilms grown on the BSA-covered surfaces are studied with CAM, FE-SEM, Fourier Transform Infrared (FTIR) and Surface Enhanced Raman Spectroscopy (SERS). Most notably, the{phi} -values are independent of the bacterial species and strain but dependent on the polymer, as is also shown strikingly by both types of spectra, while H-values show some bacterial variation. Thus, the EPS composition and consequently the wetting properties of the corresponding bacterial biofilms seems to be decided by the interaction of the conditioning BSA layer with a specific polymer used as the growth substrate.

biophysics↗

bioRxiv2019

A Systems Approach Identifies Enhancer of Zeste Homolog 2 (EZH2) as a Protective Factor in Epilepsy

Complex neurological conditions can give rise to large scale transcriptomic changes that drive disease progression. It is likely that alterations in one or a few transcription factors or cofactors underlie these transcriptomic alterations. Identifying the driving transcription factors/cofactors is a non-trivial problem and a limiting step in the understanding of neurological disorders. Epilepsy has a prevalence of 1% and is the fourth most common neurological disorder. While a number of anti-seizure drugs exist to treat seizures symptomatically, none is curative or preventive. This reflects a lack of understanding of disease progression. We used a novel systems approach to mine transcriptome profiles of rodent and human epileptic brain samples to identify regulators of transcriptional networks in the epileptic brain. We find that Enhancer of Zeste Homolog 2 (EZH2) regulates differentially expressed genes in epilepsy across multiple rodent models of acquired epilepsy. EZH2 undergoes a prolonged upregulation in the epileptic brain. A transient inhibition of EZH2 immediately after seizure induction robustly increases spontaneous seizure burden weeks later. Thus, EZH2 upregulation is a protective response mounted after a seizure. These findings are the first to characterize a role for EZH2 in opposing epileptogenesis and debut a bioinformatic approach to identify nuclear drivers of complex transcriptional changes in disease.\n\nAuthor SummaryEpilepsy is the fourth most common neurological disorder and has been described since the time of Hippocrates. Despite this, no treatments exist to stop epilepsy progression. This is fundamentally due to the complex nature of the disease. Epilepsy is associated with hundreds if not thousands of gene expression changes in the brain that are likely driven by a few key master regulators called transcription factors and cofactors. Finding the aberrantly acting factors is a complex problem that currently lacks a satisfactory solution. We used a novel datamining tool to define key master regulators of gene expression changes across multiple epilepsy models and patient samples. We find that a nuclear enzyme, EZH2, regulates a large number of genes in the rodent and patient epileptic brain and that its function is protective. Thus, inhibiting EZH2 greatly exacerbates seizure burden. This is the first report of a novel datamining tool to define drivers of large-scale gene changes and is also the first report of EZH2 induction as an endogenous protective response in the epilepsy.

neuroscience↗