Malignant gliomas are highly invasive brain cancers that carry a dismal prognosis. Recent studies indicate that Cl(-) channels facilitate glioma cell invasion by promoting hydrodynamic cell shape and volume changes. Here we asked how Cl(-) channels are regulated in the context of migration. Using patch-clamp recordings we show Cl(-) currents are activated by physiological increases of [Ca(2+)]i to 65 and 180nM. Cl(-) currents appear to be mediated by ClC-3, a voltage-gated, CaMKII-regulated Cl(-) channel highly expressed by glioma cells. ClC-3 channels colocalized with TRPC1 on caveolar lipid rafts on glioma cell processes. Using perforated-patch electrophysiological recordings, we demonstrate that inducible knockdown of TRPC1 expression with shRNA significantly inhibited glioma Cl(-) currents in a Ca(2+)-dependent fashion, placing Cl(-) channels under the regulation of Ca(2+) entry via TRPC1. In chemotaxis assays epidermal growth factor (EGF)-induced invasion was inhibition by TRPC1 knockdown to the same extent as pharmacological block of Cl(-) channels. Thus endogenous glioma Cl(-) channels are regulated by TRPC1. Cl(-) channels could be an important downstream target of TRPC1 in many other cells types, coupling elevations in [Ca(2+)]i to the shape and volume changes associated with migrating cells.
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http://dx.doi.org/10.1016/j.ceca.2012.11.013 | DOI Listing |
Am J Physiol Cell Physiol
January 2025
Laboratoire de Physiopathologie et Régulation des Transports Ioniques, Université de Poitiers, France.
Despite the importance of ocular surface in human physiology and diseases, little is known about ion channel expression, properties and regulation in ocular epithelial cells. Furthermore, human primary epithelial cells have rarely been studied in favor of rat, mouse and especially rabbit animal models. Here, we developed primary human Meibomian gland (hMGEC) and conjunctival (hConEC) epithelial cells.
View Article and Find Full Text PDFInorg Chem
January 2025
Department of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States.
Luminescent chiral metal-organic frameworks (CMOFs) are promising candidates for the enantioselective sensing of important chiral molecules. Herein, we report the synthesis and characterization of Zn and Cd CMOFs based on 1,1'-bi-2-naphthol (BINOL)-derived 3,3',6,6'-tetra(benzoic acids), H-OEt and H-OH. Four CMOFs, -OEt, -OH, -OEt, and -OH, based on these ligands were crystallographically characterized.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
Faculty of Psychology, Naval Medical University (Second Military Medical University), No. 800 Xiangyin Road, Yangpu District, Shanghai, 200433 China.
Fatigue-induced incidents in transportation, aerospace, military, and other areas have been on the rise, posing a threat to human life and safety. The determination of fatigue states holds significant importance, especially through reliable and conveniently available physiological indicators. Here, a portable custom-built fNIRS system was used to monitor the fatigue state caused by nap deprivation.
View Article and Find Full Text PDFFront Plant Sci
January 2025
School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China.
Introduction: With the advent of technologies such as deep learning in agriculture, a novel approach to classifying wheat seed varieties has emerged. However, some existing deep learning models encounter challenges, including long processing times, high computational demands, and low classification accuracy when analyzing wheat seed images, which can hinder their ability to meet real-time requirements.
Methods: To address these challenges, we propose a lightweight wheat seed classification model called LWheatNet.
Sci Rep
January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
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