Recent findings show that single, non-neuronal cells are also able to learn signalling responses developing cellular memory. In cellular learning nodes of signalling networks strengthen their interactions e.g. by the conformational memory of intrinsically disordered proteins, protein translocation, miRNAs, lncRNAs, chromatin memory and signalling cascades. This can be described by a generalized, unicellular Hebbian learning process, where those signalling connections, which participate in learning, become stronger. Here we review those scenarios, where cellular signalling is not only repeated in a few times (when learning occurs), but becomes too frequent, too large, or too complex and overloads the cell. This leads to desensitisation of signalling networks by decoupling signalling components, receptor internalization, and consequent downregulation. These molecular processes are examples of anti-Hebbian learning and 'forgetting' of signalling networks. Stress can be perceived as signalling overload inducing the desensitisation of signalling pathways. Ageing occurs by the summative effects of cumulative stress downregulating signalling. We propose that cellular learning desensitisation, stress and ageing may be placed along the same axis of more and more intensive (prolonged or repeated) signalling. We discuss how cells might discriminate between repeated and unexpected signals, and highlight the Hebbian and anti-Hebbian mechanisms behind the fold-change detection in the NF-κB signalling pathway. We list drug design methods using Hebbian learning (such as chemically-induced proximity) and clinical treatment modalities inducing (cancer, drug allergies) desensitisation or avoiding drug-induced desensitisation. A better discrimination between cellular learning, desensitisation and stress may open novel directions in drug design, e.g. helping to overcome drug resistance.
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http://dx.doi.org/10.1007/s00018-024-05112-7 | DOI Listing |
Ann N Y Acad Sci
January 2025
Hainan Institute, Zhejiang University, Sanya, China.
In this paper, we introduce FUSION-ANN, a novel artificial neural network (ANN) designed for acoustic emission (AE) signal classification. FUSION-ANN comprises four distinct ANN branches, each housing an independent multilayer perceptron. We extract denoised features of speech recognition such as linear predictive coding, Mel-frequency cepstral coefficient, and gammatone cepstral coefficient to represent AE signals.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Department of Gastroenterology and Hepatology and Laboratory of Gastrointestinal Cancer and Liver Disease, West China Hospital, Sichuan University, Chengdu, 610041, China.
Skeletal muscle atrophy (sarcopenia) is a serious complication of liver cirrhosis, and chronic muscle inflammation plays a pivotal role in its pathologenesis. However, the detailed mechanism through which injured liver tissues mediate skeletal muscle inflammatory injury remains elusive. Here, it is reported that injured hepatocytes might secrete mtDNA-enriched extracellular vesicles (EVs) to trigger skeletal muscle inflammation by activating the cGAS-STING pathway.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Electrical and Computer Engineering Department, Concordia University, Montreal, Canada.
Astrocytes critically shape whole-brain structure and function by forming extensive gap junctional networks that intimately and actively interact with neurons. Despite their importance, existing computational models of whole-brain activity ignore the roles of astrocytes while primarily focusing on neurons. Addressing this oversight, we introduce a biophysical neural mass network model, designed to capture the dynamic interplay between astrocytes and neurons via glutamatergic and GABAergic transmission pathways.
View Article and Find Full Text PDFPLoS One
January 2025
School of Resources and Environment, Inner Mongolia University of Technology, Hohhot, China.
The aim of this study is to address the limitations of convolutional networks in recognizing modulation patterns. These networks are unable to utilize temporal information effectively for feature extraction and modulation pattern recognition, resulting in inefficient modulation pattern recognition. To address this issue, a signal modulation recognition method based on a two-way interactive temporal attention network algorithm has been developed.
View Article and Find Full Text PDFJCI Insight
January 2025
Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, United States of America.
Obscurin is a giant protein that coordinates diverse aspects of striated muscle physiology. Obscurin immunoglobulin domains 58/59 (Ig58/59) associate with essential sarcomeric and Ca2+ cycling proteins. To explore the pathophysiological significance of Ig58/59, we generated the Obscn-ΔIg58/59 mouse model, expressing obscurin constitutively lacking Ig58/59.
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