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Biology subjects

Gad, A. K.

Publications and source records attributed to Gad, A. K..

2 recordsLinked to original sources

Mechanical control of the splicing factor PTBP1 regulates extracellular matrix stiffness-induced cell proliferation and mechanomemory

Cells sense and respond to mechanical cues from their environment. Mechanical cues are important for many biological processes, including embryonic development, ageing, cellular homeostasis, and diseases. Cells translate mechanical cues into cellular biochemical signals that govern cellular behaviour, like cell proliferation or migration, via a process called mechanotransduction. However, this process and the proteins involved remain incompletely understood. Here, we present an unbiased and large-scale approach to identify proteins involved in mechanotransduction. The screen revealed that the splicing factor PTBP1 is a novel mechanotransducer. We show that the nuclear localisation of PTBP1 depends on extracellular matrix stiffness, cell density, and the actomyosin-based contractility of the cell. Furthermore, we demonstrate that PTBP1 promotes the mechanosensitive splicing of the adapter protein Numb and that alternative splicing of Numb is crucial for matrix stiffness-induced cell proliferation and mechanomemory. Our results support the idea that changes in alternative splicing are an integral part of mechanotransduction and provide a mechanism by which matrix stiffness regulates cell proliferation and the formation of a mechanomemory in cells.

cell biology↗

A novel universal algorithm for filament network tracing and cytoskeleton analysis

The rapid development of advanced microscopy techniques over recent decades has significantly increased the quality of imaging and our understanding of subcellular structures, such as the organization of the filaments of the cytoskeleton using fluorescence and electron microscopy. However, these recent improvements in imaging techniques have not been matched by similar development of techniques for computational analysis of the images of filament networks that can now be obtained. Hence, for a wide range of applications, reliable computational analysis of such two-dimensional (2D) methods remains challenging. Here, we present a new algorithm for tracing of filament networks. This software can extract many important parameters from grayscale images of filament networks, including the Mesh Hole Size, and Filament Length and Connectivity (also known as Coordination Number. In addition, the method allows sub-networks to be distinguished in 2D images using intensity thresholding. We show that the algorithm can be used to analyze images of cytoskeleton networks obtained using different advanced microscopy methods. We have thus developed a new improved method for computational analysis of 2D images of filamentous networks that has wide applications for existing imaging techniques. The algorithm is available as open-source software.

biophysics↗