Pituitary adenylate cyclase activating polypeptide (PACAP) is a neurohormone exerting protective function during various stress conditions either in mature or developing tissues. Previously we proved the presence of PACAP signaling elements in chicken limb bud-derived chondrogenic cells in micromass cell cultures. Since no data can be found if PACAP signaling is playing any role during mechanical stress in any tissues, we aimed to investigate its contribution in mechanotransduction during chondrogenesis. Expressions of the mRNAs of PACAP and its major receptor, PAC1 increased, while that of other receptors, VPAC1, VPAC2 decreased upon mechanical stimulus. Mechanical load enhanced the expression of collagen type X, a marker of hypertrophic differentiation of chondrocytes and PACAP addition attenuated this elevation. Moreover, exogenous PACAP also prevented the mechanical load evoked activation of hedgehog signaling: protein levels of Sonic and Indian Hedgehogs and Gli1 transcription factor were lowered while expressions of Gli2 and Gli3 were elevated by PACAP application during mechanical load. Our results suggest that mechanical load activates PACAP signaling and exogenous PACAP acts against the hypertrophy inducing effect of mechanical load.
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http://dx.doi.org/10.3390/ijms160817344 | DOI Listing |
Immunol Rev
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Laboratory of Immunobiology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
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The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia 30332, United States.
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Yıldız Technical University, Department of Bioengineering, Davutpasa Campus, 34210 Esenler, İstanbul, Turkey.
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View Article and Find Full Text PDFNeural Netw
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Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland.
Real-time online optimisation plays a crucial role in high-frequency trading (HFT) strategies. The Markowitz model, as a Nobel Prize-winning framework, is widely used for portfolio management optimisation by framing the problem as a constrained quadratic programming task. While conventional analytical methods are typically effective for solving quadratic programming problems with linear constraints, the introduction of both linear equality and inequality constraints in the Markowitz model necessitates the use of numerical methods.
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