Palmitoylation of proteins plays important roles in various physiological processes, such as cell proliferation, inflammation, cell differentiation etc. However, inhibition of protein palmitoylation has led to few new drugs to date. ZDHHC5 serves as a key enzyme to catalyze palmitoylation on SSTR5 (a proven anti-proliferation receptor in pancreatic cells). Herein, we compare single-cell transcriptome data between pancreatic cancer tissues and normal pancreas tissues and identify that ZDHHC5 is a potential target to inhibit proliferation of pancreatic cancer cells. In addition, we report the repositioning of an orphan drug (Lomitapide) as an inhibitor of ZDHHC5, and we speculate that this inhibitor may be able to block palmitylation on SSTR5. Pharmacological blockade of ZDHHC5 with Lomitapide results in attenuated cancer cell growth and proliferation which collectively contributes to antitumor responses in vitro and in vivo. This is the first study, to our knowledge, to demonstrate the utility of a pharmacological inhibitor of ZDHHC5 in pancreatic cancer, representing a new class of palmitoylation targeted therapy and laying a framework for paradigm-shifting therapies targeting cancer cell palmitoylation.
Some dynamical behaviors of fractional-order commutative quaternion-valued neural networks (FCQVNNs) are studied in this paper. First, because the commutative quaternion does not satisfy Schwartz triangle inequality, the FCQVNNs are divided into four real-valued neural networks (RVNNs) through quaternion commutative multiplication rules. Furthermore, several types of dynamical behaviors including global Mittag-Leffler stability, the boundedness with bounded disturbances, complete synchronization and quasi-synchronization of FCQVNNs are studied. Simultaneously, several conditions for these dynamical behaviors are driven by fractional-order Lyapunov direct method, some inequality techniques and fractional differential equation theory. At last, the effectiveness and feasibility of the obtained theoretical results are verified by several numerical simulation examples.
The global exponential stability of commutative quaternion-valued neural networks (C-QVNNs) with time varying delays is investigated in this paper. Considering that the Schwartz triangle inequality is not satisfied in the commutative quaternion field, the CQVNNs are isolated into real-valued neural networks by commutative multiplication rules among the three imaginary units. From the perspective of algebra and matrices, several sufficient conditions for ensuring the stability of CQVNNs are derived via the Lyapunov stability theory, the method of matrix measures and some inequality techniques. Finally, the feasibility and validity of the obtained outcomes are confirmed by an example.INDEX TERMS Commutative quaternion-valued neural networks (CQVNNs); Matrix measures; Linear matrix inequality (LMI); Global exponential stability
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