Protein tyrosine kinases (RTKs) modulate a wide range of pathophysiological events in several non-malignant disorders, including diabetic complications. To find new targets driving the development of diabetic cardiomy...Protein tyrosine kinases (RTKs) modulate a wide range of pathophysiological events in several non-malignant disorders, including diabetic complications. To find new targets driving the development of diabetic cardiomyopathy (DCM), we profiled an RTKs phosphorylation array in diabetic mouse hearts and identified increased phosphorylated fibroblast growth factor receptor 1 (p-FGFR1) levels in cardiomyocytes, indicating that FGFR1 may contribute to the pathogenesis of DCM. Using primary cardiomyocytes and H9C2 cell lines, we discovered that high-concentration glucose (HG) transactivates FGFR1 kinase domain through toll-like receptor 4 (TLR4) and c-Src, independent of FGF ligands. Knocking down the levels of either TLR4 or c-Src prevents HG-activated FGFR1 in cardiomyocytes. RNA-sequencing analysis indicates that the elevated FGFR1 activity induces pro-inflammatory responses via MAPKs–NFκB signaling pathway in HG-challenged cardiomyocytes, which further results in fibrosis and hypertrophy. We then generated cardiomyocyte-specific FGFR1 knockout mice and showed that a lack of FGFR1 in cardiomyocytes prevents diabetes-induced cardiac inflammation and preserves cardiac function in mice. Pharmacological inhibition of FGFR1 by a selective inhibitor, AZD4547, also prevents cardiac inflammation, fibrosis, and dysfunction in both type 1 and type 2 diabetic mice. These studies have identified FGFR1 as a new player in driving DCM and support further testing of FGFR1 inhibitors for possible cardioprotective benefits.展开更多
The NLRP3 inflammasome’s core and most specific protein,NLRP3,has a variety of functions in inflammation-driven diseases.Costunolide(COS)is the major active ingredient of the traditional Chinese medicinal herb Saussu...The NLRP3 inflammasome’s core and most specific protein,NLRP3,has a variety of functions in inflammation-driven diseases.Costunolide(COS)is the major active ingredient of the traditional Chinese medicinal herb Saussurea lappa and has anti-inflammatory activity,but the principal mechanism and molecular target of COS remain unclear.Here,we show that COS covalently binds to cysteine 598 in NACHT domain of NLRP3,altering the ATPase activity and assembly of NLRP3 inflammasome.We declare COS’s great anti-inflammasome efficacy in macrophages and disease models of gouty arthritis and ulcerative colitis via inhibiting NLRP3 inflammasome activation.We also reveal that theα-methylene-γ-butyrolactone motif in sesquiterpene lactone is the certain active group in inhibiting NLRP3 activation.Taken together,NLRP3 is identified as a direct target of COS for its anti-inflammasome activity.COS,especially theα-methylene-γ-butyrolactone motif in COS structure,might be used to design and produce novel NLRP3 inhibitors as a lead compound.展开更多
Training a machine learning model with federated edge learning(FEEL)is typically time consuming due to the constrained computation power of edge devices and the limited wireless resources in edge networks.In this stud...Training a machine learning model with federated edge learning(FEEL)is typically time consuming due to the constrained computation power of edge devices and the limited wireless resources in edge networks.In this study,the training time minimization problem is investigated in a quantized FEEL system,where heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels.In particular,a stochastic quantization scheme is adopted for compression of uploaded gradients,which can reduce the burden of per-round communication but may come at the cost of increasing the number of communication rounds.The training time is modeled by taking into account the communication time,computation time,and the number of communication rounds.Based on the proposed training time model,the intrinsic trade-off between the number of communication rounds and per-round latency is characterized.Specifically,we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap.Furthermore,a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap,based on which the closed-form expressions for the number of communication rounds and the total training time are obtained.Constrained by the total bandwidth,the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization problem.To this end,an algorithm based on alternating optimization is proposed,which alternatively solves the subproblem of quantization optimization through successive convex approximation and the subproblem of bandwidth allocation by bisection search.With different learning tasks and models,the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the simulation results.展开更多
基金This study was supported by the National Key Research Project(2017YFA0506000 to Guang Liang,China)National Natural Science Foundation of China(81930108 to Guang Liang and 82000793 to Wu Luo,and 82270364 to Xiong Chen).
文摘Protein tyrosine kinases (RTKs) modulate a wide range of pathophysiological events in several non-malignant disorders, including diabetic complications. To find new targets driving the development of diabetic cardiomyopathy (DCM), we profiled an RTKs phosphorylation array in diabetic mouse hearts and identified increased phosphorylated fibroblast growth factor receptor 1 (p-FGFR1) levels in cardiomyocytes, indicating that FGFR1 may contribute to the pathogenesis of DCM. Using primary cardiomyocytes and H9C2 cell lines, we discovered that high-concentration glucose (HG) transactivates FGFR1 kinase domain through toll-like receptor 4 (TLR4) and c-Src, independent of FGF ligands. Knocking down the levels of either TLR4 or c-Src prevents HG-activated FGFR1 in cardiomyocytes. RNA-sequencing analysis indicates that the elevated FGFR1 activity induces pro-inflammatory responses via MAPKs–NFκB signaling pathway in HG-challenged cardiomyocytes, which further results in fibrosis and hypertrophy. We then generated cardiomyocyte-specific FGFR1 knockout mice and showed that a lack of FGFR1 in cardiomyocytes prevents diabetes-induced cardiac inflammation and preserves cardiac function in mice. Pharmacological inhibition of FGFR1 by a selective inhibitor, AZD4547, also prevents cardiac inflammation, fibrosis, and dysfunction in both type 1 and type 2 diabetic mice. These studies have identified FGFR1 as a new player in driving DCM and support further testing of FGFR1 inhibitors for possible cardioprotective benefits.
基金supported by the National Natural Science Foundation of China(81930108 to Guang Liang,82000793 to Wu Luo,and 82170373 to Yi Wang)Natural Science Foundation of Zhejiang Province(LY22H070004 to Wu Luo,China)+1 种基金Zhejiang Provincial Key Scientific Project(2021C03041 to Guang Liang,China)Wenzhou Scientific Project in China(Y20210213 to Wu Luo)。
文摘The NLRP3 inflammasome’s core and most specific protein,NLRP3,has a variety of functions in inflammation-driven diseases.Costunolide(COS)is the major active ingredient of the traditional Chinese medicinal herb Saussurea lappa and has anti-inflammatory activity,but the principal mechanism and molecular target of COS remain unclear.Here,we show that COS covalently binds to cysteine 598 in NACHT domain of NLRP3,altering the ATPase activity and assembly of NLRP3 inflammasome.We declare COS’s great anti-inflammasome efficacy in macrophages and disease models of gouty arthritis and ulcerative colitis via inhibiting NLRP3 inflammasome activation.We also reveal that theα-methylene-γ-butyrolactone motif in sesquiterpene lactone is the certain active group in inhibiting NLRP3 activation.Taken together,NLRP3 is identified as a direct target of COS for its anti-inflammasome activity.COS,especially theα-methylene-γ-butyrolactone motif in COS structure,might be used to design and produce novel NLRP3 inhibitors as a lead compound.
基金supported by the National Key R&D Program of China(No.2020YFB1807100)the National Natural Science Foundation of China(No.62001310)the Guangdong Basic and Applied Basic Research Foundation,China(No.2022A1515010109)。
文摘Training a machine learning model with federated edge learning(FEEL)is typically time consuming due to the constrained computation power of edge devices and the limited wireless resources in edge networks.In this study,the training time minimization problem is investigated in a quantized FEEL system,where heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels.In particular,a stochastic quantization scheme is adopted for compression of uploaded gradients,which can reduce the burden of per-round communication but may come at the cost of increasing the number of communication rounds.The training time is modeled by taking into account the communication time,computation time,and the number of communication rounds.Based on the proposed training time model,the intrinsic trade-off between the number of communication rounds and per-round latency is characterized.Specifically,we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap.Furthermore,a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap,based on which the closed-form expressions for the number of communication rounds and the total training time are obtained.Constrained by the total bandwidth,the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization problem.To this end,an algorithm based on alternating optimization is proposed,which alternatively solves the subproblem of quantization optimization through successive convex approximation and the subproblem of bandwidth allocation by bisection search.With different learning tasks and models,the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the simulation results.
