- Browse by Author
Browsing by Author "Zhang, Tuo"
Now showing 1 - 5 of 5
Results Per Page
Sort Options
Item Checkpoint kinase 2 controls insulin secretion and glucose homeostasis(Springer Nature, 2024) Chong, Angie Chi Nok; Vandana, J. Jeya; Jeng, Ginnie; Li, Ge; Meng, Zihe; Duan, Xiaohua; Zhang, Tuo; Qiu, Yunping; Duran-Struuck, Raimon; Coker, Kimberly; Wang, Wei; Li, Yanjing; Min, Zaw; Zuo, Xi; de Silva, Neranjan; Chen, Zhengming; Naji, Ali; Hao, Mingming; Liu, Chengyang; Chen, Shuibing; Urology, School of MedicineAfter the discovery of insulin, a century ago, extensive work has been done to unravel the molecular network regulating insulin secretion. Here we performed a chemical screen and identified AZD7762, a compound that potentiates glucose-stimulated insulin secretion (GSIS) of a human β cell line, healthy and type 2 diabetic (T2D) human islets and primary cynomolgus macaque islets. In vivo studies in diabetic mouse models and cynomolgus macaques demonstrated that AZD7762 enhances GSIS and improves glucose tolerance. Furthermore, genetic manipulation confirmed that ablation of CHEK2 in human β cells results in increased insulin secretion. Consistently, high-fat-diet-fed Chk2-/- mice show elevated insulin secretion and improved glucose clearance. Finally, untargeted metabolic profiling demonstrated the key role of the CHEK2-PP2A-PLK1-G6PD-PPP pathway in insulin secretion. This study successfully identifies a previously unknown insulin secretion regulating pathway that is conserved across rodents, cynomolgus macaques and human β cells in both healthy and T2D conditions.Item Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via A Novel Structured SCCA Approach(Springer, 2017-06) Du, Lei; Zhang, Tuo; Liu, Kefei; Yan, Jingwen; Yao, Xiaohui; Risacher, Shannon L.; Saykin, Andrew J.; Han, Junwei; Guo, Lei; Shen, Li; Alzheimer's Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineBrain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure. However, the group lasso methods have limitation in generalization because of the incomplete or unavailable prior knowledge in real world. The graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. We introduce a new SCCA model using a novel graph guided pairwise group lasso penalty, and propose an efficient optimization algorithm. The proposed method has a strong upper bound for the grouping effect for both positively and negatively correlated variables. We show that our method performs better than or equally to two state-of-the-art SCCA methods on both synthetic and real neuroimaging genetics data. In particular, our method identifies stronger canonical correlations and captures better canonical loading profiles, showing its promise for revealing biologically meaningful imaging genetic associations.Item Methylation of dual-specificity phosphatase 4 controls cell differentiation(Cell Press, 2021) Su, Hairui; Jiang, Ming; Senevirathne, Chamara; Aluri, Srinivas; Zhang, Tuo; Guo, Han; Xavier-Ferrucio, Juliana; Jin, Shuiling; Tran, Ngoc-Tung; Liu, Szu-Mam; Sun, Chiao-Wang; Zhu, Yongxia; Zhao, Qing; Chen, Yuling; Cable, LouAnn; Shen, Yudao; Liu, Jing; Qu, Cheng-Kui; Han, Xiaosi; Klug, Christopher A.; Bhatia, Ravi; Chen, Yabing; Nimer, Stephen D.; Zheng, Y. George; Iancu-Rubin, Camelia; Jin, Jian; Deng, Haiteng; Krause, Diane S.; Xiang, Jenny; Verma, Amit; Luo, Minkui; Zhao, Xinyang; Pediatrics, School of MedicineMitogen-activated protein kinases (MAPKs) are inactivated by dual-specificity phosphatases (DUSPs), the activities of which are tightly regulated during cell differentiation. Using knockdown screening and single-cell transcriptional analysis, we demonstrate that DUSP4 is the phosphatase that specifically inactivates p38 kinase to promote megakaryocyte (Mk) differentiation. Mechanistically, PRMT1-mediated methylation of DUSP4 triggers its ubiquitinylation by an E3 ligase HUWE1. Interestingly, the mechanistic axis of the DUSP4 degradation and p38 activation is also associated with a transcriptional signature of immune activation in Mk cells. In the context of thrombocytopenia observed in myelodysplastic syndrome (MDS), we demonstrate that high levels of p38 MAPK and PRMT1 are associated with low platelet counts and adverse prognosis, while pharmacological inhibition of p38 MAPK or PRMT1 stimulates megakaryopoiesis. These findings provide mechanistic insights into the role of the PRMT1-DUSP4-p38 axis on Mk differentiation and present a strategy for treatment of thrombocytopenia associated with MDS.Item A Novel SCCA Approach via Truncated ℓ1-norm and Truncated Group Lasso for Brain Imaging Genetics(Oxford University Press, 2017-09-18) Du, Lei; Liu, Kefei; Zhang, Tuo; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L; Han, Junwei; Guo, Lei; Saykin, Andrew J.; Shen, Li; Radiology and Imaging Sciences, School of MedicineMotivation: Brain imaging genetics, which studies the linkage between genetic variations and structural or functional measures of the human brain, has become increasingly important in recent years. Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is one major task in imaging genetics. Sparse Canonical Correlation Analysis (SCCA) has been a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ 1 -norm or its variants to induce sparsity. The ℓ 0 -norm penalty is a perfect sparsity-inducing tool which, however, is an NP-hard problem. Results: In this paper, we propose the truncated ℓ 1 -norm penalized SCCA to improve the performance and effectiveness of the ℓ 1 -norm based SCCA methods. Besides, we propose an efficient optimization algorithms to solve this novel SCCA problem. The proposed method is an adaptive shrinkage method via tuning τ . It can avoid the time intensive parameter tuning if given a reasonable small τ . Furthermore, we extend it to the truncated group-lasso (TGL), and propose TGL-SCCA model to improve the group-lasso-based SCCA methods. The experimental results, compared with four benchmark methods, show that our SCCA methods identify better or similar correlation coefficients, and better canonical loading profiles than the competing methods. This demonstrates the effectiveness and efficiency of our methods in discovering interesting imaging genetic associations. Availability: The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/tlpscca/ .Item Sparse Canonical Correlation Analysis via Truncated ℓ1-norm with Application to Brain Imaging Genetics(IEEE, 2016-12) Du, Lei; Zhang, Tuo; Liu, Kefei; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L.; Guo, Lei; Saykin, Andrew J.; Shen, Li; Medical and Molecular Genetics, School of MedicineDiscovering bi-multivariate associations between genetic markers and neuroimaging quantitative traits is a major task in brain imaging genetics. Sparse Canonical Correlation Analysis (SCCA) is a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ1-norm or its variants. The ℓ0-norm is more desirable, which however remains unexplored since the ℓ0-norm minimization is NP-hard. In this paper, we impose the truncated ℓ1-norm to improve the performance of the ℓ1-norm based SCCA methods. Besides, we propose two efficient optimization algorithms and prove their convergence. The experimental results, compared with two benchmark methods, show that our method identifies better and meaningful canonical loading patterns in both simulated and real imaging genetic analyse.