Browsing by Author "Wang, Yu"

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    Pyruvate Dehydrogenase Kinase 4 Promotes Vascular Calcification via SMAD1/5/8 Phosphorylation
    (Nature Publishing Group, 2015-11-12) Lee, Sun Joo; Jeong, Ji Yun; Oh, Chang Joo; Park, Sungmi; Kim, Joon-Young; Kim, Han-Jong; Doo Kim, Nam; Choi, Young-Keun; Do, Ji-Yeon; Go, Younghoon; Ha, Chae-Myung; Choi, Je-Yong; Huh, Seung; Ho Jeoung, Nam; Lee, Ki-Up; Choi, Hueng-Sik; Wang, Yu; Park, Keun-Gyu; Harris, Robert A.; Lee, In-Kyu; Department of Biochemistry & Molecular Biology, IU School of Medicine
    Vascular calcification, a pathologic response to defective calcium and phosphate homeostasis, is strongly associated with cardiovascular mortality and morbidity. In this study, we have observed that pyruvate dehydrogenase kinase 4 (PDK4) is upregulated and pyruvate dehydrogenase complex phosphorylation is increased in calcifying vascular smooth muscle cells (VSMCs) and in calcified vessels of patients with atherosclerosis, suggesting that PDK4 plays an important role in vascular calcification. Both genetic and pharmacological inhibition of PDK4 ameliorated the calcification in phosphate-treated VSMCs and aortic rings and in vitamin D3-treated mice. PDK4 augmented the osteogenic differentiation of VSMCs by phosphorylating SMAD1/5/8 via direct interaction, which enhances BMP2 signaling. Furthermore, increased expression of PDK4 in phosphate-treated VSMCs induced mitochondrial dysfunction followed by apoptosis. Taken together, our results show that upregulation of PDK4 promotes vascular calcification by increasing osteogenic markers with no adverse effect on bone formation, demonstrating that PDK4 is a therapeutic target for vascular calcification.
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    Sample Size Determination in Multivariate Parameters With Applications to Nonuniform Subsampling in Big Data High Dimensional Linear Regression
    (2021-12) Wang, Yu; Peng, Hanxiang; Li, Fang; Sarkar, Jyoti; Tan, Fei
    Subsampling is an important method in the analysis of Big Data. Subsample size determination (SSSD) plays a crucial part in extracting information from data and in breaking the challenges resulted from huge data sizes. In this thesis, (1) Sample size determination (SSD) is investigated in multivariate parameters, and sample size formulas are obtained for multivariate normal distribution. (2) Sample size formulas are obtained based on concentration inequalities. (3) Improved bounds for McDiarmid’s inequalities are obtained. (4) The obtained results are applied to nonuniform subsampling in Big Data high dimensional linear regression. (5) Numerical studies are conducted. The sample size formula in univariate normal distribution is a melody in elementary statistics. It appears that its generalization to multivariate normal (or more generally multivariate parameters) hasn’t been caught much attention to the best of our knowledge. In this thesis, we introduce a definition for SSD, and obtain explicit formulas for multivariate normal distribution, in gratifying analogy of the sample size formula in univariate normal. Commonly used concentration inequalities provide exponential rates, and sample sizes based on these inequalities are often loose. Talagrand (1995) provided the missing factor to sharpen these inequalities. We obtained the numeric values of the constants in the missing factor and slightly improved his results. Furthermore, we provided the missing factor in McDiarmid’s inequality. These improved bounds are used to give shrunken sample sizes.
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    Three-dimensional nanoscopy of whole cells and tissues with in situ point spread function retrieval
    (Nature, 2020-05) Xu, Fan; Ma, Donghan; MacPherson, Kathryn P.; Liu, Sheng; Bu, Ye; Wang, Yu; Tang, Yu; Bi, Cheng; Kwok, Tim; Chubykin, Alexander A.; Yin, Peng; Calve, Sarah; Landreth, Gary E.; Huang, Fang; Anatomy and Cell Biology, School of Medicine
    Single-molecule localization microscopy is a powerful tool for visualizing subcellular structures, interactions and protein functions in biological research. However, inhomogeneous refractive indices inside cells and tissues distort the fluorescent signal emitted from single-molecule probes, which rapidly degrades resolution with increasing depth. We propose a method that enables the construction of an in situ 3D response of single emitters directly from single-molecule blinking datasets, and therefore allows their locations to be pinpointed with precision that achieves the Cramér-Rao lower bound and uncompromised fidelity. We demonstrate this method, named in situ PSF retrieval (INSPR), across a range of cellular and tissue architectures, from mitochondrial networks and nuclear pores in mammalian cells to amyloid-β plaques and dendrites in brain tissues and elastic fibers in developing cartilage of mice. This advancement expands the routine applicability of super-resolution microscopy from selected cellular targets near coverslips to intra- and extracellular targets deep inside tissues.
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    Tumor-Infiltrating Immune-Related Long Non-Coding RNAs Indicate Prognoses and Response to PD-1 Blockade in Head and Neck Squamous Cell Carcinoma
    (Frontiers Media, 2021-10-19) Ma, Ben; Jiang, Hongyi; Luo, Yi; Liao, Tian; Xu, Weibo; Wang, Xiao; Dong, Chuanpeng; Ji, Qinghai; Wang, Yu; BioHealth Informatics, School of Informatics and Computing
    Long non-coding RNAs (lncRNAs) in immune cells play critical roles in tumor cell-immune cell interactions. This study aimed to characterize the landscape of tumor-infiltrating immune-related lncRNAs (Ti-lncRNAs) and reveal their correlations with prognoses and immunotherapy response in head and neck squamous cell carcinoma (HNSCC). We developed a computational model to identify Ti-lncRNAs in HNSCC and analyzed their associations with clinicopathological features, molecular alterations, and immunotherapy response. A signature of nine Ti-lncRNAs demonstrated an independent prognostic factor for both overall survival and disease-free survival among the cohorts from Fudan University Shanghai Cancer Center, The Cancer Genome Atlas, GSE41613, and GSE42743. The Ti-lncRNA signature scores in immune cells showed significant associations with TP53 mutation, CDKN2A mutation, and hypoxia. Inferior signature scores were enriched in patients with high levels of PDCD1 and CTLA4 and high expanded immune gene signature (IGS) scores, who displayed good response to PD-1 blockade in HNSCC. Consistently, superior clinical response emerged in melanoma patients with low signature scores undergoing anti-PD-1 therapy. Moreover, the Ti-lncRNA signature was a prognostic factor independent of PDCD1, CTLA4, and the expanded IGS score. In conclusion, tumor-infiltrating immune profiling identified a prognostic Ti-lncRNA signature indicative of clinical response to PD-1 blockade in HNSCC.