👤 Chun Wang

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Also published as: A Wang, Ai-Ling Wang, Ai-Ting Wang, Aihua Wang, Aijun Wang, Aili Wang, Aimin Wang, Aiting Wang, Aixian Wang, Aiyun Wang, Aizhong Wang, Alexander Wang, Alice Wang, Allen Wang, Anlai Wang, Anli Wang, Annette Wang, Anni Wang, Anqi Wang, Anthony Z Wang, Anxiang Wang, Anxin Wang, Ao Wang, Aoli Wang, B R Wang, B Wang, Baihan Wang, Baisong Wang, Baitao Wang, Bangchen Wang, Banghui Wang, Bangmao Wang, Bangshing Wang, Bao Wang, Bao-Long Wang, Baocheng Wang, Baofeng Wang, Baogui Wang, Baojun Wang, Baoli Wang, Baolong Wang, Baoming Wang, Baosen Wang, Baowei Wang, Baoying Wang, Baoyun Wang, Bei Bei Wang, Bei Wang, Beibei Wang, Beilan Wang, Beilei Wang, Ben Wang, Benjamin H Wang, Benzhong Wang, Bi Wang, Bi-Dar Wang, Biao Wang, Bicheng Wang, Bijue Wang, Bin Wang, Bin-Xue Wang, Binbin Wang, Bing Qing Wang, Bing Wang, Binghai Wang, Binghan Wang, Bingjie Wang, Binglong Wang, Bingnan Wang, Bingyan Wang, Bingyu Wang, Binquan Wang, Biqi Wang, Bo Wang, Bochu Wang, Boyu Wang, Bruce Wang, C Wang, C Z Wang, Cai Ren Wang, Cai-Hong Wang, Cai-Yun Wang, Cailian Wang, Caiqin Wang, Caixia Wang, Caiyan Wang, Can Wang, Cangyu Wang, Carol A Wang, Catherine Ruiyi Wang, Cenxuan Wang, Chan Wang, Chang Wang, Chang-Yun Wang, Changduo Wang, Changjing Wang, Changliang Wang, Changlong Wang, Changqian Wang, Changtu Wang, Changwei Wang, Changying Wang, Changyu Wang, Changyuan Wang, Changzhen Wang, Chao Wang, Chao-Jun Wang, Chao-Yung Wang, Chaodong Wang, Chaofan Wang, Chaohan Wang, Chaohui Wang, Chaojie Wang, Chaokui Wang, Chaomeng Wang, Chaoqun Wang, Chaoxian Wang, Chaoyi Wang, Chaoyu Wang, Chaozhan Wang, Charles C N Wang, Chau-Jong Wang, Chen Wang, Chen-Cen Wang, Chen-Ma Wang, Chen-Yu Wang, Chenchen Wang, Chenfei Wang, Cheng An Wang, Cheng Wang, Cheng-Cheng Wang, Cheng-Jie Wang, Cheng-zhang Wang, Chengbin Wang, Chengcheng Wang, Chenggang Wang, Chenghao Wang, Chenghua Wang, Chengjian Wang, Chengjun Wang, Chenglin Wang, Chenglong Wang, Chengniu Wang, Chengqiang Wang, Chengshuo Wang, Chenguang Wang, Chengwen Wang, Chengyan Wang, Chengyu Wang, Chengze Wang, Chenji Wang, Chenliang Wang, Chenwei Wang, Chenxi Wang, Chenxin Wang, Chenxuan Wang, Chenyang Wang, Chenyao Wang, Chenyin Wang, Chenyu Wang, Chenzi Wang, Chi Chiu Wang, Chi Wang, Chi-Ping Wang, Chia-Chuan Wang, Chia-Lin Wang, Chien-Hsun Wang, Chien-Wei Wang, Chih-Chun Wang, Chih-Hao Wang, Chih-Hsien Wang, Chih-Liang Wang, Chih-Yang Wang, Chih-Yuan Wang, Chijia Wang, Ching C Wang, Ching-Jen Wang, Chiou-Miin Wang, Chong Wang, Chongjian Wang, Chonglong Wang, Chongmin Wang, Chongze Wang, Christina Wang, Christine Wang, Chu Wang, Chuan Wang, Chuan-Chao Wang, Chuan-Hui Wang, Chuan-Jiang Wang, Chuan-Wen Wang, Chuang Wang, Chuanhai Wang, Chuansen Wang, Chuansheng Wang, Chuanxin Wang, Chuanyue Wang, Chuduan Wang, Chun-Chieh Wang, Chun-Juan Wang, Chun-Li Wang, Chun-Lin Wang, Chun-Ting Wang, Chun-Xia Wang, Chung-Hsi Wang, Chung-Hsing Wang, Chung-Teng Wang, Chunguo Wang, Chunhong Wang, Chuning Wang, Chunjiong Wang, Chunjuan Wang, Chunle Wang, Chunli Wang, Chunlong Wang, Chunmei Wang, Chunsheng Wang, Chunting Wang, Chunxia Wang, Chunxue Wang, Chunyan Wang, Chunyang Wang, Chunyi Wang, Chunyu Wang, Chuyao Wang, Cindy Wang, Ciyang Wang, Cong Wang, Congcong Wang, Congrong Wang, Congrui Wang, Cui Wang, Cui-Fang Wang, Cui-Shan Wang, Cuili Wang, Cuiling Wang, Cuizhe Wang, Cun-Yu Wang, Cunchuan Wang, Cunyi Wang, D Wang, Da Wang, Da-Cheng Wang, Da-Li Wang, Da-Yan Wang, Da-Zhi Wang, Dadong Wang, Dai Wang, Daijun Wang, Daiwei Wang, Daixi Wang, Dajia Wang, Dake Wang, Dali Wang, Dalong Wang, Dalu Wang, Dan Wang, Dan-Dan Wang, Danan Wang, Dandan Wang, Danfeng Wang, Dang Wang, Dangfeng Wang, Danling Wang, Danqing Wang, Danxin Wang, Danyang Wang, Dao Wen Wang, Dao-Wen Wang, Dao-Xin Wang, Daolong Wang, Daoping Wang, Daozhong Wang, Dapeng Wang, Daping Wang, Daqi Wang, Daqing Wang, David Q H Wang, David Q-H Wang, David Wang, Dawei Wang, Dayan Wang, Dayong Wang, Dazhi Wang, De-He Wang, Dedong Wang, Dehao Wang, Deli Wang, Delin Wang, Delong Wang, Demin Wang, Deming Wang, Dengbin Wang, Dennis Qing Wang, Dennis Wang, Deqi Wang, Deshou Wang, Dezhong Wang, Di Wang, Dinghui Wang, Dingting Wang, Dingxiang Wang, Dong D Wang, Dong Hao Wang, Dong Wang, Dong-Dong Wang, Dong-Jie Wang, Dong-Mei Wang, DongWei Wang, Dongdong Wang, Donggen Wang, Donghao Wang, Donghong Wang, Donghui Wang, Dongliang Wang, Donglin Wang, Dongmei Wang, Dongqin Wang, Dongshi Wang, Dongxia Wang, Dongxu Wang, Dongyan Wang, Dongyang Wang, Dongyi Wang, Dongying Wang, Dongyu Wang, Doudou Wang, Du Wang, Duan Wang, Duanyang Wang, Duo-Ping Wang, E Wang, Edward Wang, En-bo Wang, En-hua Wang, Endi Wang, Enhua Wang, Er-Jin Wang, Erfei Wang, Erika Y Wang, Ermao Wang, Erming Wang, Ertao Wang, Eryao Wang, Eunice S Wang, Exing Wang, F Wang, Fa-Kai Wang, Fan Wang, Fanchang Wang, Fang Wang, Fang-Tao Wang, Fangfang Wang, Fangjie Wang, Fangjun Wang, Fangyan Wang, Fangyong Wang, Fangyu Wang, Fanhua Wang, Fanwen Wang, Fanxiong Wang, Fei Wang, Fei-Fei Wang, Fei-Yan Wang, Feida Wang, Feifei Wang, Feijie Wang, Feimiao Wang, Feixiang Wang, Feiyan Wang, Fen Wang, Feng Wang, Feng-Sheng Wang, Fengchong Wang, Fengge Wang, Fenghua Wang, Fengliang Wang, Fenglin Wang, Fengling Wang, Fengqiang Wang, Fengyang Wang, Fengying Wang, Fengyong Wang, Fengyun Wang, Fengzhen Wang, Fengzhong Wang, Fu Wang, Fu-Sheng Wang, Fu-Yan Wang, Fu-Zhen Wang, Fubao Wang, Fubing Wang, Fudi Wang, Fuhua Wang, Fuqiang Wang, Furong Wang, Fuwen Wang, Fuxin Wang, Fuyan Wang, G Q Wang, G Wang, G-W Wang, Gan Wang, Gang Wang, Ganggang Wang, Ganglin Wang, Gangyang Wang, Ganyu Wang, Gao T Wang, Gao Wang, Gaofu Wang, Gaopin Wang, Gavin Wang, Ge Wang, Geng Wang, Genghao Wang, Gengsheng Wang, Gongming Wang, Guan Wang, Guan-song Wang, Guandi Wang, Guanduo Wang, Guang Wang, Guang-Jie Wang, Guang-Rui Wang, Guangdi Wang, Guanghua Wang, Guanghui Wang, Guangliang Wang, Guangming Wang, Guangsuo Wang, Guangwen Wang, Guangyan Wang, Guangzhi Wang, Guanrou Wang, Guanru Wang, Guansong Wang, Guanyun Wang, Gui-Qi Wang, Guibin Wang, Guihu Wang, Guihua Wang, Guimin Wang, Guiping Wang, Guiqun Wang, Guixin Wang, Guixue Wang, Guiying Wang, Guo-Du Wang, Guo-Hua Wang, Guo-Liang Wang, Guo-Ping Wang, Guo-Quan Wang, Guo-hong Wang, GuoYou Wang, Guobin Wang, Guobing Wang, Guodong Wang, Guohang Wang, Guohao Wang, Guoliang Wang, Guoling Wang, Guoping Wang, Guoqian Wang, Guoqiang Wang, Guoqing Wang, Guorong Wang, Guowen Wang, Guoxiang Wang, Guoxiu Wang, Guoyi Wang, Guoying Wang, Guozheng Wang, H J Wang, H Wang, H X Wang, H Y Wang, H-Y Wang, Hai Bo Wang, Hai Wang, Hai Yang Wang, Hai-Feng Wang, Hai-Jun Wang, Hai-Long Wang, Haibin Wang, Haibing Wang, Haibo Wang, Haichao Wang, Haichuan Wang, Haifei Wang, Haifeng Wang, Haihe Wang, Haihong Wang, Haihua Wang, Haijiao Wang, Haijing Wang, Haijiu Wang, Haikun Wang, Hailei Wang, Hailin Wang, Hailing Wang, Hailong Wang, Haimeng Wang, Haina Wang, Haining Wang, Haiping Wang, Hairong Wang, Haitao Wang, Haiwei Wang, Haixia Wang, Haixin Wang, Haixing Wang, Haiyan Wang, Haiying Wang, Haiyong Wang, Haiyun Wang, Haizhen Wang, Han Wang, Hanbin Wang, Hanbing Wang, Hanchao Wang, Handong Wang, Hang Wang, Hangzhou Wang, Hanmin Wang, Hanping Wang, Hanqi Wang, Hanying Wang, Hanyu Wang, Hanzhi Wang, Hao Wang, Hao-Ching Wang, Hao-Hua Wang, Hao-Tian Wang, Hao-Yu Wang, Haobin Wang, Haochen Wang, Haohao Wang, Haohui Wang, Haojie Wang, Haolong Wang, Haomin Wang, Haoming Wang, Haonan Wang, Haoping Wang, Haoqi Wang, Haoran Wang, Haowei Wang, Haoxin Wang, Haoyang Wang, Haoyu Wang, Haozhou Wang, He Wang, He-Cheng Wang, He-Ling Wang, He-Ping Wang, He-Tong Wang, Hebo Wang, Hechuan Wang, Heling Wang, Hemei Wang, Heming Wang, Heng Wang, Heng-Cai Wang, Hengjiao Wang, Hengjun Wang, Hequn Wang, Hesuiyuan Wang, Heyong Wang, Hezhi Wang, Hong Wang, Hong Yi Wang, Hong-Gang Wang, Hong-Hui Wang, Hong-Kai Wang, Hong-Qin Wang, Hong-Wei Wang, Hong-Xia Wang, Hong-Yan Wang, Hong-Yang Wang, Hong-Ying Wang, Hongbin Wang, Hongbing Wang, Hongbo Wang, Hongcai Wang, Hongda Wang, Hongdan Wang, Hongfang Wang, Hongjia Wang, Hongjian Wang, Hongjie Wang, Hongjuan Wang, Hongkun Wang, Honglei Wang, Hongli Wang, Honglian Wang, Honglun Wang, Hongmei Wang, Hongpin Wang, Hongqian Wang, Hongshan Wang, Hongsheng Wang, Hongtao Wang, Hongwei Wang, Hongxia Wang, Hongxin Wang, Hongyan Wang, Hongyang Wang, Hongyi Wang, Hongyin Wang, Hongying Wang, Hongyu Wang, Hongyuan Wang, Hongyue Wang, Hongyun Wang, Hongze Wang, Hongzhan Wang, Hongzhuang Wang, Horng-Dar Wang, Houchun Wang, Hsei-Wei Wang, Hsueh-Chun Wang, Hu WANG, Hua Wang, Hua-Qin Wang, Hua-Wei Wang, Huabo Wang, Huafei Wang, Huai-Zhou Wang, Huaibing Wang, Huaili Wang, Huaizhi Wang, Huajin Wang, Huajing Wang, Hualin Wang, Hualing Wang, Huan Wang, Huan-You Wang, Huang Wang, Huanhuan Wang, Huanyu Wang, Huaquan Wang, Huating Wang, Huawei Wang, Huaxiang Wang, Huayang Wang, Huei Wang, Hui Miao Wang, Hui Wang, Hui-Hui Wang, Hui-Li Wang, Hui-Nan Wang, Hui-Yu Wang, HuiYue Wang, Huie Wang, Huiguo Wang, Huihua Wang, Huihui Wang, Huijie Wang, Huijun Wang, Huilun Wang, Huimei Wang, Huimin Wang, Huina Wang, Huiping Wang, Huiquan Wang, Huiqun Wang, Huishan Wang, Huiting Wang, Huiwen Wang, Huixia Wang, Huiyan Wang, Huiyang Wang, Huiyao Wang, Huiying Wang, Huiyu Wang, Huizhen Wang, Huizhi Wang, Huming Wang, I-Ching Wang, Iris X Wang, Isabel Z Wang, J J Wang, J P Wang, J Q Wang, J Wang, J Z Wang, J-Y Wang, Jacob E Wang, James Wang, Jeffrey Wang, Jen-Chun Wang, Jen-Chywan Wang, Jennifer E Wang, Jennifer T Wang, Jennifer X Wang, Jenny Y Wang, Jeremy R Wang, Jeremy Wang, Ji M Wang, Ji Wang, Ji-Nuo Wang, Ji-Yang Wang, Ji-Yao Wang, Ji-zheng Wang, Jia Bei Wang, Jia Bin Wang, Jia Wang, Jia-Liang Wang, Jia-Lin Wang, Jia-Mei Wang, Jia-Peng Wang, Jia-Qi Wang, Jia-Qiang Wang, Jia-Ying Wang, Jia-Yu Wang, Jiabei Wang, Jiabo Wang, Jiafeng Wang, Jiafu Wang, Jiahao Wang, Jiahui Wang, Jiajia Wang, Jiakun Wang, Jiale Wang, Jiali Wang, Jialiang Wang, Jialin Wang, Jialing Wang, Jiamin Wang, Jiaming Wang, Jian Wang, Jian'an Wang, Jian-Bin Wang, Jian-Guo Wang, Jian-Hong Wang, Jian-Long Wang, Jian-Wei Wang, Jian-Xiong Wang, Jian-Yong Wang, Jian-Zhi Wang, Jian-chun Wang, Jianan Wang, Jianbing Wang, Jianbo Wang, Jianding Wang, Jianfang Wang, Jianfei Wang, Jiang Wang, Jiangbin Wang, Jiangbo Wang, Jianghua Wang, Jianghui Wang, Jiangong Wang, Jianguo Wang, Jianhao Wang, Jianhua Wang, Jianhui Wang, Jiani Wang, Jianjiao Wang, Jianjie Wang, Jianjun Wang, Jianle Wang, Jianli Wang, Jianlin Wang, Jianliu Wang, Jianlong Wang, Jianmei Wang, Jianmin Wang, Jianning Wang, Jianping Wang, Jianqin Wang, Jianqing Wang, Jianqun Wang, Jianru Wang, Jianshe Wang, Jianshu Wang, Jiantao Wang, Jianwei Wang, Jianwu Wang, Jianxiang Wang, Jianxin Wang, Jianye Wang, Jianying Wang, Jianyong Wang, Jianyu Wang, Jianzhang Wang, Jianzhi Wang, Jiao Wang, Jiaojiao Wang, Jiapan Wang, Jiaping Wang, Jiaqi Wang, Jiaqian Wang, Jiatao Wang, Jiawei Wang, Jiawen Wang, Jiaxi Wang, Jiaxin Wang, Jiaxing Wang, Jiaxuan Wang, Jiayan Wang, Jiayang Wang, Jiayi Wang, Jiaying Wang, Jiayu Wang, Jiazheng Wang, Jiazhi Wang, Jie Jin Wang, Jie Wang, Jieda Wang, Jieh-Neng Wang, Jiemei Wang, Jieqi Wang, Jieyan Wang, Jieyu Wang, Jifei Wang, Jiheng Wang, Jihong Wang, Jiliang Wang, Jilin Wang, Jin Wang, Jin'e Wang, Jin-Bao Wang, Jin-Cheng Wang, Jin-Da Wang, Jin-E Wang, Jin-Juan Wang, Jin-Liang Wang, Jin-Xia Wang, Jin-Xing Wang, Jincheng Wang, Jindan Wang, Jinfei Wang, Jinfeng Wang, Jinfu Wang, Jing J Wang, Jing Wang, Jing-Hao Wang, Jing-Huan Wang, Jing-Jing Wang, Jing-Long Wang, Jing-Min Wang, Jing-Shi Wang, Jing-Wen Wang, Jing-Xian Wang, Jing-Yi Wang, Jing-Zhai Wang, Jingang Wang, Jingchun Wang, Jingfan Wang, Jingfeng Wang, Jingheng Wang, Jinghong Wang, Jinghua Wang, Jinghuan Wang, Jingjing Wang, Jingkang Wang, Jinglin Wang, Jingmin Wang, Jingnan Wang, Jingqi Wang, Jingru Wang, Jingtong Wang, Jingwei Wang, Jingwen Wang, Jingxiao Wang, Jingyang Wang, Jingyi Wang, Jingying Wang, Jingyu Wang, Jingyue Wang, Jingyun Wang, Jingzhou Wang, Jinhai Wang, Jinhao Wang, Jinhe Wang, Jinhua Wang, Jinhuan Wang, Jinhui Wang, Jinjie Wang, Jinjin Wang, Jinkang Wang, Jinling Wang, Jinlong Wang, Jinmeng Wang, Jinning Wang, Jinping Wang, Jinqiu Wang, Jinrong Wang, Jinru Wang, Jinsong Wang, Jintao Wang, Jinxia Wang, Jinxiang Wang, Jinyang Wang, Jinyu Wang, Jinyue Wang, Jinyun Wang, Jinzhu Wang, Jiou Wang, Jipeng Wang, Jiqing Wang, Jiqiu Wang, Jisheng Wang, Jiu Wang, Jiucun Wang, Jiun-Ling Wang, Jiwen Wang, Jixuan Wang, Jiyan Wang, Jiying Wang, Jiyong Wang, Jizheng Wang, John Wang, Jou-Kou Wang, Joy Wang, Ju Wang, Juan Wang, Jue Wang, Jueqiong Wang, Jufeng Wang, Julie Wang, Juling Wang, Jun Kit Wang, Jun Wang, Jun Yi Wang, Jun-Feng Wang, Jun-Jie Wang, Jun-Jun Wang, Jun-Ling Wang, Jun-Sheng Wang, Jun-Sing Wang, Jun-Zhuo Wang, Jundong Wang, Junfeng Wang, Jung-Pan Wang, Junhong Wang, Junhua Wang, Junhui Wang, Junjiang Wang, Junjie Wang, Junjun Wang, Junkai Wang, Junke Wang, Junli Wang, Junlin Wang, Junling Wang, Junmei Wang, Junmin Wang, Junpeng Wang, Junping Wang, Junqin Wang, Junqing Wang, Junrui Wang, Junsheng Wang, Junshi Wang, Junshuang Wang, Junwen Wang, Junxiao Wang, Junya Wang, Junying Wang, Junyu Wang, Justin Wang, Jutao Wang, Juxiang Wang, K Wang, Kai Wang, Kai-Kun Wang, Kai-Wen Wang, Kaicen Wang, Kaihao Wang, Kaihe Wang, Kaihong Wang, Kaijie Wang, Kaijuan Wang, Kailu Wang, Kaiming Wang, Kaining Wang, Kaiting Wang, Kaixi Wang, Kaixu Wang, Kaiyan Wang, Kaiyuan Wang, Kaiyue Wang, Kan Wang, Kangli Wang, Kangling Wang, Kangmei Wang, Kangning Wang, Ke Wang, Ke-Feng Wang, KeShan Wang, Kehan Wang, Kehao Wang, Kejia Wang, Kejian Wang, Kejun Wang, Keke Wang, Keming Wang, Kenan Wang, Keqing Wang, Kesheng Wang, Kexin Wang, Keyan Wang, Keyi Wang, Keyun Wang, Kongyan Wang, Kuan Hong Wang, Kui Wang, Kun Wang, Kunhua Wang, Kunpeng Wang, Kunzheng Wang, L F Wang, L M Wang, L Wang, L Z Wang, L-S Wang, Laidi Wang, Laijian Wang, Laiyuan Wang, Lan Wang, Lan-Wan Wang, Lan-lan Wang, Lanlan Wang, Larry Wang, Le Wang, Le-Xin Wang, Ledan Wang, Lee-Kai Wang, Lei P Wang, Lei Wang, Lei-Lei Wang, Leiming Wang, Leishen Wang, Leli Wang, Leran Wang, Lexin Wang, Leying Wang, Li Chun Wang, Li Dong Wang, Li Wang, Li-Dong Wang, Li-E Wang, Li-Juan Wang, Li-Li Wang, Li-Na Wang, Li-San Wang, Li-Ting Wang, Li-Xin Wang, Li-Yong Wang, LiLi Wang, Lian Wang, Lianchun Wang, Liang Wang, Liang-Yan Wang, Liangfu Wang, Lianghai Wang, Liangli Wang, Liangliang Wang, Liangxu Wang, Lianshui Wang, Lianyong Wang, Libo Wang, Lichan Wang, Lichao Wang, Liewei Wang, Lifang Wang, Lifei Wang, Lifen Wang, Lifeng Wang, Ligang Wang, Lihong Wang, Lihua Wang, Lihui Wang, Lijia Wang, Lijin Wang, Lijing Wang, Lijuan Wang, Lijun Wang, Liling Wang, Lily Wang, Limeng Wang, Limin Wang, Liming Wang, Lin Wang, Lin-Fa Wang, Lin-Yu Wang, Lina Wang, Linfang Wang, Ling Jie Wang, Ling Wang, Ling-Ling Wang, Lingbing Wang, Lingda Wang, Linghua Wang, Linghuan Wang, Lingli Wang, Lingling Wang, Lingyan Wang, Lingzhi Wang, Linhua Wang, Linhui Wang, Linjie Wang, Linli Wang, Linlin Wang, Linping Wang, Linshu Wang, Linshuang Wang, Lintao Wang, Linxuan Wang, Linying Wang, Linyuan Wang, Liping Wang, Liqing Wang, Liqun Wang, Lirong Wang, Litao Wang, Liting Wang, Liu Wang, Liusong Wang, Liuyang Wang, Liwei Wang, Lixia Wang, Lixian Wang, Lixiang Wang, Lixin Wang, Lixing Wang, Lixiu Wang, Liyan Wang, Liyi Wang, Liying Wang, Liyong Wang, Liyuan Wang, Liyun Wang, Long Wang, Longcai Wang, Longfei Wang, Longsheng Wang, Longxiang Wang, Lou-Pin Wang, Lu Wang, Lu-Lu Wang, Lueli Wang, Lufang Wang, Luhong Wang, Luhui Wang, Lujuan Wang, Lulu Wang, Luofu Wang, Luping Wang, Luting Wang, Luwen Wang, Luxiang Wang, Luya Wang, Luyao Wang, Luyun Wang, Lynn Yuning Wang, M H Wang, M Wang, M Y Wang, M-J Wang, Maiqiu Wang, Man Wang, Mangju Wang, Manli Wang, Mao-Xin Wang, Maochun Wang, Maojie Wang, Maoju Wang, Mark Wang, Mei Wang, Mei-Gui Wang, Mei-Xia Wang, Meiding Wang, Meihui Wang, Meijun Wang, Meiling Wang, Meixia Wang, Melissa T Wang, Meng C Wang, Meng Wang, Meng Yu Wang, Meng-Dan Wang, Meng-Lan Wang, Meng-Meng Wang, Meng-Ru Wang, Meng-Wei Wang, Meng-Ying Wang, Meng-hong Wang, Mengge Wang, Menghan Wang, Menghui Wang, Mengjiao Wang, Mengjing Wang, Mengjun Wang, Menglong Wang, Menglu Wang, Mengmeng Wang, Mengqi Wang, Mengru Wang, Mengshi Wang, Mengwen Wang, Mengxiao Wang, Mengya Wang, Mengyao Wang, Mengying Wang, Mengyuan Wang, Mengyue Wang, Mengyun Wang, Mengze Wang, Mengzhao Wang, Mengzhi Wang, Mian Wang, Miao Wang, Mimi Wang, Min Wang, Min-sheng Wang, Ming Wang, Ming-Chih Wang, Ming-Hsi Wang, Ming-Jie Wang, Ming-Wei Wang, Ming-Yang Wang, Ming-Yuan Wang, Mingchao Wang, Mingda Wang, Minghua Wang, Minghuan Wang, Minghui Wang, Mingji Wang, Mingjin Wang, Minglei Wang, Mingliang Wang, Mingmei Wang, Mingming Wang, Mingqiang Wang, Mingrui Wang, Mingsong Wang, Mingxi Wang, Mingxia Wang, Mingxun Wang, Mingya Wang, Mingyang Wang, Mingyi Wang, Mingyu Wang, Mingzhi Wang, Mingzhu Wang, Minjie Wang, Minjun Wang, Minmin Wang, Minxian Wang, Minxiu Wang, Minzhou Wang, Miranda C Wang, Mo Wang, Mofei Wang, Monica Wang, Mu Wang, Mutian Wang, Muxiao Wang, Muxuan Wang, N Wang, Na Wang, Nan Wang, Nana Wang, Nanbu Wang, Nannan Wang, Nanping Wang, Neng Wang, Ni Wang, Niansong Wang, Ning Wang, Ningjian Wang, Ningli Wang, Ningyuan Wang, Nuan Wang, Oliver Wang, Ouchen Wang, P Jeremy Wang, P L Wang, P N Wang, P Wang, Pai Wang, Pan Wang, Pan-Pan Wang, Panfeng Wang, Panliang Wang, Pei Chang Wang, Pei Wang, Pei-Hua Wang, Pei-Jian Wang, Pei-Juan Wang, Pei-Wen Wang, Pei-Yu Wang, Peichang Wang, Peigeng Wang, Peihe Wang, Peijia Wang, Peijuan Wang, Peijun Wang, Peilin Wang, Peipei Wang, Peirong Wang, Peiwen Wang, Peixi Wang, Peiyao Wang, Peiyin Wang, Peng Wang, Peng-Cheng Wang, Pengbo Wang, Pengchao Wang, Pengfei Wang, Pengjie Wang, Pengju Wang, Penglai Wang, Penglong Wang, Pengpu Wang, Pengtao Wang, Pengxiang Wang, Pengyu Wang, Pin Wang, Ping Wang, Pingchuan Wang, Pingfeng Wang, Pingping Wang, Pintian Wang, Po-Jen Wang, Pu Wang, Q Wang, Q Z Wang, Qi Wang, Qi-Bing Wang, Qi-En Wang, Qi-Jia Wang, Qi-Qi Wang, Qian Wang, Qian-Liang Wang, Qian-Wen Wang, Qian-Zhu Wang, Qian-fei Wang, Qianbao Wang, Qiang Wang, Qiang-Sheng Wang, Qiangcheng Wang, Qianghu Wang, Qiangqiang Wang, Qianjin Wang, Qianliang Wang, Qianqian Wang, Qianrong Wang, Qianru Wang, Qianwen Wang, Qianxu Wang, Qiao Wang, Qiao-Ping Wang, Qiaohong Wang, Qiaoqi Wang, Qiaoqiao Wang, Qifan Wang, Qifei Wang, Qifeng Wang, Qigui Wang, Qihao Wang, Qihua Wang, Qijia Wang, Qiming Wang, Qin Wang, Qing Jun Wang, Qing K Wang, Qing Kenneth Wang, Qing Mei Wang, Qing Wang, Qing-Bin Wang, Qing-Dong Wang, Qing-Jin Wang, Qing-Liang Wang, Qing-Mei Wang, Qing-Yan Wang, Qing-Yuan Wang, Qing-Yun Wang, QingDong Wang, Qingchun Wang, Qingfa Wang, Qingfeng Wang, Qinghang Wang, Qingliang Wang, Qinglin Wang, Qinglu Wang, Qingming Wang, Qingping Wang, Qingqing Wang, Qingshi Wang, Qingshui Wang, Qingsong Wang, Qingtong Wang, Qingyong Wang, Qingyu Wang, Qingyuan Wang, Qingyun Wang, Qingzhong Wang, Qinqin Wang, Qinrong Wang, Qintao Wang, Qinwen Wang, Qinyun Wang, Qiong Wang, Qiqi Wang, Qirui Wang, Qishan Wang, Qiu-Ling Wang, Qiu-Xia Wang, Qiuhong Wang, Qiuli Wang, Qiuling Wang, Qiuning Wang, Qiuping Wang, Qiushi Wang, Qiuting Wang, Qiuyan Wang, Qiuyu Wang, Qiwei Wang, Qixue Wang, Qiyu Wang, Qiyuan Wang, Quan Wang, Quan-Ming Wang, Quanli Wang, Quanren Wang, Quanxi Wang, Qun Wang, Qunxian Wang, Qunzhi Wang, R Wang, Ran Wang, Ranjing Wang, Ranran Wang, Re-Hua Wang, Ren Wang, Rencheng Wang, Renjun Wang, Renqian Wang, Renwei Wang, Renxi Wang, Renxiao Wang, Renyuan Wang, Rihua Wang, Rikang Wang, Rixiang Wang, Robert Yl Wang, Rong Wang, Rong-Chun Wang, Rong-Rong Wang, Rong-Tsorng Wang, RongRong Wang, Rongjia Wang, Rongping Wang, Rongyun Wang, Ru Wang, RuNan Wang, Ruey-Yun Wang, Rufang Wang, Ruhan Wang, Rui Wang, Rui-Hong Wang, Rui-Min Wang, Rui-Ping Wang, Rui-Rui Wang, Ruibin Wang, Ruibing Wang, Ruibo Wang, Ruicheng Wang, Ruifang Wang, Ruijing Wang, Ruimeng Wang, Ruimin Wang, Ruiming Wang, Ruinan Wang, Ruining Wang, Ruiquan Wang, Ruiwen Wang, Ruixian Wang, Ruixin Wang, Ruixuan Wang, Ruixue Wang, Ruiying Wang, Ruizhe Wang, Ruizhi Wang, Rujie Wang, Ruling Wang, Ruming Wang, Runci Wang, Runuo Wang, Runze Wang, Runzhi Wang, Ruo-Nan Wang, Ruo-Ran Wang, Ruonan Wang, Ruosu Wang, Ruoxi Wang, Rurong Wang, Ruting Wang, Ruxin Wang, Ruxuan Wang, Ruyue Wang, S L Wang, S S Wang, S Wang, S X Wang, Sa A Wang, Sa Wang, Saifei Wang, Saili Wang, Sainan Wang, Saisai Wang, Sangui Wang, Sanwang Wang, Sasa Wang, Sen Wang, Seok Mui Wang, Seungwon Wang, Sha Wang, Shan Wang, Shan-Shan Wang, Shang Wang, Shangyu Wang, Shanshan Wang, Shao-Kang Wang, Shaochun Wang, Shaohsu Wang, Shaokun Wang, Shaoli Wang, Shaolian Wang, Shaoshen Wang, Shaowei Wang, Shaoyi Wang, Shaoying Wang, Shaoyu Wang, Shaozheng Wang, Shasha Wang, Shau-Chun Wang, Shawn Wang, Shen Wang, Shen-Nien Wang, Shenao Wang, Sheng Wang, Sheng-Min Wang, Sheng-Nan Wang, Sheng-Ping Wang, Sheng-Quan Wang, Sheng-Yang Wang, Shengdong Wang, Shengjie Wang, Shengli Wang, Shengqi Wang, Shengya Wang, Shengyao Wang, Shengyu Wang, Shengyuan Wang, Shenqi Wang, Sheri Wang, Shi Wang, Shi-Cheng Wang, Shi-Han Wang, Shi-Qi Wang, Shi-Xin Wang, Shi-Yao Wang, Shibin Wang, Shichao Wang, Shicung Wang, Shidong Wang, Shifa Wang, Shifeng Wang, Shih-Wei Wang, Shihan Wang, Shihao Wang, Shihua Wang, Shijie Wang, Shijin Wang, Shijun Wang, Shikang Wang, Shimiao Wang, Shiqi Wang, Shiqiang Wang, Shitao Wang, Shitian Wang, Shiwen Wang, Shixin Wang, Shixuan Wang, Shiyang Wang, Shiyao Wang, Shiyin Wang, Shiyu Wang, Shiyuan Wang, Shiyue Wang, Shizhi Wang, Shouli Wang, Shouling Wang, Shouzhi Wang, Shu Wang, Shu-Huei Wang, Shu-Jin Wang, Shu-Ling Wang, Shu-Na Wang, Shu-Song Wang, Shu-Xia Wang, Shu-qiang Wang, Shuai Wang, Shuaiqin Wang, Shuang Wang, Shuang-Shuang Wang, Shuang-Xi Wang, Shuangyuan Wang, Shubao Wang, Shudan Wang, Shuge Wang, Shuguang Wang, Shuhe Wang, Shuiliang Wang, Shuiyun Wang, Shujin Wang, Shukang Wang, Shukui Wang, Shun Wang, Shuning Wang, Shunjun Wang, Shunran Wang, Shuo Wang, Shuping Wang, Shuqi Wang, Shuqing Wang, Shuren Wang, Shusen Wang, Shusheng Wang, Shushu Wang, Shuu-Jiun Wang, Shuwei Wang, Shuxia Wang, Shuxin Wang, Shuya Wang, Shuye Wang, Shuyue Wang, Shuzhe Wang, Shuzhen Wang, Shuzhong Wang, Shyi-Gang P Wang, Si Wang, Sibo Wang, Sidan Wang, Sihua Wang, Sijia Wang, Silas L Wang, Silu Wang, Simeng Wang, Siqi Wang, Siqing Wang, Siwei Wang, Siyang Wang, Siyi Wang, Siying Wang, Siyu Wang, Siyuan Wang, Siyue Wang, Song Wang, Songjiao Wang, Songlin Wang, Songping Wang, Songsong Wang, Songtao Wang, Sophie H Wang, Stephani Wang, Su'e Wang, Su-Guo Wang, Su-Hua Wang, Sufang Wang, Sugai Wang, Sui Wang, Suiyan Wang, Sujie Wang, Sujuan Wang, Suli Wang, Sun Wang, Supeng Perry Wang, Suxia Wang, Suyun Wang, Suzhen Wang, T Q Wang, T Wang, T Y Wang, Taian Wang, Taicheng Wang, Taishu Wang, Tammy C Wang, Tao Wang, Taoxia Wang, Teng Wang, Tengfei Wang, Theodore Wang, Thomas T Y Wang, Tian Wang, Tian-Li Wang, Tian-Lu Wang, Tian-Tian Wang, Tian-Yi Wang, Tiancheng Wang, Tiange Wang, Tianhao Wang, Tianhu Wang, Tianhui Wang, Tianjing Wang, Tianjun Wang, Tianlin Wang, Tiannan Wang, Tianpeng Wang, Tianqi Wang, Tianqin Wang, Tianqing Wang, Tiansheng Wang, Tiansong Wang, Tiantian Wang, Tianyi Wang, Tianying Wang, Tianyuan Wang, Tielin Wang, Tienju Wang, Tieqiao Wang, Timothy C Wang, Ting Chen Wang, Ting Wang, Ting-Chen Wang, Ting-Hua Wang, Ting-Ting Wang, Tingting Wang, Tingye Wang, Tingyu Wang, Tom J Wang, Tong Wang, Tong-Hong Wang, Tongsong Wang, Tongtong Wang, Tongxia Wang, Tongxin Wang, Tongyao Wang, Tony Wang, Tzung-Dau Wang, Victoria Wang, Vivian Wang, W Wang, Wanbing Wang, Wanchun Wang, Wang Wang, Wangxia Wang, Wanliang Wang, Wanxia Wang, Wanyao Wang, Wanyi Wang, Wanyu Wang, Wayseen Wang, Wei Wang, Wei-En Wang, Wei-Feng Wang, Wei-Lien Wang, Wei-Qi Wang, Wei-Ting Wang, Wei-Wei Wang, Weicheng Wang, Weiding Wang, Weidong Wang, Weifan Wang, Weiguang Wang, Weihao Wang, Weihong Wang, Weihua Wang, Weijian Wang, Weijie Wang, Weijun Wang, Weilin Wang, Weiling Wang, Weilong Wang, Weimin Wang, Weina Wang, Weining Wang, Weipeng Wang, Weiqin Wang, Weiqing Wang, Weirong Wang, Weiwei Wang, Weiwen Wang, Weixiao Wang, Weixue Wang, Weiyan Wang, Weiyu Wang, Weiyuan Wang, Weizhen Wang, Weizhi Wang, Weizhong Wang, Wen Wang, Wen-Chang Wang, Wen-Der Wang, Wen-Fei Wang, Wen-Jie Wang, Wen-Jun Wang, Wen-Qing Wang, Wen-Xuan Wang, Wen-Yan Wang, Wen-Ying Wang, Wen-Yong Wang, Wen-mei Wang, Wenbin Wang, Wenbo Wang, Wence Wang, Wenchao Wang, Wencheng Wang, Wendong Wang, Wenfei Wang, Wengong Wang, Wenhan Wang, Wenhao Wang, Wenhe Wang, Wenhui Wang, Wenjie Wang, Wenjing Wang, Wenju Wang, Wenjuan Wang, Wenjun Wang, Wenkai Wang, Wenkang Wang, Wenke Wang, Wenming Wang, Wenqi Wang, Wenqiang Wang, Wenqing Wang, Wenran Wang, Wenrui Wang, Wentao Wang, Wentian Wang, Wenting Wang, Wenwen Wang, Wenxia Wang, Wenxian Wang, Wenxiang Wang, Wenxiu Wang, Wenxuan Wang, Wenya Wang, Wenyan Wang, Wenyi Wang, Wenying Wang, Wenyu Wang, Wenyuan Wang, Wenzhou Wang, William Wang, Won-Jing Wang, Wu-Wei Wang, Wuji Wang, Wuqing Wang, Wusan Wang, X E Wang, X F Wang, X O Wang, X S Wang, X Wang, X-T Wang, Xi Wang, Xi-Hong Wang, Xi-Rui Wang, Xia Wang, Xian Wang, Xian-e Wang, Xianding Wang, Xianfeng Wang, Xiang Wang, Xiang-Dong Wang, Xiangcheng Wang, Xiangding Wang, Xiangdong Wang, Xiangguo Wang, Xianghua Wang, Xiangkun Wang, Xiangrong Wang, Xiangru Wang, Xiangwei Wang, Xiangyu Wang, Xianna Wang, Xianqiang Wang, Xianrong Wang, Xianshi Wang, Xianshu Wang, Xiansong Wang, Xiantao Wang, Xianwei Wang, Xianxing Wang, Xianze Wang, Xianzhe Wang, Xianzong Wang, Xiao Ling Wang, Xiao Qun Wang, Xiao Wang, Xiao-Ai Wang, Xiao-Fei Wang, Xiao-Hui Wang, Xiao-Jie Wang, Xiao-Juan Wang, Xiao-Lan Wang, Xiao-Li Wang, Xiao-Lin Wang, Xiao-Ming Wang, Xiao-Pei Wang, Xiao-Qian Wang, Xiao-Qun Wang, Xiao-Tong Wang, Xiao-Xia Wang, Xiao-Yi Wang, Xiao-Yun Wang, Xiao-jian WANG, Xiao-liang Wang, Xiaobin Wang, Xiaobo Wang, Xiaochen Wang, Xiaochuan Wang, Xiaochun Wang, Xiaodan Wang, Xiaoding Wang, Xiaodong Wang, Xiaofang Wang, Xiaofei Wang, Xiaofen Wang, Xiaofeng Wang, Xiaogang Wang, Xiaohong Wang, Xiaohu Wang, Xiaohua Wang, Xiaohui Wang, Xiaojia Wang, Xiaojian Wang, Xiaojiao Wang, Xiaojie Wang, Xiaojing Wang, Xiaojuan Wang, Xiaojun Wang, Xiaokun Wang, Xiaole Wang, Xiaoli Wang, Xiaoliang Wang, Xiaolin Wang, Xiaoling Wang, Xiaolong Wang, Xiaolu Wang, Xiaolun Wang, Xiaoman Wang, Xiaomei Wang, Xiaomeng Wang, Xiaomin Wang, Xiaoming Wang, Xiaona Wang, Xiaonan Wang, Xiaoning Wang, Xiaoqi Wang, Xiaoqian Wang, Xiaoqin Wang, Xiaoqing Wang, Xiaoqiu Wang, Xiaoqun Wang, Xiaorong Wang, Xiaorui Wang, Xiaoshan Wang, Xiaosong Wang, Xiaotang Wang, Xiaoting Wang, Xiaotong Wang, Xiaowei Wang, Xiaowen Wang, Xiaowu Wang, Xiaoxia Wang, Xiaoxiao Wang, Xiaoxin Wang, Xiaoxin X Wang, Xiaoxuan Wang, Xiaoya Wang, Xiaoyan Wang, Xiaoyang Wang, Xiaoye Wang, Xiaoying Wang, Xiaoyu Wang, Xiaozhen Wang, Xiaozhi Wang, Xiaozhong Wang, Xiaozhu Wang, Xichun Wang, Xidi Wang, Xietong Wang, Xifeng Wang, Xifu Wang, Xijun Wang, Xike Wang, Xin Wang, Xin Wei Wang, Xin-Hua Wang, Xin-Liang Wang, Xin-Ming Wang, Xin-Peng Wang, Xin-Qun Wang, Xin-Shang Wang, Xin-Xin Wang, Xin-Yang Wang, Xin-Yue Wang, Xinbo Wang, Xinchang Wang, Xinchao Wang, Xinchen Wang, Xincheng Wang, Xinchun Wang, Xindi Wang, Xindong Wang, Xing Wang, Xing-Huan Wang, Xing-Jin Wang, Xing-Jun Wang, Xing-Lei Wang, Xing-Ping Wang, Xing-Quan Wang, Xingbang Wang, Xingchen Wang, Xingde Wang, Xingguo Wang, Xinghao Wang, Xinghui Wang, Xingjie Wang, Xingjin Wang, Xinglei Wang, Xinglong Wang, Xingqin Wang, Xinguo Wang, Xingxin Wang, Xingxing Wang, Xingye Wang, Xingyu Wang, Xingyue Wang, Xingyun Wang, Xinhui Wang, Xinjing Wang, Xinjun Wang, Xinke Wang, Xinkun Wang, Xinli Wang, Xinlin Wang, Xinlong Wang, Xinmei Wang, Xinqi Wang, Xinquan Wang, Xinran Wang, Xinrong Wang, Xinru Wang, Xinrui Wang, Xinshuai Wang, Xintong Wang, Xinwen Wang, Xinxin Wang, Xinyan Wang, Xinyang Wang, Xinye Wang, Xinyi Wang, Xinying Wang, Xinyu Wang, Xinyue Wang, Xinzhou Wang, Xiong Wang, Xiongjun Wang, Xiru Wang, Xitian Wang, Xiu-Lian Wang, Xiu-Ping Wang, Xiufen Wang, Xiujuan Wang, Xiujun Wang, Xiurong Wang, Xiuwen Wang, Xiuyu Wang, Xiuyuan Hugh Wang, Xixi Wang, Xixiang Wang, Xiyan Wang, Xiyue Wang, Xizhi Wang, Xu Wang, Xu-Hong Wang, Xuan Wang, Xuan-Ren Wang, Xuan-Ying Wang, Xuanwen Wang, Xuanyi Wang, Xubo Wang, Xudong Wang, Xue Wang, Xue-Feng Wang, Xue-Hua Wang, Xue-Lei Wang, Xue-Lian Wang, Xue-Rui Wang, Xue-Yao Wang, Xue-Ying Wang, Xuebin Wang, Xueding Wang, Xuedong Wang, Xuefei Wang, Xuefeng Wang, Xueguo Wang, Xuehao Wang, Xuejie Wang, Xuejing Wang, Xueju Wang, Xuejun Wang, Xuekai Wang, Xuelai Wang, Xuelian Wang, Xuelin Wang, Xuemei Wang, Xuemin Wang, Xueping Wang, Xueqian Wang, Xueqin Wang, Xuesong Wang, Xueting Wang, Xuewei Wang, Xuewen Wang, Xuexiang Wang, Xueyan Wang, Xueyi Wang, Xueying Wang, Xueyun Wang, Xuezhen Wang, Xuezheng Wang, Xufei Wang, Xujing Wang, Xuliang Wang, Xumeng Wang, Xun Wang, Xuping Wang, Xuqiao Wang, Xuru Wang, Xusheng Wang, Xv Wang, Y Alan Wang, Y B Wang, Y H Wang, Y L Wang, Y P Wang, Y Wang, Y Y Wang, Y Z Wang, Y-H Wang, Y-S Wang, Ya Qi Wang, Ya Wang, Ya Xing Wang, Ya-Han Wang, Ya-Jie Wang, Ya-Long Wang, Ya-Nan Wang, Ya-Ping Wang, Ya-Qin Wang, Ya-Zhou Wang, Yachen Wang, Yachun Wang, Yadong Wang, Yafang Wang, Yafen Wang, Yahong Wang, Yahui Wang, Yajie Wang, Yajing Wang, Yajun Wang, Yake Wang, Yakun Wang, Yali Wang, Yalin Wang, Yaling Wang, Yalong Wang, Yan Ming Wang, Yan Wang, Yan-Chao Wang, Yan-Chun Wang, Yan-Feng Wang, Yan-Ge Wang, Yan-Jiang Wang, Yan-Jun Wang, Yan-Ming Wang, Yan-Yang Wang, Yan-Yi Wang, Yan-Zi Wang, Yana Wang, Yanan Wang, Yanbin Wang, Yanbing Wang, Yanchun Wang, Yancun Wang, Yanfang Wang, Yanfei Wang, Yanfeng Wang, Yang Wang, Yang-Yang Wang, Yange Wang, Yanggan Wang, Yangpeng Wang, Yangyang Wang, Yangyufan Wang, Yanhai Wang, Yanhong Wang, Yanhua Wang, Yanhui Wang, Yani Wang, Yanjin Wang, Yanjun Wang, Yankun Wang, Yanlei Wang, Yanli Wang, Yanliang Wang, Yanlin Wang, Yanling Wang, Yanmei Wang, Yanming Wang, Yanni Wang, Yanong Wang, Yanping Wang, Yanqing Wang, Yanru Wang, Yanting Wang, Yanwen Wang, Yanxia Wang, Yanxing Wang, Yanyang Wang, Yanyun Wang, Yanzhe Wang, Yanzhu Wang, Yao Wang, Yaobin Wang, Yaochun Wang, Yaodong Wang, Yaohe Wang, Yaokun Wang, Yaoling Wang, Yaolou Wang, Yaoxian Wang, Yaoxing Wang, Yaozhi Wang, Yapeng Wang, Yaping Wang, Yaqi Wang, Yaqian Wang, Yaqiong Wang, Yaru Wang, Yatao Wang, Yating Wang, Yawei Wang, Yaxian Wang, Yaxin Wang, Yaxiong Wang, Yaxuan Wang, Yayu Wang, Yazhou Wang, Ye Wang, Ye-Ran Wang, Yefu Wang, Yeh-Han Wang, Yehan Wang, Yeming Wang, Yen-Feng Wang, Yen-Sheng Wang, Yeou-Lih Wang, Yeqi Wang, Yezhou Wang, Yi Fan Wang, Yi Lei Wang, Yi Wang, Yi-Cheng Wang, Yi-Chuan Wang, Yi-Ming Wang, Yi-Ni Wang, Yi-Ning Wang, Yi-Shan Wang, Yi-Shiuan Wang, Yi-Shu Wang, Yi-Tao Wang, Yi-Ting Wang, Yi-Wen Wang, Yi-Xin Wang, Yi-Xuan Wang, Yi-Yi Wang, Yi-Ying Wang, Yi-Zhen Wang, Yi-sheng Wang, YiLi Wang, Yian Wang, Yibin Wang, Yibing Wang, Yichen Wang, Yicheng Wang, Yichuan Wang, Yifan Wang, Yifei Wang, Yigang Wang, Yige Wang, Yihan Wang, Yihao Wang, Yihe Wang, Yijin Wang, Yijing Wang, Yijun Wang, Yikang Wang, Yike Wang, Yilin Wang, Yilu Wang, Yimeng Wang, Yiming Wang, Yin Wang, Yin-Hu Wang, Yinan Wang, Yinbo Wang, Yindan Wang, Ying Wang, Ying-Piao Wang, Ying-Wei Wang, Ying-Zi Wang, Yingbo Wang, Yingcheng Wang, Yingchun Wang, Yingfei Wang, Yingge Wang, Yinggui Wang, Yinghui Wang, Yingjie Wang, Yingmei Wang, Yingna Wang, Yingping Wang, Yingqiao Wang, Yingtai Wang, Yingte Wang, Yingwei Wang, Yingwen Wang, Yingxiong Wang, Yingxue Wang, Yingyi Wang, Yingying Wang, Yingzi Wang, Yinhuai Wang, Yining E Wang, Yinong Wang, Yinsheng Wang, Yintao Wang, Yinuo Wang, Yinxiong Wang, Yinyin Wang, Yiou Wang, Yipeng Wang, Yiping Wang, Yiqi Wang, Yiqiao Wang, Yiqin Wang, Yiqing Wang, Yiquan Wang, Yirong Wang, Yiru Wang, Yirui Wang, Yishan Wang, Yishu Wang, Yitao Wang, Yiting Wang, Yiwei Wang, Yiwen Wang, Yixi Wang, Yixian Wang, Yixuan Wang, Yiyan Wang, Yiyi Wang, Yiying Wang, Yizhe Wang, Yong Wang, Yong-Bo Wang, Yong-Gang Wang, Yong-Jie Wang, Yong-Jun Wang, Yong-Tang Wang, Yongbin Wang, Yongdi Wang, Yongfei Wang, Yongfeng Wang, Yonggang Wang, Yonghong Wang, Yongjie Wang, Yongjun Wang, Yongkang Wang, Yongkuan Wang, Yongli Wang, Yongliang Wang, Yonglun Wang, Yongmei Wang, Yongming Wang, Yongni Wang, Yongqiang Wang, Yongqing Wang, Yongrui Wang, Yongsheng Wang, Yongxiang Wang, Yongyi Wang, Yongzhong Wang, You Wang, Youhua Wang, Youji Wang, Youjie Wang, Youli Wang, Youzhao Wang, Youzhi Wang, Yu Qin Wang, Yu Tian Wang, Yu Wang, Yu'e Wang, Yu-Chen Wang, Yu-Fan Wang, Yu-Fen Wang, Yu-Hang Wang, Yu-Hui Wang, Yu-Ping Wang, Yu-Ting Wang, Yu-Wei Wang, Yu-Wen Wang, Yu-Ying Wang, Yu-Zhe Wang, Yu-Zhuo Wang, Yuan Wang, Yuan-Hung Wang, Yuanbo Wang, Yuanfan Wang, Yuanjiang Wang, Yuanli Wang, Yuanqiang Wang, Yuanqing Wang, Yuanyong Wang, Yuanyuan Wang, Yuanzhen Wang, Yubing Wang, Yubo Wang, Yuchen Wang, Yucheng Wang, Yuchuan Wang, Yudong Wang, Yue Wang, Yue-Min Wang, Yue-Nan Wang, YueJiao Wang, Yuebing Wang, Yuecong Wang, Yuegang Wang, Yuehan Wang, Yuehong Wang, Yuehu Wang, Yuehua Wang, Yuelong Wang, Yuemiao Wang, Yueshen Wang, Yueting Wang, Yuewei Wang, Yuexiang Wang, Yuexin Wang, Yueying Wang, Yueze Wang, Yufei Wang, Yufeng Wang, Yugang Wang, Yuh-Hwa Wang, Yuhan Wang, Yuhang Wang, Yuhua Wang, Yuhuai Wang, Yuhuan Wang, Yuhui Wang, Yujia Wang, Yujiao Wang, Yujie Wang, Yujiong Wang, Yulai Wang, Yulei Wang, Yuli Wang, Yuliang Wang, Yulin Wang, Yuling Wang, Yulong Wang, Yumei Wang, Yumeng Wang, Yumin Wang, Yuming Wang, Yun Wang, Yun Yong Wang, Yun-Hui Wang, Yun-Jin Wang, Yun-Xing Wang, Yunbing Wang, Yunce Wang, Yunchao Wang, Yuncong Wang, Yunduan Wang, Yunfang Wang, Yunfei Wang, Yunhan Wang, Yunhe Wang, Yunong Wang, Yunpeng Wang, Yunqiong Wang, Yuntai Wang, Yunzhang Wang, Yunzhe Wang, Yunzhi Wang, Yupeng Wang, Yuping Wang, Yuqi Wang, Yuqian Wang, Yuqiang Wang, Yuqin Wang, Yusha Wang, Yushe Wang, Yusheng Wang, Yutao Wang, Yuting Wang, Yuwei Wang, 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Zhengyi Wang, Zhengyu Wang, Zhenhua Wang, Zhenning Wang, Zhenqian Wang, Zhenshan Wang, Zhentang Wang, Zhenwei Wang, Zhenxi Wang, Zhenyu Wang, Zhenze Wang, Zhenzhen Wang, Zheyi Wang, Zheyue Wang, Zhezhi Wang, Zhi Wang, Zhi Xiao Wang, Zhi-Gang Wang, Zhi-Guo Wang, Zhi-Hao Wang, Zhi-Hong Wang, Zhi-Hua Wang, Zhi-Jian Wang, Zhi-Long Wang, Zhi-Qin Wang, Zhi-Wei Wang, Zhi-Xiao Wang, Zhi-Xin Wang, Zhibo Wang, Zhichao Wang, Zhicheng Wang, Zhicun Wang, Zhidong Wang, Zhifang Wang, Zhifeng Wang, Zhifu Wang, Zhigang Wang, Zhige Wang, Zhiguo Wang, Zhihao Wang, Zhihong Wang, Zhihua Wang, Zhihui Wang, Zhiji Wang, Zhijian Wang, Zhijie Wang, Zhijun Wang, Zhilun Wang, Zhimei Wang, Zhimin Wang, Zhipeng Wang, Zhiping Wang, Zhiqi Wang, Zhiqian Wang, Zhiqiang Wang, Zhiqing Wang, Zhiren Wang, Zhiruo Wang, Zhisheng Wang, Zhitao Wang, Zhiting Wang, Zhiwu Wang, Zhixia Wang, Zhixiang Wang, Zhixiao Wang, Zhixin Wang, Zhixing Wang, Zhixiong Wang, Zhixiu Wang, Zhiying Wang, Zhiyong Wang, Zhiyou Wang, Zhiyu Wang, Zhiyuan Wang, Zhizheng Wang, Zhizhong Wang, Zhong Wang, Zhong-Hao Wang, Zhong-Hui Wang, Zhong-Ping Wang, Zhong-Yu Wang, ZhongXia Wang, Zhongfang Wang, Zhongjing Wang, Zhongli Wang, Zhonglin Wang, Zhongqun Wang, Zhongsu Wang, Zhongwei Wang, Zhongyi Wang, Zhongyu Wang, Zhongyuan Wang, Zhongzhi Wang, Zhou Wang, Zhou-Ping Wang, Zhoufeng Wang, Zhouguang Wang, Zhuangzhuang Wang, Zhugang Wang, Zhulin Wang, Zhulun Wang, Zhuo Wang, Zhuo-Hui Wang, Zhuo-Jue Wang, Zhuo-Xin Wang, Zhuowei Wang, Zhuoying Wang, Zhuozhong Wang, Zhuqing Wang, Zi Wang, Zi Xuan Wang, Zi-Hao Wang, Zi-Qi Wang, Zi-Yi Wang, Zicheng Wang, Zifeng Wang, Zihan Wang, Ziheng Wang, Zihua Wang, Zihuan Wang, Zijian Wang, Zijie Wang, Zijue Wang, Zijun Wang, Zikang Wang, Zikun Wang, Ziliang Wang, Zilin Wang, Ziling Wang, Zilong Wang, Zining Wang, Ziping Wang, Ziqi Wang, Ziqian Wang, Ziqiang Wang, Ziqing Wang, Ziqiu Wang, Zitao Wang, Ziwei Wang, Zixi Wang, Zixia Wang, Zixian Wang, Zixiang Wang, Zixu Wang, Zixuan Wang, Ziyi Wang, Ziying Wang, Ziyu Wang, Ziyun Wang, Zongbao Wang, Zonggui Wang, Zongji Wang, Zongkui Wang, Zongqi Wang, Zongwei Wang, Zou Wang, Zulong Wang, Zumin Wang, Zun Wang, Zunxian Wang, Zuo Wang, Zuoheng Wang, Zuoyan Wang, Zusen Wang
articles
Scott K Sherman, Jessica E Maxwell, Jennifer C Carr +4 more · 2014 · The Journal of surgical research · Elsevier · added 2026-04-24
Compounds targeting somatostatin-receptor-type-2 (SSTR2) are useful for small bowel neuroendocrine tumor (SBNET) and pancreatic neuroendocrine tumor (PNET) imaging and treatment. We recently character Show more
Compounds targeting somatostatin-receptor-type-2 (SSTR2) are useful for small bowel neuroendocrine tumor (SBNET) and pancreatic neuroendocrine tumor (PNET) imaging and treatment. We recently characterized expression of 13 cell surface receptor genes in SBNETs and PNETs, identifying three drug targets (GIPR, OXTR, and OPRK1). This study set out to characterize expression of this gene panel in the less common neuroendocrine tumors of the stomach and duodenum (gastric and duodenal neuroendocrine tumors [GDNETs]). Primary tumors and adjacent normal tissue were collected at surgery, RNA was extracted, and expression of 13 target genes was determined by quantitative polymerase chain reaction. Expression was normalized to GAPDH and POLR2A internal control genes. Expression relative to normal tissue (ddCT) and absolute expression (dCT) were calculated. Wilcoxon tests compared median expression with false discovery rate correction for multiple comparisons. Gene expression was similar in two gastric and seven duodenal tumors, and these were analyzed together. Like SBNETs (n = 63) and PNETs (n = 51), GDNETs showed significant overexpression compared with normal tissue of BRS3, GIPR, GRM1, GPR113, OPRK1, and SSTR2 (P < 0.05 for all). Of these, SSTR2 had the highest absolute expression in GDNETs (median dCT 4.0). Absolute expression of BRS3, GRM1, GPR113, and OPRK1 was significantly lower than SSTR2 in GDNETs (P < 0.05 for all), whereas expression of GIPR was similar to SSTR2 (median 4.3, P = 0.4). As in SBNETs and PNETs, GIPR shows absolute expression close to SSTR2 but has greater overexpression relative to normal tissue (21.1 versus 3.5-fold overexpression). We conclude that GIPR could provide an improved signal-to-noise ratio for imaging versus SSTR2 and represents a promising novel therapeutic target in GDNETs. Show less
📄 PDF DOI: 10.1016/j.jss.2014.01.044
GIPR
Belinda K Cornes, Jennifer A Brody, Naghmeh Nikpoor +25 more · 2014 · Circulation. Cardiovascular genetics · added 2026-04-24
Common variation at the 11p11.2 locus, encompassing MADD, ACP2, NR1H3, MYBPC3, and SPI1, has been associated in genome-wide association studies with fasting glucose and insulin (FI). In the Cohorts fo Show more
Common variation at the 11p11.2 locus, encompassing MADD, ACP2, NR1H3, MYBPC3, and SPI1, has been associated in genome-wide association studies with fasting glucose and insulin (FI). In the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study, we sequenced 5 gene regions at 11p11.2 to identify rare, potentially functional variants influencing fasting glucose or FI levels. Sequencing (mean depth, 38×) across 16.1 kb in 3566 individuals without diabetes mellitus identified 653 variants, 79.9% of which were rare (minor allele frequency <1%) and novel. We analyzed rare variants in 5 gene regions with FI or fasting glucose using the sequence kernel association test. At NR1H3, 53 rare variants were jointly associated with FI (P=2.73×10(-3)); of these, 7 were predicted to have regulatory function and showed association with FI (P=1.28×10(-3)). Conditioning on 2 previously associated variants at MADD (rs7944584, rs10838687) did not attenuate this association, suggesting that there are >2 independent signals at 11p11.2. One predicted regulatory variant, chr11:47227430 (hg18; minor allele frequency=0.00068), contributed 20.6% to the overall sequence kernel association test score at NR1H3, lies in intron 2 of NR1H3, and is a predicted binding site for forkhead box A1 (FOXA1), a transcription factor associated with insulin regulation. In human HepG2 hepatoma cells, the rare chr11:47227430 A allele disrupted FOXA1 binding and reduced FOXA1-dependent transcriptional activity. Sequencing at 11p11.2-NR1H3 identified rare variation associated with FI. One variant, chr11:47227430, seems to be functional, with the rare A allele reducing transcription factor FOXA1 binding and FOXA1-dependent transcriptional activity. Show less
📄 PDF DOI: 10.1161/CIRCGENETICS.113.000169
ACP2
Xin Liu, Qi Chen, Hui-Ju Tsai +14 more · 2014 · Environmental and molecular mutagenesis · Wiley · added 2026-04-24
Maternal obesity is associated with a variety of common diseases in the offspring. One possible underlying mechanism could be maternal obesity induced alterations in DNA methylation. However, this hyp Show more
Maternal obesity is associated with a variety of common diseases in the offspring. One possible underlying mechanism could be maternal obesity induced alterations in DNA methylation. However, this hypothesis is yet to be tested. We performed epigenomic mapping of cord blood among 308 Black mother-infant pairs delivered at term at the Boston Medical Center using the Illumina HumanMethylation27 BeadChip. Linear regression and pathway analyses were conducted to evaluate the associations between DNA methylation levels and prepregnancy maternal BMI (<25, 25-30, ≥30 kg/m(2) ). The methylation levels of 20 CpG sites were associated with maternal BMI at a significance level of P-value <10(-4) in the overall sample, and boys and girls, separately. One CpG site remained statistically significant after correction for multiple comparisons (FDR corrected P-value = 0.04) and was annotated to a potential cancer gene, ZCCHC10. Some of the other CpG site annotated genes appear to be critical to the development of cancers and cardiovascular diseases (i.e., WNT16, C18orf8, ANGPTL2, SAPCD2, ADCY3, PRR16, ERBB2, DOK2, PLAC1). Significant findings from pathway analysis, such as infectious and inflammatory and lipid metabolism pathways, lends support for the potential impact of maternal BMI on the above stated disorders. This study demonstrates that prepregnancy maternal BMI might lead to alterations in offspring DNA methylation in genes relevant to the development of a range of complex chronic diseases, providing evidence of trans-generational influence on disease susceptibility via epigenetic mechanism. Show less
📄 PDF DOI: 10.1002/em.21827
ADCY3
Ling Shen, Yin Liu, David Q H Wang +3 more · 2014 · Endocrinology · added 2026-04-24
Although estrogens have been implicated in the regulation of apolipoprotein A-IV (apo A-IV) gene expression in the nucleus tractus solitarius, previous studies have not defined the molecular mechanism Show more
Although estrogens have been implicated in the regulation of apolipoprotein A-IV (apo A-IV) gene expression in the nucleus tractus solitarius, previous studies have not defined the molecular mechanism. The aim of this study was to examine the transcriptional mechanisms involved in regulation of apo A-IV gene expression. Using cultured primary neuronal cells from rat embryonic brainstems, we found that treatment with 10nM 17β-estradiol-3-benzoate (E2) or 4,4',4″-(4-propyl-[1H]-pyrazole-1,3,5-triyl) trisphenol (an estrogen receptor [ER]α agonist), but not 2,3-bis(4-hydroxyphenyl)-propionitrile (an ERβ agonist), significantly increased apo A-IV gene expression, compared with vehicle treatment. This effect of E2 was abolished when the cells were incubated with E2 linked to BSA, which prevents E2 from entering cells, implying that a nongenomic mechanism of E2 is not involved. Two putative estrogen response elements were identified at the 5'-upstream region of the apo A-IV gene promoter, but only 1 of them was able to recruit ERα, leading to increased apo A-IV gene expression, as determined by chromatin immunoprecipitation assay and luciferase activity analysis. A cyclic regimen of E2 or 4,4',4″-(4-propyl-[1H]-pyrazole-1,3,5-triyl) trisphenol treatment for 8 cycles (4 d/cycle, mimicking the ovarian cycle of female rats) in ovariectomized female rats significantly reduced food intake and body weight gain and increased apo A-IV gene expression in the nucleus tractus solitarius, relative to vehicle. These data collectively demonstrate that nuclear ERα is the primary mediator of E2's action on apo A-IV gene expression and suggest that increased signaling of endogenous apo A-IV may at least partially mediate E2-induced inhibitory effect on feeding. Show less
no PDF DOI: 10.1210/en.2014-1239
APOA4
Guanglin Cui, Zongzhe Li, Rui Li +5 more · 2014 · Journal of the American College of Cardiology · Elsevier · added 2026-04-24
Recent genome-wide association studies identified the APOA5/A4/C3/A1 gene cluster polymorphisms influencing triglyceride level and risk of coronary artery disease (CAD). The purposes of this study wer Show more
Recent genome-wide association studies identified the APOA5/A4/C3/A1 gene cluster polymorphisms influencing triglyceride level and risk of coronary artery disease (CAD). The purposes of this study were to fine-map triglyceride association signals in the APOA5/A4/C3/A1 gene cluster and then explore the clinical relevance in CAD and potential underlying mechanisms. We resequenced the APOA5/A4/C3/A1 gene cluster in 200 patients with extremely high triglyceride levels (≥10 mm/l) and 200 healthy control subjects who were ethnically matched and genotyped 20 genetic markers among 4,991 participants with Chinese Han ethnicity. Subsequently, 8 risk markers were investigated in 917 early-onset and 1,149 late-onset CAD patients, respectively. The molecular mechanism was explored. By resequencing, a number of newly and potentially functional variants were identified, and both the common and rare variants have remarkable cumulative effects on hypertriglyceridemia risk. Of note, gene dosage of rs2266788 demonstrated a robust association with triglyceride level (p = 1.39 × 10(-19)), modified Gensini scores (p = 1.67 × 10(-3)), and numbers of vascular lesions in CAD patients (odds ratio: 1.96, 95% confidence interval: 1.31 to 2.14, p = 8.96 × 10(-4)). Functional study demonstrated that the rs2266788 C allele destroyed microRNA 3201 binding to the 3' UTR of APOA5, resulting in prolonging the half-life of APOA5 messenger RNA and increasing its expression levels. Genetic variants in APOA5/A4/C3/A1 gene cluster play an important role in the regulation of plasma triglyceride levels by an increased APOA5 concentration and contribute to the severity of CAD. Show less
no PDF DOI: 10.1016/j.jacc.2014.03.050
APOA4
Y Zhan, Y-T Yang, H-M You +9 more · 2014 · European psychiatry : the journal of the Association of European Psychiatrists · Elsevier · added 2026-04-24
Post-stroke depression (PSD) is the most common psychiatric complication facing stroke survivors and has been associated with increased distress, physical disability, poor rehabilitation, and suicidal Show more
Post-stroke depression (PSD) is the most common psychiatric complication facing stroke survivors and has been associated with increased distress, physical disability, poor rehabilitation, and suicidal ideation. However, the pathophysiological mechanisms underlying PSD remain unknown, and no objective laboratory-based test is available to aid PSD diagnosis or monitor progression. Here, an isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomic approach was performed to identify differentially expressed proteins in plasma samples obtained from PSD, stroke, and healthy control subjects. The significantly differentiated proteins were primarily involved in lipid metabolism and immunoregulation. Six proteins associated with these processes--apolipoprotein A-IV (ApoA-IV), apolipoprotein C-II (ApoC-II), C-reactive protein (CRP), gelsolin, haptoglobin, and leucine-rich alpha-2-glycoprotein (LRG)--were selected for Western blotting validation. ApoA-IV expression was significantly upregulated in PSD as compared to stroke subjects. ApoC-II, LRG, and CRP expression were significantly downregulated in both PSD and HC subjects relative to stroke subjects. Gelsolin and haptoglobin expression were significantly dysregulated across all three groups with the following expression profiles: gelsolin, healthy control>PSD>stroke subjects; haptoglobin, stroke>PSD>healthy control. Early perturbation of lipid metabolism and immunoregulation may be involved in the pathophysiology of PSD. The combination of increased gelsolin levels accompanied by decreased haptoglobin levels shows promise as a plasma-based diagnostic biomarker panel for detecting increased PSD risk in post-stroke patients. Show less
no PDF DOI: 10.1016/j.eurpsy.2014.03.004
APOA4
Wenwen Lu, Xinhua Wan, Bin Liu +7 more · 2014 · PloS one · PLOS · added 2026-04-24
The aim of this study is to identify and validate protein change in the serum from PD patients. We used serum samples from 21 PD patients and 20 age-matched normal people as control to conduct a compa Show more
The aim of this study is to identify and validate protein change in the serum from PD patients. We used serum samples from 21 PD patients and 20 age-matched normal people as control to conduct a comparative proteomic study. We performed 2-DE and analyzed the differentially expressed protein spots by LC-MS/MS. In PD group 13 spots were shown to be differentially expressed compared to control group. They were identified as 6 proteins. Among these, 3 proteins were confirmed by Western blot analysis. It showed that the frequency of fibrinogen γ-chain (FGG) appeared 70% in PD, which could not be detected in control group. The protein of inter-alpha-trypsin inhibitor heavy chain H4 (ITI-H4) was found to exist two forms in serum. The full size (120 kDa) of the protein was increased and the fragmented ITI-H4 (35 kDa) was decreased in PD group. The ratio of full size ITI-H4 to fragmented ITI-H4 in PD patients was 3.85 ± 0.29-fold higher than in control group. Furthermore, fragmented Apo A-IV (∼ 26 kDa) was mainly detected in control group, while it was rare to be found in PD group. Above findings might be useful for diagnosis of PD. When the expressions of FGG and 120 kDa ITI-H4 are increase, as well as ∼ 26 kDa Apo A-IV disappear would provide strong evidence for PD. Show less
📄 PDF DOI: 10.1371/journal.pone.0095684
APOA4
Xiaoming Li, Min Xu, Fei Wang +7 more · 2014 · The Journal of biological chemistry · American Society for Biochemistry and Molecular Biology · added 2026-04-24
We showed recently that apoA-IV improves glucose homeostasis by enhancing pancreatic insulin secretion in the presence of elevated levels of glucose. Therefore, examined whether apolipoprotein A-IV (a Show more
We showed recently that apoA-IV improves glucose homeostasis by enhancing pancreatic insulin secretion in the presence of elevated levels of glucose. Therefore, examined whether apolipoprotein A-IV (apoA-IV) also regulates glucose metabolism through the suppression of hepatic gluconeogenesis. The ability of apoA-IV to lower gluconeogenic gene expression and glucose production was measured in apoA-IV(-/-) and wild-type mice and primary mouse hepatocytes. The transcriptional regulation of Glc-6-Pase and phosphoenolpyruvate carboxykinase (PEPCK) by apoA-IV was determined by luciferase activity assay. Using bacterial two-hybrid library screening, NR1D1 was identified as a putative apoA-IV-binding protein. The colocalization and interaction between apoA-IV and NR1D1 were confirmed by immunofluorescence, in situ proximity ligation assay, and coimmunoprecipitation. Enhanced recruitment of NR1D1 and activity by apoA-IV to Glc-6-Pase promoter was verified with ChIP and a luciferase assay. Down-regulation of apoA-IV on gluconeogenic genes is mediated through NR1D1, as illustrated in cells with NR1D1 knockdown by siRNA. We found that apoA-IV suppresses the expression of PEPCK and Glc-6-Pase in hepatocytes; decreases hepatic glucose production; binds and activates nuclear receptor NR1D1 and stimulates NR1D1 expression; in cells lacking NR1D1, fails to inhibit PEPCK and Glc-6-Pase gene expression; and stimulates higher hepatic glucose production and higher gluconeogenic gene expression in apoA-IV(-/-) mice. We conclude that apoA-IV inhibits hepatic gluconeogenesis by decreasing Glc-6-Pase and PEPCK gene expression through NR1D1. This novel regulatory pathway connects an influx of energy as fat from the gut (and subsequent apoA-IV secretion) with inhibition of hepatic glucose production. Show less
no PDF DOI: 10.1074/jbc.M113.511766
APOA4
Guangping Li, Hongfa Yang, Wenxue Li +5 more · 2014 · Bio-medical materials and engineering · added 2026-04-24
Genetic, epidemiological and clinical evidence has demonstrated the importance of the human apolipoproteinA5 (apoA5), apolipoproteinA4 (apoA4), apolipoproteinC3 (apoC3), and apolipoproteinA1 (apoA1) g Show more
Genetic, epidemiological and clinical evidence has demonstrated the importance of the human apolipoproteinA5 (apoA5), apolipoproteinA4 (apoA4), apolipoproteinC3 (apoC3), and apolipoproteinA1 (apoA1) genes in the control of the triglyceride and cholesterol concentrations in the blood. However, little is known about the mechanism by which protein kinase C (PKC) regulates the expression of these genes in hepatic and intestinal cells. The aim of this study was to explore the regulatory role of PKC on the expression of apoA5, apoA4, apoC3 and apoA1. Hepatic HepG2 and intestinal Caco-2 cells were treated with a potent PKC activator, Phorbol myristate acetate (PMA). The real time quantitative RT-PCR (qRT-PCR) technique was used to evaluate the effects of PMA on the expression of apoA1, apoA4, apoA5 and apoC3 genes. Nuclear run on assay was used to determine whether the effect of PMA on apoA4 and apoC3 was due to its ability to regulate the transcription of these genes. PMA specifically down-regulated the transcription of apoA4 and apoC3, but exhibited no effects on apoA1 or apoA5 in either HepG2 or Caco-2 cells. Further study by nuclear run on assay proved that the suppressive effect of PMA on apoA4 and apoC3 resulted from PMA's regulation of the transcription rate of the two genes. PMA down-regulated transcription of apoA4 and apoC3 possibly through the common regulatory element shared by these two genes, suggesting a suppressive role of PKC on the transcriptional regulation of specific apolipoproteins in hepatic and intestinal cells. Show less
no PDF DOI: 10.3233/BME-130880
APOA4
M H Wang, J Li, V S Y Yeung +4 more · 2014 · Meta gene · Elsevier · added 2026-04-24
Metabolic disorders including type 2 diabetes, obesity and hypertension have growing prevalence globally every year. Genome-wide association studies have successfully identified many genetic markers a Show more
Metabolic disorders including type 2 diabetes, obesity and hypertension have growing prevalence globally every year. Genome-wide association studies have successfully identified many genetic markers associated to these diseases, but few studied their interaction effects. In this study, twenty candidate SNPs from sixteen genes are selected, and a lasso-multiple regression approach is implemented to consider the SNP-SNP interactions among them in an Asian population. It is found out that the main effects of the markers are weak but the interactions among the candidates showed a significant association to diseases. SNPs from genes CDKN2BAS and KCNJ11 are significantly associated to risk for developing diabetes, and SNPs from FTO and APOA5 might interact to play an important role for the onset of hypertension. Show less
📄 PDF DOI: 10.1016/j.mgene.2014.04.010
APOA5
Shuyuan Li, Bin Hu, Yi Wang +3 more · 2014 · PloS one · PLOS · added 2026-04-24
Single nucleotide polymorphisms (SNPs) in apolipoprotein A5 (APOA5) gene are associated with triglyceride (TG) levels. However, the minor allele frequencies and linkage disequilibriums (LDs) of the SN Show more
Single nucleotide polymorphisms (SNPs) in apolipoprotein A5 (APOA5) gene are associated with triglyceride (TG) levels. However, the minor allele frequencies and linkage disequilibriums (LDs) of the SNPs in addition to their effects on TG levels vary greatly between Caucasians and East Asians. The distributions of the SNPs/haplotypes and their associations with TG levels in Uyghur population, an admixture population of Caucasians and East Asians, have not been reported to date. Here, we performed a cross-sectional study to address these. Genotyping of four SNPs in APOA5 (rs662799, rs3135506, rs2075291, and rs2266788) was performed in 1174 unrelated Uyghur subjects. SNP/haplotype and TG association analyses were conducted. The frequencies of the SNPs in Uyghurs were in between those in Caucasians and East Asians. The LD between rs662799 and rs2266788 in Uyghurs was stronger than that in East Asians but weaker than that in Caucasians, and the four SNPs resulted in four haplotypes (TGGT, CGGC, TCGT, and CGTT arranged in the order of rs662799, rs3135506, rs2075291, and rs2266788) representing 99.2% of the population. All the four SNPs were significantly associated with TG levels. Compared with non-carriers, carriers of rs662799-C, rs3135506-C, rs2075291-T, and rs2266788-C alleles had 16.0%, 15.1%, 17.1%, and 12.4% higher TG levels, respectively. When haplotype TGGT was defined as the reference, the haplotypes CGGC, TCGT, and CGTT resulted in 16.1%, 19.0%, and 19.8% higher TG levels, respectively. The proportions of variance in TG explained by APOA5 locus were 2.5%, 0.3%, 0.4%, and 1.9% for single SNP rs662799, rs3135506, rs2075291, and rs2266788, respectively, and 3.0% for the haplotypes constructed by them. The association profiles between the SNPs and haplotypes at APOA5 locus and TG levels in this admixture population differed from those in Caucasians and East Asians. The functions of these SNPs and haplotypes need to be elucidated comprehensively. Show less
📄 PDF DOI: 10.1371/journal.pone.0110258
APOA5
Weihua Shou, Ying Wang, Fang Xie +7 more · 2014 · Biochimica et biophysica acta · Elsevier · added 2026-04-24
Apolipoprotein A5 (APOA5) gene plays a key role in plasma triglyceride (TG) metabolism, and shows the involvement in coronary artery disease (CAD). A set of single nucleotide polymorphisms around the Show more
Apolipoprotein A5 (APOA5) gene plays a key role in plasma triglyceride (TG) metabolism, and shows the involvement in coronary artery disease (CAD). A set of single nucleotide polymorphisms around the APOA5 gene was identified to be associated with plasma TG levels. It is of biological and clinical importance to discern the genuine genetic determinants. A polymorphism in 3' untranslated region of the APOA5 gene, rs2266788, is deserving of investigation for suggestive clues from the association in multiple independent studies. In this study, rs2266788 was genotyped in 3222 unrelated subjects consisting of 2062 CAD cases and 1160 controls. The statistical analyses indicated that the minor C allele of rs2266788 was significantly associated with elevated plasma TG levels and higher CAD risk. In normal human liver tissues, comparison of global APOA5 mRNA levels among genotypes and allelic expression imbalance analysis showed the decreased gene expression for the C allele. Luciferase assays confirmed a concordant result that transcriptional activity was lowered for the C allele compared with the T allele in four cell lines. Multiple lines of evidence in our study supported that rs2266788 was causally associated with plasma TG levels conferring CAD risk in Han Chinese population owing to a cis-acting effect to the APOA5 gene expression. Show less
no PDF DOI: 10.1016/j.bbadis.2014.08.006
APOA5
Wei-Fen Zhu, Chun-Lin Wang, Li Liang +5 more · 2014 · Lipids in health and disease · BioMed Central · added 2026-04-24
Although the association between the apolipoprotein A5 (APOA5) genetic variants and hypertriglyceridemia has been extensively studied, there have been few studies, particularly in children and adolesc Show more
Although the association between the apolipoprotein A5 (APOA5) genetic variants and hypertriglyceridemia has been extensively studied, there have been few studies, particularly in children and adolescents, on the association between APOA5 genetic variants and obesity or non-high-density lipoprotein cholesterol (non-HDL-C) levels. The objective of this study was to examine whether APOA5 gene polymorphisms affect body mass index (BMI) or plasma non-HDL-C levels in Chinese child population. This was a case-control study. Single nucleotide polymorphisms (SNPs) were genotyped using Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry for an association study in 569 obese or overweight and 194 healthy Chinese children and adolescents. Genotype distributions for all polymorphisms in both cohorts were in accordance with the Hardy-Weinberg distribution. The frequencies of the risk alleles in rs662799 and rs651821 SNPs in APOA5 gene were all increased in obese or overweight patients compared to the controls. After adjusted for age and sex, C carriers in rs662799 had a 1.496-fold [95% confidence interval (CI): 1.074-2.084, P = 0.017] higher risk for developing obesity or overweight than subjects with TT genotype, while C carriers in rs651821 had a 1.515-fold higher risk than subjects with TT genotype (95% CI: 1.088-2.100, P = 0.014). Triglyceride (TG) and non-HDL-C concentrations were significantly different among rs662799 variants and both were higher in carriers of minor allele than in noncarriers for TG (1.64 ± 0.96 vs. 1.33 ± 0.67 mmol/L) (P < 0.001), and for non-HDL-C (3.23 ± 0.92 vs. 3.02 ± 0.80 mmol/L) (P = 0.005), respectively. There was also a trend towards increased TG and non-high-density lipoprotein cholesterol levels for rs651821 C carriers (P < 0.001 and P = 0.002, respectively). Furthermore, to confirm the independence of the associations between APOA5 gene and TG or non-HDL-C levels, multiple linear regression analysis was performed and the relationships were not eliminated by adjustment for age, sex and BMI. These findings suggest the TG-raising genetic variants in the APOA5 gene may influence the susceptibility of the individual to obesity, which may also contribute to an increased risk of high non-HDL-C levels in Chinese obese children and adolescents. Show less
📄 PDF DOI: 10.1186/1476-511X-13-93
APOA5
Yan-Wei Yin, Qian-Qian Sun, Pei-Jian Wang +5 more · 2014 · PloS one · PLOS · added 2026-04-24
Several studies have investigated whether the polymorphism in the apolipoprotein A5 (APOA5) is associated with type 2 diabetes mellitus (T2DM) risk. However, those studies have produced inconsistent r Show more
Several studies have investigated whether the polymorphism in the apolipoprotein A5 (APOA5) is associated with type 2 diabetes mellitus (T2DM) risk. However, those studies have produced inconsistent results. The purpose of this study was to investigate whether the APOA5 -1131T/C polymorphism (rs662799) confers significant susceptibility to T2DM using a meta-analysis. PubMed, Embase, Web of Science, Cochrane database, CBMdisc, CNKI and Google Scholar were searched to get the genetic association studies. All statistical analyses were done with Stata 11.0. A total of 19 studies included 4,767 T2DM cases and 10,370 controls (four studies involving 555 T2DM cases and 2958 controls were performed among Europeans and 15 studies involving 4212 T2DM cases and 7412 controls were performed among Asians) were combined showing significant association between the APOA5 -1131T/C polymorphism and T2DM risk (for C allele vs. T allele: OR = 1.28, 95% CI = 1.17-1.40, p<0.00001; for C/C vs. T/T: OR = 1.57, 95% CI = 1.35-1.83, p<0.00001; for C/C vs. T/C+T/T: OR = 1.36, 95% CI = 1.18-1.57, p<0.0001; for C/C+T/C vs. T/T: OR = 1.32, 95% CI = 1.16-1.51, p<0.0001). In the subgroup analysis by ethnicity, significant association was also found among Asians (for C allele vs. T allele: OR = 1.31, 95% CI = 1.22-1.40, p<0.00001; for C/C vs. T/T: OR = 1.61, 95% CI = 1.38-1.88, p<0.00001; for C/C vs. T/C+T/T: OR = 1.39, 95% CI = 1.20-1.61, p<0.0001; for C/C+T/C vs. T/T: OR = 1.42, 95% CI = 1.25-1.62, p<0.00001). However, no significant association was found between the APOA5 -1131T/C polymorphism and T2DM risk among Europeans. The present meta-analysis suggests that the APOA5 -1131T/C polymorphism is associated with an increased T2DM risk in Asian population. Show less
📄 PDF DOI: 10.1371/journal.pone.0089167
APOA5
Liang Tang, Zhi-Peng Cheng, Qing-Yun Wang +5 more · 2014 · F1000Research · added 2026-04-24
The genetic background of ischemic vascular disease is actively being explored. Several studies have shown that inhibition of APOC3 significantly reduces plasma levels of apolipoprotein C3 and triglyc Show more
The genetic background of ischemic vascular disease is actively being explored. Several studies have shown that inhibition of APOC3 significantly reduces plasma levels of apolipoprotein C3 and triglycerides. Recently, the TG and HDL Working Group and Jørgensen et al. reported that loss-of-function mutations in APOC3 are associated with decreased triglyceride levels and a reduced risk of ischemic vascular disease in European and African individuals. We performed a replication study in 4470 Chinese participants. The coding regions of APOC3 were amplified and re-sequenced. However, only synonymous and intronic variants with no functional consequences were identified. None of the loss-of-function mutations reported in European and African individuals were observed. Therefore, APOC3 may not be an ideal predictor for risk of ischemic vascular disease in the Chinese population. Show less
📄 PDF DOI: 10.12688/f1000research.5676.2
APOC3
Tonghong Niu, Man Jiang, Haogang Liu +7 more · 2014 · Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · added 2026-04-24
To investigate the association between two polymorphisms of the APOC3 gene (T-455C and C-482T) and hereditary risk of non-alcoholic fatty liver disease (NAFLD). A total of 287 patients with NAFLD and Show more
To investigate the association between two polymorphisms of the APOC3 gene (T-455C and C-482T) and hereditary risk of non-alcoholic fatty liver disease (NAFLD). A total of 287 patients with NAFLD and 310 control subjects were genotyped by PCR and direct sequencing. Serum lipid profiles were also detected by standard biochemical One-hundred-and-eighty of the study participants were used to measure the APOC3 content by enzyme-linked immunosorbent assay. Inter-group differences and associations were assessed statistically using Chi square and t tests and logistic and linear regression analyses. The frequencies of neither the genotypes or alleles were significantly different between the NAFLD cases and the controls. Compared with the most common genotypes-455TT or-482CC, none of the variants showed a significant increase in risk of NAFLD or for the clinical and biochemical parameters. The adjusted odds ratios (with 95% confidence intervals) of NAFLD were 1.25 (0.79-1.96) and 1.20 (0.76-1.89) for carriers of the APOC3-455C and-482 T variants respectively (P more than 0.05). The T-455C and C-482T polymorphisms of the APOC3 gene are not associated with risk of NAFLD, pathogenic changes in lipid profiles, or insulin resistance in Han Chinese. Show less
no PDF DOI: 10.3760/cma.j.issn.1007-3418.2014.05.011
APOC3
Bin Lin, Yiwei Huang, Mingying Zhang +2 more · 2014 · BMJ open · added 2026-04-24
Apolipoprotein C3 (ApoC3) polymorphisms have been suggested to be associated with risk of coronary heart disease (CHD). However, the results of relevant studies were inconsistent. We aimed to systemat Show more
Apolipoprotein C3 (ApoC3) polymorphisms have been suggested to be associated with risk of coronary heart disease (CHD). However, the results of relevant studies were inconsistent. We aimed to systematically evaluate this issue. PubMed, EMBASE and Cochrane library databases (up to March 2013) were systematically searched to identify studies evaluating the association between ApoC3 polymorphisms and CHD risk. Two reviewers independently identified studies, extracted and analysed the data. Either a fixed-effects or a random-effects model was adopted to estimate overall ORs. Finally, 20 studies comprising 15 591 participants were included in this systematic review. Fifteen studies with 11 539 individuals were included in the meta-analysis of Sst I polymorphism, four studies comprising 3378 individuals assessed T-455C polymorphism, four studies with 3070 participants evaluated C-482T polymorphism and C1100T polymorphism was assessed by three studies comprising 4662 participants. Under dominant model, Sst I polymorphism was borderline significantly associated with CHD risk (S1S2+S2S2 vs S1S1, pooled OR=1.19, 95% CI 1.00 to 1.42). Subgroup analyses suggested that Sst I polymorphism was significantly associated with myocardial infarction (MI) risk (pooled OR=1.42, 95% CI 1.06 to 1.91), and Sst I polymorphism was statistically associated with CHD risk among Asian population (pooled OR=1.35, 95% CI 1.08 to 1.69) and in retrospective studies (pooled OR=1.30, 95% CI 1.04 to 1.61). A significant association was observed between T-455C polymorphism and CHD risk (TC+CC vs TT, pooled OR=1.22, 95% CI 1.06 to 1.42). A borderline significant association was suggested between T-455C polymorphism and MI risk (pooled OR=1.21, 95% CI 1.00 to 1.46). C-482T and C1100T polymorphisms were not indicated to be associated with CHD risk or MI risk. ApoC3 Sst I and T-455C polymorphisms might be associated with CHD risk. Show less
📄 PDF DOI: 10.1136/bmjopen-2013-004156
APOC3
Yan Pu, Peng Chen, Bin Zhou +5 more · 2014 · Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals · added 2026-04-24
AXIN1 is a central component of Wnt signalling pathway which is essential for embryonic development. To investigate whether polymorphisms of AXIN1 contribute to ASD susceptibility. Three tag SNPs (rs1 Show more
AXIN1 is a central component of Wnt signalling pathway which is essential for embryonic development. To investigate whether polymorphisms of AXIN1 contribute to ASD susceptibility. Three tag SNPs (rs12921862, rs370681 and rs1805105) in AXIN1 were genotyped in 208 ASD patients and 302 healthy controls using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in a Chinese population. Significantly increased ASD risk was observed to be associated with the A allele of rs12921862 (p < 0.0001, OR = 3.096, 95% CI = 2.037-4.717). Increased ASD risk was observed to be associated with rs370681 in a codominant (p = 0.043, OR = 1.52, 95% CI = 1.04-2.22) and overdominant model (p = 0.016, OR = 1.57, 95% CI = 1.08-2.27). rs12921862 and rs370681 may contribute to ASD susceptibility. Show less
no PDF DOI: 10.3109/1354750X.2014.978895
AXIN1
Xinguo Zhu, Zhilong Huang, Yan Chen +11 more · 2014 · Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · Elsevier · added 2026-04-24
Apoptosis constitutes a system for the removal of aged, or damaged cells, which is regulated by the interplay of pro-apoptotic and antiapoptotic proteins. Previous study has shown that Juvenile Batten Show more
Apoptosis constitutes a system for the removal of aged, or damaged cells, which is regulated by the interplay of pro-apoptotic and antiapoptotic proteins. Previous study has shown that Juvenile Batten disease protein, CLN3, is antiapoptotic gene in NT2 neuronal precursor cells and a few types of cancers. However, in colorectal cancer, whether CLN3 also play its antiapoptotic role and the effect of targeted controlling CLN3 on the biological behavior of human colorectal cancer cell is unknown. We employed the sequence-specific siRNA silencing the CLN3 gene and investigated its effects on growth and apoptosis of colorectal cancer HCT116 cells, which has highest elevation of CLN3 expression among four colorectal cancer cell lines. After CLN3 specific siRNA transfection, mRNA and protein expression levels of CLN3 in HCT116 cells were noticeably decreased. Moreover, CLN3-siRNA inhibited the proliferation of colorectal cancer cells, promoted their apoptosis and induced G0/G1 cell cycle arrest. Our current study demonstrated that CLN3 was expressed in colorectal cancer cells at a high frequency. Moreover, CLN3 down-regulation with RNA interference can inhibit proliferation, apoptosis, and cell cycle progression of colorectal cancer cells. Our study represented a potential new approach to understanding the role of CLN3 in cancer and provides a potential novel strategy colorectal cancer therapy. Show less
no PDF DOI: 10.1016/j.biopha.2013.12.010
CLN3
Feng Wang, Hui Wang, Han-Fang Tuan +37 more · 2014 · Human genetics · Springer · added 2026-04-24
Retinitis pigmentosa (RP) is a devastating form of retinal degeneration, with significant social and professional consequences. Molecular genetic information is invaluable for an accurate clinical dia Show more
Retinitis pigmentosa (RP) is a devastating form of retinal degeneration, with significant social and professional consequences. Molecular genetic information is invaluable for an accurate clinical diagnosis of RP due to its high genetic and clinical heterogeneity. Using a gene capture panel that covers 163 of the currently known retinal disease genes, including 48 RP genes, we performed a comprehensive molecular screening in a collection of 123 RP unsettled probands from a wide variety of ethnic backgrounds, including 113 unrelated simplex and 10 autosomal recessive RP (arRP) cases. As a result, 61 mutations were identified in 45 probands, including 38 novel pathogenic alleles. Interestingly, we observed that phenotype and genotype were not in full agreement in 21 probands. Among them, eight probands were clinically reassessed, resulting in refinement of clinical diagnoses for six of these patients. Finally, recessive mutations in CLN3 were identified in five retinal degeneration patients, including four RP probands and one cone-rod dystrophy patient, suggesting that CLN3 is a novel non-syndromic retinal disease gene. Collectively, our results underscore that, due to the high molecular and clinical heterogeneity of RP, comprehensive screening of all retinal disease genes is effective in identifying novel pathogenic mutations and provides an opportunity to discover new genotype-phenotype correlations. Information gained from this genetic screening will directly aid in patient diagnosis, prognosis, and treatment, as well as allowing appropriate family planning and counseling. Show less
📄 PDF DOI: 10.1007/s00439-013-1381-5
CLN3
Stephen R Williams, Qiong Yang, Fang Chen +20 more · 2014 · PLoS genetics · PLOS · added 2026-04-24
Circulating homocysteine levels (tHcy), a product of the folate one carbon metabolism pathway (FOCM) through the demethylation of methionine, are heritable and are associated with an increased risk of Show more
Circulating homocysteine levels (tHcy), a product of the folate one carbon metabolism pathway (FOCM) through the demethylation of methionine, are heritable and are associated with an increased risk of common diseases such as stroke, cardiovascular disease (CVD), cancer and dementia. The FOCM is the sole source of de novo methyl group synthesis, impacting many biological and epigenetic pathways. However, the genetic determinants of elevated tHcy (hyperhomocysteinemia), dysregulation of methionine metabolism and the underlying biological processes remain unclear. We conducted independent genome-wide association studies and a meta-analysis of methionine metabolism, characterized by post-methionine load test tHcy, in 2,710 participants from the Framingham Heart Study (FHS) and 2,100 participants from the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, and then examined the association of the identified loci with incident stroke in FHS. Five genes in the FOCM pathway (GNMT [p = 1.60 × 10(-63)], CBS [p = 3.15 × 10(-26)], CPS1 [p = 9.10 × 10(-13)], ALDH1L1 [p = 7.3 × 10(-13)] and PSPH [p = 1.17 × 10(-16)]) were strongly associated with the difference between pre- and post-methionine load test tHcy levels (ΔPOST). Of these, one variant in the ALDH1L1 locus, rs2364368, was associated with incident ischemic stroke. Promoter analyses reveal genetic and epigenetic differences that may explain a direct effect on GNMT transcription and a downstream affect on methionine metabolism. Additionally, a genetic-score consisting of the five significant loci explains 13% of the variance of ΔPOST in FHS and 6% of the variance in VISP. Association between variants in FOCM genes with ΔPOST suggest novel mechanisms that lead to differences in methionine metabolism, and possibly the epigenome, impacting disease risk. These data emphasize the importance of a concerted effort to understand regulators of one carbon metabolism as potential therapeutic targets. Show less
📄 PDF DOI: 10.1371/journal.pgen.1004214
CPS1
Ruiyong Jing, Junjie Liu, Zhenhua Yu +2 more · 2014 · PloS one · PLOS · added 2026-04-24
Numerous studies have revealed the high diversity of cyanophages in marine and freshwater environments, but little is currently known about the diversity of cyanophages in paddy fields, particularly i Show more
Numerous studies have revealed the high diversity of cyanophages in marine and freshwater environments, but little is currently known about the diversity of cyanophages in paddy fields, particularly in Northeast (NE) China. To elucidate the genetic diversity of cyanophages in paddy floodwaters in NE China, viral capsid assembly protein gene (g20) sequences from five floodwater samples were amplified with the primers CPS1 and CPS8. Denaturing gradient gel electrophoresis (DGGE) was applied to distinguish different g20 clones. In total, 54 clones differing in g20 nucleotide sequences were obtained in this study. Phylogenetic analysis showed that the distribution of g20 sequences in this study was different from that in Japanese paddy fields, and all the sequences were grouped into Clusters α, β, γ and ε. Within Clusters α and β, three new small clusters (PFW-VII∼-IX) were identified. UniFrac analysis of g20 clone assemblages demonstrated that the community compositions of cyanophage varied among marine, lake and paddy field environments. In paddy floodwater, community compositions of cyanophage were also different between NE China and Japan. Show less
📄 PDF DOI: 10.1371/journal.pone.0088634
CPS1
Zhiyuan Hu, Christopher Lausted, Hyuntae Yoo +6 more · 2014 · Theranostics · added 2026-04-24
We discuss here a new approach to detecting hepatotoxicity by employing concentration changes of liver-specific blood proteins during disease progression. These proteins are capable of assessing the b Show more
We discuss here a new approach to detecting hepatotoxicity by employing concentration changes of liver-specific blood proteins during disease progression. These proteins are capable of assessing the behaviors of their cognate liver biological networks for toxicity or disease perturbations. Blood biomarkers are highly desirable diagnostics as blood is easily accessible and baths virtually all organs. Fifteen liver-specific blood proteins were identified as markers of acetaminophen (APAP)-induced hepatotoxicity using three proteomic technologies: label-free antibody microarrays, quantitative immunoblotting, and targeted iTRAQ mass spectrometry. Liver-specific blood proteins produced a toxicity signature of eleven elevated and four attenuated blood protein levels. These blood protein perturbations begin to provide a systems view of key mechanistic features of APAP-induced liver injury relating to glutathione and S-adenosyl-L-methionine (SAMe) depletion, mitochondrial dysfunction, and liver responses to the stress. Two markers, elevated membrane-bound catechol-O-methyltransferase (MB-COMT) and attenuated retinol binding protein 4 (RBP4), report hepatic injury significantly earlier than the current gold standard liver biomarker, alanine transaminase (ALT). These biomarkers were perturbed prior to onset of irreversible liver injury. Ideal markers should be applicable for both rodent model studies and human clinical trials. Five of these mouse liver-specific blood markers had human orthologs that were also found to be responsive to human hepatotoxicity. This panel of liver-specific proteins has the potential to effectively identify the early toxicity onset, the nature and extent of liver injury and report on some of the APAP-perturbed liver networks. Show less
📄 PDF DOI: 10.7150/thno.7868
CPS1
Peter J McGuire, Tatiana N Tarasenko, Tony Wang +6 more · 2014 · Disease models & mechanisms · added 2026-04-24
The urea cycle functions to incorporate ammonia, generated by normal metabolism, into urea. Urea cycle disorders (UCDs) are caused by loss of function in any of the enzymes responsible for ureagenesis Show more
The urea cycle functions to incorporate ammonia, generated by normal metabolism, into urea. Urea cycle disorders (UCDs) are caused by loss of function in any of the enzymes responsible for ureagenesis, and are characterized by life-threatening episodes of acute metabolic decompensation with hyperammonemia (HA). A prospective analysis of interim HA events in a cohort of individuals with ornithine transcarbamylase (OTC) deficiency, the most common UCD, revealed that intercurrent infection was the most common precipitant of acute HA and was associated with markers of increased morbidity when compared with other precipitants. To further understand these clinical observations, we developed a model system of metabolic decompensation with HA triggered by viral infection (PR8 influenza) using spf-ash mice, a model of OTC deficiency. Both wild-type (WT) and spf-ash mice displayed similar cytokine profiles and lung viral titers in response to PR8 influenza infection. During infection, spf-ash mice displayed an increase in liver transaminases, suggesting a hepatic sensitivity to the inflammatory response and an altered hepatic immune response. Despite having no visible pathological changes by histology, WT and spf-ash mice had reduced CPS1 and OTC enzyme activities, and, unlike WT, spf-ash mice failed to increase ureagenesis. Depression of urea cycle function was seen in liver amino acid analysis, with reductions seen in aspartate, ornithine and arginine during infection. In conclusion, we developed a model system of acute metabolic decompensation due to infection in a mouse model of a UCD. In addition, we have identified metabolic perturbations during infection in the spf-ash mice, including a reduction of urea cycle intermediates. This model of acute metabolic decompensation with HA due to infection in UCD serves as a platform for exploring biochemical perturbations and the efficacy of treatments, and could be adapted to explore acute decompensation in other types of inborn errors of metabolism. Show less
📄 PDF DOI: 10.1242/dmm.013003
CPS1
Chunxia Song, Fangjun Wang, Kai Cheng +7 more · 2014 · Journal of proteome research · ACS Publications · added 2026-04-24
Global quantification of the single amino-acid variations (SAAVs) is essential to investigate the roles of SAAVs in disease progression. However, few efforts have been made on this issue due to the la Show more
Global quantification of the single amino-acid variations (SAAVs) is essential to investigate the roles of SAAVs in disease progression. However, few efforts have been made on this issue due to the lack of high -throughput approach. Here we presented a strategy by integration of the stable isotope dimethyl labeling with variation-associated database search to globally quantify the SAAVs at the first time. A protein database containing 87,745 amino acid variant sequences and 73,910 UniProtKB/Swiss-Prot canonical protein entries was constructed for database search, and higher energy collisional dissociation combined with collision-induced dissociation fragmentation modes were applied to improve the quantification coverage of SAAVs. Compared with target proteomics in which only a few sites could be quantified, as many as 282 unique SAAVs sites were quantified between hepatocellular carcinoma (HCC) and normal human liver tissues by our strategy. The variation rates in different samples were evaluated, and some interesting SAAVs with significant increase normalized quantification ratios, such as T1406N in CPS1 and S197R in HTATIP2, were observed to highly associate with HCC progression. Therefore, the newly developed strategy enables the large-scale comparative analysis of variations at the protein level and holds a promising future in the research related to variations. Show less
no PDF DOI: 10.1021/pr400544j
CPS1
Xiang Chen, Armita Bahrami, Alberto Pappo +29 more · 2014 · Cell reports · Elsevier · added 2026-04-24
Pediatric osteosarcoma is characterized by multiple somatic chromosomal lesions, including structural variations (SVs) and copy number alterations (CNAs). To define the landscape of somatic mutations Show more
Pediatric osteosarcoma is characterized by multiple somatic chromosomal lesions, including structural variations (SVs) and copy number alterations (CNAs). To define the landscape of somatic mutations in pediatric osteosarcoma, we performed whole-genome sequencing of DNA from 20 osteosarcoma tumor samples and matched normal tissue in a discovery cohort, as well as 14 samples in a validation cohort. Single-nucleotide variations (SNVs) exhibited a pattern of localized hypermutation called kataegis in 50% of the tumors. We identified p53 pathway lesions in all tumors in the discovery cohort, nine of which were translocations in the first intron of the TP53 gene. Beyond TP53, the RB1, ATRX, and DLG2 genes showed recurrent somatic alterations in 29%-53% of the tumors. These data highlight the power of whole-genome sequencing for identifying recurrent somatic alterations in cancer genomes that may be missed using other methods. Show less
📄 PDF DOI: 10.1016/j.celrep.2014.03.003
DLG2
Ge Gao, Jan L Kasperbauer, Nicole M Tombers +3 more · 2014 · Genes, chromosomes & cancer · Wiley · added 2026-04-24
The common fragile sites (CFSs) are large regions of profound genomic instability found in all individuals. The frequent deletions and other alterations in these regions in multiple cancers has led to Show more
The common fragile sites (CFSs) are large regions of profound genomic instability found in all individuals. The frequent deletions and other alterations in these regions in multiple cancers has led to the discovery of a number of extremely large genes contained within these regions and several of the large CFS genes have already been demonstrated to function as tumor suppressors involved in the formation of many different cancers. To study the large CFS genes in oropharyngeal squamous cell carcinoma (OPSCC), we did RNA seq analysis from 11 head and neck cancer patients. This revealed that there are six large CFS genes which consistently had decreased expression in the tumor samples compared to their matched normal tissues. These six genes are PARK2, DLG2, NBEA, CTNNA3, DMD, and FHIT. PARK2 and FHIT are located within the two most frequently expressed CFSs and both have been demonstrated to function as tumor suppressors, while the other large genes are found to have frequent alterations in multiple cancers. Validation experiments using real time PCR indicated that over 60% of OPSCC tumors showed decreased expression for all six genes. Both HPV-positive and HPV-negative OPSCCs had similar proportions with loss of expression of these genes. Our results suggest that this selected group of large genes might serve as potential tumor suppressors involved in the development of OPSCCs. Further studies are needed to investigate whether the decreased expression observed is due to genomic instability within the CFS regions or the selection for alterations of specific large CFS genes. Show less
no PDF DOI: 10.1002/gcc.22150
DLG2
Yilin Tai, Justyna A Janas, Chia-Lin Wang +1 more · 2014 · Cell reports · Elsevier · added 2026-04-24
Chandelier cells (ChCs), typified by their unique axonal morphology, are the most distinct interneurons present in cortical circuits. Via their distinctive axonal terminals, called cartridges, these c Show more
Chandelier cells (ChCs), typified by their unique axonal morphology, are the most distinct interneurons present in cortical circuits. Via their distinctive axonal terminals, called cartridges, these cells selectively target the axon initial segment of pyramidal cells and control action potential initiation; however, the mechanisms that govern the characteristic ChC axonal structure have remained elusive. Here, by employing an in utero electroporation-based method that enables genetic labeling and manipulation of ChCs in vivo, we identify DOCK7, a member of the DOCK180 family, as a molecule essential for ChC cartridge and bouton development. Furthermore, we present evidence that DOCK7 functions as a cytoplasmic activator of the schizophrenia-associated ErbB4 receptor tyrosine kinase and that DOCK7 modulates ErbB4 activity to control ChC cartridge and bouton development. Thus, our findings define DOCK7 and ErbB4 as key components of a pathway that controls the morphological differentiation of ChCs, with implications for the pathogenesis of schizophrenia. Show less
📄 PDF DOI: 10.1016/j.celrep.2013.12.034
DOCK7
Yong Wang, Bing Zhou, Hongbin Fan +3 more · 2014 · Zhonghua zhong liu za zhi [Chinese journal of oncology] · added 2026-04-24
To compare the transcriptome of esophageal cancer cells (EC9706), human mesenchymal stem cells (MSCs), and after fusion of esophageal cancer cells with MSCs, and to further study their different expre Show more
To compare the transcriptome of esophageal cancer cells (EC9706), human mesenchymal stem cells (MSCs), and after fusion of esophageal cancer cells with MSCs, and to further study their different expression profiles and the changes of their signaling pathways. We examined the gene expression profiles of these cells with transcriptome microarray using LIMMA package and several web-based applications, such as DAVID, ToppGene and MSigDB. The resulting sets of differentially expressed genes (DEGs) were comprehensively analyzed to identify the pathways and their changes after the cell fusion. A total of 4 548 significantly DEGs among the three cell lines were found by LIMMA. Three functional annotation web tools predicted that DNA damage repair, cell cycle arrest and apoptosis pathways were enriched. Total DEGs were mapped to the canonic pathways with KEGGanim which depicted that the core genes of DNA damage repair, cell cycle arrest and pro-apoptosis were up-regulated in fusion cells, and they mightbe combined to respond the fusion-induced damage stress. The up-regulation of suppressive factor DUSP6 might feedback inhibit the MAPK signaling pathway in the fusion cells, too. Transcriptome analysis suggests that hMSCs and EC9706 cell fusion may inhibit growth of EC cells by induction of pro-apoptotic signaling and DUSP6 negative feedback inhibition mechanism. In addition, the changes of immune regulation-related and differentiation-related genes indicate that the fusion cells inherited certain immune-suppressive function from the stem cells. Show less
no PDF
DUSP6
Weihan Yang, Yu Wang, Qiang Pu +6 more · 2014 · Molecular medicine reports · added 2026-04-24
Abnormal expression of solute carrier family 34 (sodium phosphate), member 2 (SLC34A2) in the lung may induce abnormal alveolar type II (AT II) cells to transform into lung adenocarcinoma cells, and m Show more
Abnormal expression of solute carrier family 34 (sodium phosphate), member 2 (SLC34A2) in the lung may induce abnormal alveolar type II (AT II) cells to transform into lung adenocarcinoma cells, and may also be important in biological process of lung adenocarcinoma. However, at present, the effects and molecular mechanisms of SLC34A2 in the initiation and progression of lung cancer remain to be elucidated. To the best of our knowledge, the present study revealed for the first time that the expression levels of SLC34A2 were downregulated in the A549 and H1299 lung adenocarcinoma cell lines. Further investigation demonstrated that the elevated expression of SLC34A2 in A549 cells was able to significantly inhibit cell viability and invasion in vitro. In addition, 10 upregulated genes between the A549‑P‑S cell line stably expressing SLC34A2 and the control cell line A549‑P were identified by microarray analysis and quantitative polymerase chain reaction, including seven tumor suppressor genes and three complement genes. Furthermore, the upregulation of complement gene C3 and complement 4B preproprotein (C4b) in A549‑P‑S cells was confirmed by ELISA analysis and was identified to be correlated with recovering Pi absorption in A549 cells by the phosphomolybdic acid method by enhancing the expression of SLC34A2. Therefore, it was hypothesized that the mechanisms underlying the effect of SLC34A2 on A549 cells might be associated with the activation of the complement alternative pathway (C3 and C4b) and upregulation of the expression of selenium binding protein 1, thioredoxin‑interacting protein, PDZK1‑interacting protein 1 and dual specificity protein phosphatase 6. Downregulation of SLC34A2 may primarily cause abnormal AT II cells to escape from complement‑associated immunosurveillance and abnormally express certain tumor‑suppressor genes inducing AT II cells to develop into lung adenocarcinoma. The present study further elucidated the effects and mechanisms of SLC34A2 in the generation and development of lung cancer. Show less
📄 PDF DOI: 10.3892/mmr.2014.2376
DUSP6