👤 Shi-guo Liu

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3182
Articles
1983
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Also published as: A Liu, Ai Liu, Ai-Guo Liu, Aidong Liu, Aiguo Liu, Aihua Liu, Aijun Liu, Ailing Liu, Aimin Liu, Allen P Liu, Aman Liu, An Liu, An-Qi Liu, Ang-Jun Liu, Anjing Liu, Anjun Liu, Ankang Liu, Anling Liu, Anmin Liu, Annuo Liu, Anshu Liu, Ao Liu, Aoxing Liu, B Liu, Baihui Liu, Baixue Liu, Baiyan Liu, Ban Liu, Bang Liu, Bang-Quan Liu, Bao Liu, Bao-Cheng Liu, Baogang Liu, Baohui Liu, Baolan Liu, Baoli Liu, Baoning Liu, Baoxin Liu, Baoyi Liu, Bei Liu, Beibei Liu, Ben Liu, Bi-Cheng Liu, Bi-Feng Liu, Bihao Liu, Bilin Liu, Bin Liu, Bing Liu, Bing-Wen Liu, Bingcheng Liu, Bingjie Liu, Bingwen Liu, Bingxiao Liu, Bingya Liu, Bingyu Liu, Binjie Liu, Bo Liu, Bo-Gong Liu, Bo-Han Liu, Boao Liu, Bolin Liu, Boling Liu, Boqun Liu, Bowen Liu, Boxiang Liu, Boxin Liu, Boya Liu, Boyang Liu, Brian Y Liu, C Liu, C M Liu, C Q Liu, C-T Liu, C-Y Liu, Caihong Liu, Cailing Liu, Caiyan Liu, Can Liu, Can-Zhao Liu, Catherine H Liu, Chan Liu, Chang Liu, Chang-Bin Liu, Chang-Hai Liu, Chang-Ming Liu, Chang-Pan Liu, Chang-Peng Liu, Changbin Liu, Changjiang Liu, Changliang Liu, Changming Liu, Changqing Liu, Changtie Liu, Changya Liu, Changyun Liu, Chao Liu, Chao-Ming Liu, Chaohong Liu, Chaoqi Liu, Chaoyi Liu, Chelsea Liu, Chen Liu, Chenchen Liu, Chendong Liu, Cheng Liu, Cheng-Li Liu, Cheng-Wu Liu, Cheng-Yong Liu, Cheng-Yun Liu, Chengbo Liu, Chenge Liu, Chengguo Liu, Chenghui Liu, Chengkun Liu, Chenglong Liu, Chengxiang Liu, Chengyao Liu, Chengyun Liu, Chenmiao Liu, Chenming Liu, Chenshu Liu, Chenxing Liu, Chenxu Liu, Chenxuan Liu, Chi Liu, Chia-Chen Liu, Chia-Hung Liu, Chia-Jen Liu, Chia-Yang Liu, Chia-Yu Liu, Chiang Liu, Chin-Chih Liu, Chin-Ching Liu, Chin-San Liu, Ching-Hsuan Liu, Ching-Ti Liu, Chong Liu, Christine S Liu, ChuHao Liu, Chuan Liu, Chuanfeng Liu, Chuanxin Liu, Chuanyang Liu, Chun Liu, Chun-Chi Liu, Chun-Feng Liu, Chun-Lei Liu, Chun-Ming Liu, Chun-Xiao Liu, Chun-Yu Liu, Chunchi Liu, Chundong Liu, Chunfeng Liu, Chung-Cheng Liu, Chung-Ji Liu, Chunhua Liu, Chunlei Liu, Chunliang Liu, Chunling Liu, Chunming Liu, Chunpeng Liu, Chunping Liu, Chunsheng Liu, Chunwei Liu, Chunxiao Liu, Chunyan Liu, Chunying Liu, Chunyu Liu, Cici Liu, Clarissa M Liu, Cong Cong Liu, Cong Liu, Congcong Liu, Cui Liu, Cui-Cui Liu, Cuicui Liu, Cuijie Liu, Cuilan Liu, Cun Liu, Cun-Fei Liu, D Liu, Da Liu, Da-Ren Liu, Daiyun Liu, Dajiang J Liu, Dan Liu, Dan-Ning Liu, Dandan Liu, Danhui Liu, Danping Liu, Dantong Liu, Danyang Liu, Danyong Liu, Daoshen Liu, David Liu, David R Liu, Dawei Liu, Daxu Liu, Dayong Liu, Dazhi Liu, De-Pei Liu, De-Shun Liu, Dechao Liu, Dehui Liu, Deliang Liu, Deng-Xiang Liu, Depei Liu, Deping Liu, Derek Liu, Deruo Liu, Desheng Liu, Dewu Liu, Dexi Liu, Deyao Liu, Deying Liu, Dezhen Liu, Di Liu, Didi Liu, Ding-Ming Liu, Dingding Liu, Dinglu Liu, Dingxiang Liu, Dong Liu, Dong-Yun Liu, Dongang Liu, Dongbo Liu, Dongfang Liu, Donghui Liu, Dongjuan Liu, Dongliang Liu, Dongmei Liu, Dongming Liu, Dongping Liu, Dongxian Liu, Dongxue Liu, Dongyan Liu, Dongyang Liu, Dongyao Liu, Dongzhou Liu, Dudu Liu, Dunjiang Liu, Edison Tak-Bun Liu, En-Qi Liu, Enbin Liu, Enlong Liu, Enqi Liu, Erdong Liu, Erfeng Liu, Erxiong Liu, F Liu, F Z Liu, Fan Liu, Fan-Jie Liu, Fang Liu, Fang-Zhou Liu, Fangli Liu, Fangmei Liu, Fangping Liu, Fangqi Liu, Fangzhou Liu, Fani Liu, Fayu Liu, Fei Liu, Feifan Liu, Feilong Liu, Feiyan Liu, Feiyang Liu, Feiye Liu, Fen Liu, Fendou Liu, Feng Liu, Feng-Ying Liu, Fengbin Liu, Fengchao Liu, Fengen Liu, Fengguo Liu, Fengjiao Liu, Fengjie Liu, Fengjuan Liu, Fengqiong Liu, Fengsong Liu, Fonda Liu, Foqiu Liu, Fu-Jun Liu, Fu-Tong Liu, Fubao Liu, Fuhao Liu, Fuhong Liu, Fujun Liu, Gan Liu, Gang Liu, Gangli Liu, Ganqiang Liu, Gaohua Liu, Ge Liu, Ge-Li Liu, Gen Sheng Liu, Geng Liu, Geng-Hao Liu, Geoffrey Liu, George E Liu, George Liu, Geroge Liu, Gexiu Liu, Gongguan Liu, Guang Liu, Guangbin Liu, Guangfan Liu, Guanghao Liu, Guangliang Liu, Guangqin Liu, Guangwei Liu, Guangxu Liu, Guannan Liu, Guantong Liu, Gui Yao Liu, Gui-Fen Liu, Gui-Jing Liu, Gui-Rong Liu, Guibo Liu, Guidong Liu, Guihong Liu, Guiju Liu, Guili Liu, Guiqiong Liu, Guiquan Liu, Guisheng Liu, Guiyou Liu, Guiyuan Liu, Guning Liu, Guo-Liang Liu, Guochang Liu, Guodong Liu, Guohao Liu, Guojun Liu, Guoke Liu, Guoliang Liu, Guopin Liu, Guoqiang Liu, Guoqing Liu, Guoquan Liu, Guowen Liu, Guoyong Liu, H Liu, Hai Feng Liu, Hai-Jing Liu, Hai-Xia Liu, Hai-Yan Liu, Haibin Liu, Haichao Liu, Haifei Liu, Haifeng Liu, Hailan Liu, Hailin Liu, Hailing Liu, Haitao Liu, Haiyan Liu, Haiyang Liu, Haiying Liu, Haizhao Liu, Han Liu, Han-Fu Liu, Han-Qi Liu, Hancong Liu, Hang Liu, Hanhan Liu, Hanjiao Liu, Hanjie Liu, Hanmin Liu, Hanqing Liu, Hanxiang Liu, Hanyuan Liu, Hao Liu, Haobin Liu, Haodong Liu, Haogang Liu, Haojie Liu, Haokun Liu, Haoling Liu, Haowei Liu, Haowen Liu, Haoyue Liu, He-Kun Liu, Hehe Liu, Hekun Liu, Heliang Liu, Heng Liu, Hengan Liu, Hengru Liu, Hengtong Liu, Heyi Liu, Hong Juan Liu, Hong Liu, Hong Wei Liu, Hong-Bin Liu, Hong-Li Liu, Hong-Liang Liu, Hong-Tao Liu, Hong-Xiang Liu, Hong-Ying Liu, Hongbin Liu, Hongbing Liu, Hongfa Liu, Honghan Liu, Honghe Liu, Hongjian Liu, Hongjie Liu, Hongjun Liu, Hongli Liu, Hongliang Liu, Hongmei Liu, Hongqun Liu, Hongtao Liu, Hongwei Liu, Hongxiang Liu, Hongxing Liu, Hongyan Liu, Hongyang Liu, Hongyao Liu, Hongyu Liu, Hongyuan Liu, Houbao Liu, Hsiao-Ching Liu, Hsiao-Sheng Liu, Hsiaowei Liu, Hsu-Hsiang Liu, Hu Liu, Hua Liu, Hua-Cheng Liu, Hua-Ge Liu, Huadong Liu, Huaizheng Liu, Huan Liu, Huan-Yu Liu, Huanhuan Liu, Huanliang Liu, Huanyi Liu, Huatao Liu, Huawei Liu, Huayang Liu, Huazhen Liu, Hui Liu, Hui-Chao Liu, Hui-Fang Liu, Hui-Guo Liu, Hui-Hui Liu, Hui-Xin Liu, Hui-Ying Liu, Huibin Liu, Huidi Liu, Huihua Liu, Huihui Liu, Huijuan Liu, Huijun Liu, Huikun Liu, Huiling Liu, Huimao Liu, Huimin Liu, Huiming Liu, Huina Liu, Huiping Liu, Huiqing Liu, Huisheng Liu, Huiying Liu, Huiyu Liu, Hulin Liu, J Liu, J R Liu, J W Liu, J X Liu, J Z Liu, James K C Liu, Jamie Liu, Jay Liu, Ji Liu, Ji-Kai Liu, Ji-Long Liu, Ji-Xing Liu, Ji-Xuan Liu, Ji-Yun Liu, Jia Liu, Jia-Cheng Liu, Jia-Jun Liu, Jia-Qian Liu, Jia-Yao Liu, JiaXi Liu, Jiabin Liu, Jiachen Liu, Jiahao Liu, Jiahua Liu, Jiahui Liu, Jiajie Liu, Jiajuan Liu, Jiakun Liu, Jiali Liu, Jialin Liu, Jiamin Liu, Jiaming Liu, Jian Liu, Jian-Jun Liu, Jian-Kun Liu, Jian-hong Liu, Jian-shu Liu, Jianan Liu, Jianbin Liu, Jianbo Liu, Jiandong Liu, Jianfang Liu, Jianfeng Liu, Jiang Liu, Jiangang Liu, Jiangbin Liu, Jianghong Liu, Jianghua Liu, Jiangjiang Liu, Jiangjin Liu, Jiangling Liu, Jiangxin Liu, Jiangyan Liu, Jianhua Liu, Jianhui Liu, Jiani Liu, Jianing Liu, Jianjiang Liu, Jianjun Liu, Jiankang Liu, Jiankun Liu, Jianlei Liu, Jianmei Liu, Jianmin Liu, Jiannan Liu, Jianping Liu, Jiantao Liu, Jianwei Liu, Jianxi Liu, Jianxin Liu, Jianyong Liu, Jianyu Liu, Jianyun Liu, Jiao Liu, Jiaojiao Liu, Jiaoyang Liu, Jiaqi Liu, Jiaqing Liu, Jiawen Liu, Jiaxian Liu, Jiaxiang Liu, Jiaxin Liu, Jiayan Liu, Jiayi Liu, Jiayin Liu, Jiaying Liu, Jiayu Liu, Jiayun Liu, Jiazhe Liu, Jiazheng Liu, Jiazhuo Liu, Jidan Liu, Jie Liu, Jie-Qing Liu, Jierong Liu, Jiewei Liu, Jiewen Liu, Jieying Liu, Jieyu Liu, Jihe Liu, Jiheng Liu, Jin Liu, Jin-Juan Liu, Jin-Qing Liu, Jinbao Liu, Jinbo Liu, Jincheng Liu, Jindi Liu, Jinfeng Liu, Jing Liu, Jing Min Liu, Jing-Crystal Liu, Jing-Hua Liu, Jing-Ying Liu, Jing-Yu Liu, Jingbo Liu, Jingchong Liu, Jingfang Liu, Jingfeng Liu, Jingfu Liu, Jinghui Liu, Jingjie Liu, Jingjing Liu, Jingmeng Liu, Jingmin Liu, Jingqi Liu, Jingquan Liu, Jingqun Liu, Jingsheng Liu, Jingwei Liu, Jingwen Liu, Jingxing Liu, Jingyi Liu, Jingying Liu, Jingyun Liu, Jingzhong Liu, Jinjie Liu, Jinlian Liu, Jinlong Liu, Jinman Liu, Jinpei Liu, Jinpeng Liu, Jinping Liu, Jinqin Liu, Jinrong Liu, Jinsheng Liu, Jinsong Liu, Jinsuo Liu, Jinxiang Liu, Jinxin Liu, Jinxing Liu, Jinyue Liu, Jinze Liu, Jinzhao Liu, Jinzhi Liu, Jiong Liu, Jishan Liu, Jitao Liu, Jiwei Liu, Jixin Liu, Jonathan Liu, Joyce F Liu, Joyce Liu, Ju Liu, Ju-Fang Liu, Juan Liu, Juanjuan Liu, Juanxi Liu, Jue Liu, Jui-Tung Liu, Jun Liu, Jun O Liu, Jun Ting Liu, Jun Yi Liu, Jun-Jen Liu, Jun-Yan Liu, Jun-Yi Liu, Junbao Liu, Junchao Liu, Junfen Liu, Junhui Liu, Junjiang Liu, Junjie Liu, Junjin Liu, Junjun Liu, Junlin Liu, Junling Liu, Junnian Liu, Junpeng Liu, Junqi Liu, Junrong Liu, Juntao Liu, Juntian Liu, Junwen Liu, Junwu Liu, Junxi Liu, Junyan Liu, Junye Liu, Junying Liu, Junyu Liu, Juyao Liu, Kai Liu, Kai-Zheng Liu, Kaidong Liu, Kaijing Liu, Kaikun Liu, Kaiqi Liu, Kaisheng Liu, Kaitai Liu, Kaiwen Liu, Kang Liu, Kang-le Liu, Kangdong Liu, Kangwei Liu, Kathleen D Liu, Ke Liu, Ke-Tong Liu, Kechun Liu, Kehui Liu, Kejia Liu, Keng-Hau Liu, Keqiang Liu, Kexin Liu, Kiang Liu, Kuangyi Liu, Kun Liu, Kun-Cheng Liu, Kwei-Yan Liu, L L Liu, L Liu, L W Liu, Lan Liu, Lan-Xiang Liu, Lang Liu, Lanhao Liu, Le Liu, Lebin Liu, Lei Liu, Lele Liu, Leping Liu, Li Liu, Li-Fang Liu, Li-Min Liu, Li-Rong Liu, Li-Wen Liu, Li-Xuan Liu, Li-Ying Liu, Li-ping Liu, Lian Liu, Lianfei Liu, Liang Liu, Liang-Chen Liu, Liang-Feng Liu, Liangguo Liu, Liangji Liu, Liangjia Liu, Liangliang Liu, Liangyu Liu, Lianxin Liu, Lianyong Liu, Libin Liu, Lichao Liu, Lichun Liu, Lidong Liu, Liegang Liu, Lifang Liu, Ligang Liu, Lihua Liu, Lijuan Liu, Lijun Liu, Lili Liu, Liling Liu, Limin Liu, Liming Liu, Lin Liu, Lina Liu, Ling Liu, Ling-Yun Liu, Ling-Zhi Liu, Lingfei Liu, Lingjiao Liu, Lingjuan Liu, Linglong Liu, Lingyan Liu, Lining Liu, Linlin Liu, Linqing Liu, Linwen Liu, Liping Liu, Liqing Liu, Liqiong Liu, Liqun Liu, Lirong Liu, Liru Liu, Liu Liu, Liumei Liu, Liusheng Liu, Liwen Liu, Lixia Liu, Lixian Liu, Lixiao Liu, Liying Liu, Liyue Liu, Lizhen Liu, Long Liu, Longfei Liu, Longjian Liu, Longqian Liu, Longyang Liu, Longzhou Liu, Lu Liu, Luhong Liu, Lulu Liu, Luming Liu, Lunxu Liu, Luping Liu, Lushan Liu, Lv Liu, M L Liu, M Liu, Man Liu, Man-Ru Liu, Manjiao Liu, Manqi Liu, Manran Liu, Maolin Liu, Mei Liu, Mei-mei Liu, Meicen Liu, Meifang Liu, Meijiao Liu, Meijing Liu, Meijuan Liu, Meijun Liu, Meiling Liu, Meimei Liu, Meixin Liu, Meiyan Liu, Meng Han Liu, Meng Liu, Meng-Hui Liu, Meng-Meng Liu, Meng-Yue Liu, Mengduan Liu, Mengfan Liu, Mengfei Liu, Menggang Liu, Menghan Liu, Menghua Liu, Menghui Liu, Mengjia Liu, Mengjiao Liu, Mengke Liu, Menglin Liu, Mengling Liu, Mengmei Liu, Mengqi Liu, Mengqian Liu, Mengxi Liu, Mengxue Liu, Mengyang Liu, Mengying Liu, Mengyu Liu, Mengyuan Liu, Mengzhen Liu, Mi Liu, Mi-Hua Liu, Mi-Min Liu, Miao Liu, Miaoliang Liu, Min Liu, Minda Liu, Minetta C Liu, Ming Liu, Ming-Jiang Liu, Ming-Qi Liu, Mingcheng Liu, Mingchun Liu, Mingfan Liu, Minghui Liu, Mingjiang Liu, Mingjing Liu, Mingjun Liu, Mingli Liu, Mingming Liu, Mingna Liu, Mingqin Liu, Mingrui Liu, Mingsen Liu, Mingsong Liu, Mingxiao Liu, Mingxing Liu, Mingxu Liu, Mingyang Liu, Mingyao Liu, Mingying Liu, Mingyu Liu, Minhao Liu, Minxia Liu, Mo-Nan Liu, Modan Liu, Mouze Liu, Muqiu Liu, Musang Liu, N A Liu, N Liu, Na Liu, Na-Nv Liu, Na-Wei Liu, Nai-feng Liu, Naihua Liu, Naili Liu, Nan Liu, Nan-Song Liu, Nana Liu, Nannan Liu, Nanxi Liu, Ni Liu, Nian Liu, Ning Liu, Ning'ang Liu, Ningning Liu, Niya Liu, Ou Liu, Ouxuan Liu, P C Liu, Pan Liu, Panhong Liu, Panting Liu, Paul Liu, Pei Liu, Pei-Ning Liu, Peijian Liu, Peijie Liu, Peijun Liu, Peilong Liu, Peiqi Liu, Peiqing Liu, Peiwei Liu, Peixi Liu, Peiyao Liu, Peizhong Liu, Peng Liu, Pengcheng Liu, Pengfei Liu, Penghong Liu, Pengli Liu, Pengtao Liu, Pengyu Liu, Pengyuan Liu, Pentao Liu, Peter S Liu, Piaopiao Liu, Pinduo Liu, Ping Liu, Ping-Yen Liu, Pinghuai Liu, Pingping Liu, Pingsheng Liu, Q Liu, Qi Liu, Qi-Xian Liu, Qian Liu, Qian-Wen Liu, Qiang Liu, Qiang-Yuan Liu, Qiangyun Liu, Qianjin Liu, Qianqi Liu, Qianshuo Liu, Qianwei Liu, Qiao-Hong Liu, Qiaofeng Liu, Qiaoyan Liu, Qiaozhen Liu, Qiji Liu, Qiming Liu, Qin Liu, Qinfang Liu, Qing Liu, Qing-Huai Liu, Qing-Rong Liu, Qingbin Liu, Qingbo Liu, Qingguang Liu, Qingguo Liu, Qinghao Liu, Qinghong Liu, Qinghua Liu, Qinghuai Liu, Qinghuan Liu, Qinglei Liu, Qingping Liu, Qingqing Liu, Qingquan Liu, Qingsong Liu, Qingxia Liu, Qingxiang Liu, Qingyang Liu, Qingyou Liu, Qingyun Liu, Qingzhuo Liu, Qinqin Liu, Qiong Liu, Qiu-Ping Liu, Qiulei Liu, Qiuli Liu, Qiulu Liu, Qiushi Liu, Qiuxu Liu, Qiuyu Liu, Qiuyue Liu, Qiwei Liu, Qiyao Liu, Qiye Liu, Qizhan Liu, Quan Liu, Quan-Jun Liu, Quanxin Liu, Quanying Liu, Quanzhong Liu, Quentin Liu, Qun Liu, Qunlong Liu, Qunpeng Liu, R F Liu, R Liu, R Y Liu, Ran Liu, Rangru Liu, Ranran Liu, Ren Liu, Renling Liu, Ri Liu, Rong Liu, Rong-Zong Liu, Rongfei Liu, Ronghua Liu, Rongxia Liu, Rongxun Liu, Rui Liu, Rui-Jie Liu, Rui-Tian Liu, Rui-Xuan Liu, Ruichen Liu, Ruihua Liu, Ruijie Liu, Ruijuan Liu, Ruilong Liu, Ruiping Liu, Ruiqi Liu, Ruitong Liu, Ruixia Liu, Ruiyi Liu, Ruizao Liu, Runjia Liu, Runjie Liu, Runni Liu, Runping Liu, Ruochen Liu, Ruotian Liu, Ruowen Liu, Ruoyang Liu, Ruyi Liu, Ruyue Liu, S Liu, Saiji Liu, Sasa Liu, Sen Liu, Senchen Liu, Senqi Liu, Sha Liu, Shan Liu, Shan-Shan Liu, Shandong Liu, Shang-Feng Liu, Shang-Xin Liu, Shangjing Liu, Shangxin Liu, Shangyu Liu, Shangyuan Liu, Shangyun Liu, Shanhui Liu, Shanling Liu, Shanshan Liu, Shao-Bin Liu, Shao-Jun Liu, Shao-Yuan Liu, Shaobo Liu, Shaocheng Liu, Shaohua Liu, Shaojun Liu, Shaoqing Liu, Shaowei Liu, Shaoying Liu, Shaoyou Liu, Shaoyu Liu, Shaozhen Liu, Shasha Liu, Sheng Liu, Shengbin Liu, Shengjun Liu, Shengnan Liu, Shengyang Liu, Shengzhi Liu, Shengzhuo Liu, Shenhai Liu, Shenping Liu, Shi Liu, Shi-Lian Liu, Shi-Wei Liu, Shi-Yong Liu, ShiWei Liu, Shih-Ping Liu, Shijia Liu, Shijian Liu, Shijie Liu, Shijun Liu, Shikai Liu, Shikun Liu, Shilin Liu, Shing-Hwa Liu, Shiping Liu, Shiqian Liu, Shiquan Liu, Shiru Liu, Shixi Liu, Shiyan Liu, Shiyang Liu, Shiying Liu, Shiyu Liu, Shiyuan Liu, Shou-Sheng Liu, Shouguo Liu, Shoupei Liu, Shouxin Liu, Shouyang Liu, Shu Liu, Shu-Chen Liu, Shu-Jing Liu, Shu-Lin Liu, Shu-Qiang Liu, Shu-Qin Liu, Shuai Liu, Shuaishuai Liu, Shuang Liu, Shuangli Liu, Shuangzhu Liu, Shuhong Liu, Shuhua Liu, Shui-Bing Liu, Shujie Liu, Shujing Liu, Shujun Liu, Shulin Liu, Shuling Liu, Shumin Liu, Shun-Mei Liu, Shunfang Liu, Shuning Liu, Shunming Liu, Shuqian Liu, Shuqing Liu, Shuwen Liu, Shuxi Liu, Shuxian Liu, Shuya Liu, Shuyan Liu, Shuyu Liu, Si-Jin Liu, Si-Xu Liu, Si-Yan Liu, Si-jun Liu, Sicheng Liu, Sidan Liu, Side Liu, Sihao Liu, Sijing Liu, Sijun Liu, Silvia Liu, Simin Liu, Sipu Liu, Siqi Liu, Siqin Liu, Siru Liu, Sirui Liu, Sisi Liu, Sitian Liu, Siwen Liu, Sixi Liu, Sixin Liu, Sixiu Liu, Sixu Liu, Siyao Liu, Siyi Liu, Siyu Liu, Siyuan Liu, Song Liu, Song-Fang Liu, Song-Mei Liu, Song-Ping Liu, Songfang Liu, Songhui Liu, Songqin Liu, Songsong Liu, Songyi Liu, Su Liu, Su-Yun Liu, Sudong Liu, Suhuan Liu, Sui-Feng Liu, Suling Liu, Suosi Liu, Sushuang Liu, Susu Liu, Szu-Heng Liu, T H Liu, T Liu, Ta-Chih Liu, Taihang Liu, Taixiang Liu, Tang Liu, Tao Liu, Taoli Liu, Taotao Liu, Te Liu, Teng Liu, Tengfei Liu, Tengli Liu, Teresa T Liu, Tian Liu, Tian Shu Liu, Tianhao Liu, Tianhu Liu, Tianjia Liu, Tianjiao Liu, Tianlai Liu, Tianlang Liu, Tianlong Liu, Tianqiang Liu, Tianrui Liu, Tianshu Liu, Tiantian Liu, Tianyao Liu, Tianyi Liu, Tianyu Liu, Tianze Liu, Tiemin Liu, Tina Liu, Ting Liu, Ting-Li Liu, Ting-Ting Liu, Ting-Yuan Liu, Tingjiao Liu, Tingting Liu, Tong Liu, Tonglin Liu, Tongtong Liu, Tongyan Liu, Tongyu Liu, Tongyun Liu, Tongzheng Liu, Tsang-Wu Liu, Tsung-Yun Liu, Vincent W S Liu, W Liu, W-Y Liu, Wan Liu, Wan-Chun Liu, Wan-Di Liu, Wan-Guo Liu, Wan-Ying Liu, Wang Liu, Wangrui Liu, Wanguo Liu, Wangyang Liu, Wanjun Liu, Wanli Liu, Wanlu Liu, Wanqi Liu, Wanqing Liu, Wanting Liu, Wei Liu, Wei-Chieh Liu, Wei-Hsuan Liu, Wei-Hua Liu, Weida Liu, Weifang Liu, Weifeng Liu, Weiguo Liu, Weihai Liu, Weihong Liu, Weijian Liu, Weijie Liu, Weijun Liu, Weilin Liu, Weimin Liu, Weiming Liu, Weina Liu, Weiqin Liu, Weiqing Liu, Weiren Liu, Weisheng Liu, Weishuo Liu, Weiwei Liu, Weiyang Liu, Wen Liu, Wen Yuan Liu, Wen-Chun Liu, Wen-Di Liu, Wen-Fang Liu, Wen-Jie Liu, Wen-Jing Liu, Wen-Qiang Liu, Wen-Tao Liu, Wen-ling Liu, Wenbang Liu, Wenbin Liu, Wenbo Liu, Wenchao Liu, Wenen Liu, Wenfeng Liu, Wenhan Liu, Wenhao Liu, Wenhua Liu, Wenjie Liu, Wenjing Liu, Wenlang Liu, Wenli Liu, Wenling Liu, Wenlong Liu, Wenna Liu, Wenping Liu, Wenqi Liu, Wenrui Liu, Wensheng Liu, Wentao Liu, Wenwu Liu, Wenxiang Liu, Wenxuan Liu, Wenya Liu, Wenyan Liu, Wenyi Liu, Wenzhong Liu, Wu Liu, Wuping Liu, Wuyang Liu, X C Liu, X Liu, X P Liu, X-D Liu, Xi Liu, Xi-Yu Liu, Xia Liu, Xia-Meng Liu, Xialin Liu, Xian Liu, Xianbao Liu, Xianchen Liu, Xianda Liu, Xiang Liu, Xiang-Qian Liu, Xiang-Yu Liu, Xiangchen Liu, Xiangfei Liu, Xianglan Liu, Xiangli Liu, Xiangliang Liu, Xianglu Liu, Xiangning Liu, Xiangping Liu, Xiangsheng Liu, Xiangtao Liu, Xiangting Liu, Xiangxiang Liu, Xiangxuan Liu, Xiangyong Liu, Xiangyu Liu, Xiangyun Liu, Xianli Liu, Xianling Liu, Xiansheng Liu, Xianyang Liu, Xiao Dong Liu, Xiao Liu, Xiao Yan Liu, Xiao-Cheng Liu, Xiao-Dan Liu, Xiao-Gang Liu, Xiao-Guang Liu, Xiao-Huan Liu, Xiao-Jiao Liu, Xiao-Li Liu, Xiao-Ling Liu, Xiao-Ning Liu, Xiao-Qiu Liu, Xiao-Qun Liu, Xiao-Rong Liu, Xiao-Song Liu, Xiao-Xiao Liu, Xiao-lan Liu, Xiaoan Liu, Xiaobai Liu, Xiaobei Liu, Xiaobing Liu, Xiaocen Liu, Xiaochuan Liu, Xiaocong Liu, Xiaodan Liu, Xiaoding Liu, Xiaodong Liu, Xiaofan Liu, Xiaofang Liu, Xiaofei Liu, Xiaogang Liu, Xiaoguang Liu, Xiaoguang Margaret Liu, Xiaohan Liu, Xiaoheng Liu, Xiaohong Liu, Xiaohua Liu, Xiaohuan Liu, Xiaohui Liu, Xiaojie Liu, Xiaojing Liu, Xiaoju Liu, Xiaojun Liu, Xiaole Shirley Liu, Xiaolei Liu, Xiaoli Liu, Xiaolin Liu, Xiaoling Liu, Xiaoman Liu, Xiaomei Liu, Xiaomeng Liu, Xiaomin Liu, Xiaoming Liu, Xiaona Liu, Xiaonan Liu, Xiaopeng Liu, Xiaoping Liu, Xiaoqian Liu, Xiaoqiang Liu, Xiaoqin Liu, Xiaoqing Liu, Xiaoran Liu, Xiaosong Liu, Xiaotian Liu, Xiaoting Liu, Xiaowei Liu, Xiaoxi Liu, Xiaoxia Liu, Xiaoxiao Liu, Xiaoxu Liu, Xiaoxue Liu, Xiaoya Liu, Xiaoyan Liu, Xiaoyang Liu, Xiaoye Liu, Xiaoying Liu, Xiaoyong Liu, Xiaoyu Liu, Xiawen Liu, Xibao Liu, Xibing Liu, Xie-hong Liu, Xiehe Liu, Xiguang Liu, Xijun Liu, Xili Liu, Xin Liu, Xin-Hua Liu, Xin-Yan Liu, Xinbo Liu, Xinchang Liu, Xing Liu, Xing-De Liu, Xing-Li Liu, Xing-Yang Liu, Xingbang Liu, Xingde Liu, Xinghua Liu, Xinghui Liu, Xingjing Liu, Xinglei Liu, Xingli Liu, Xinglong Liu, Xinguo Liu, Xingxiang Liu, Xingyi Liu, Xingyu Liu, Xinhua Liu, Xinjun Liu, Xinlei Liu, Xinli Liu, Xinmei Liu, Xinmin Liu, Xinran Liu, Xinru Liu, Xinrui Liu, Xintong Liu, Xinxin Liu, Xinyao Liu, Xinyi Liu, Xinying Liu, Xinyong Liu, Xinyu Liu, Xinyue Liu, Xiong Liu, Xiqiang Liu, Xiru Liu, Xishan Liu, Xiu Liu, Xiufen Liu, Xiufeng Liu, Xiuheng Liu, Xiuling Liu, Xiumei Liu, Xiuqin Liu, Xiyong Liu, Xu Liu, Xu-Dong Liu, Xu-Hui Liu, Xuan Liu, Xuanlin Liu, Xuanyu Liu, Xuanzhu Liu, Xue Liu, Xue-Lian Liu, Xue-Min Liu, Xue-Qing Liu, Xue-Zheng Liu, Xuefang Liu, Xuejing Liu, Xuekui Liu, Xuelan Liu, Xueling Liu, Xuemei Liu, Xuemeng Liu, Xuemin Liu, Xueping Liu, Xueqin Liu, Xueqing Liu, Xueru Liu, Xuesen Liu, Xueshibojie Liu, Xuesong Liu, Xueting Liu, Xuewei Liu, Xuewen Liu, Xuexiu Liu, Xueying Liu, Xueyuan Liu, Xuezhen Liu, Xuezheng Liu, Xuezhi Liu, Xufeng Liu, Xuguang Liu, Xujie Liu, Xulin Liu, Xuming Liu, Xunhua Liu, Xunyue Liu, Xuxia Liu, Xuxu Liu, Xuyi Liu, Xuying Liu, Y H Liu, Y L Liu, Y Liu, Y Y Liu, Ya Liu, Ya-Jin Liu, Ya-Kun Liu, Ya-Wei Liu, Yadong Liu, Yafei Liu, Yajing Liu, Yajuan Liu, Yaling Liu, Yalu Liu, Yan Liu, Yan-Li Liu, Yanan Liu, Yanchao Liu, Yanchen Liu, Yandong Liu, Yanfei Liu, Yanfen Liu, Yanfeng Liu, Yang Liu, Yange Liu, Yangfan Liu, Yangfan P Liu, Yangjun Liu, Yangkai Liu, Yangruiyu Liu, Yangyang Liu, Yanhong Liu, Yanhua Liu, Yanhui Liu, Yanjie Liu, Yanju Liu, Yanjun Liu, Yankuo Liu, Yanli Liu, Yanliang Liu, Yanling Liu, Yanman Liu, Yanmin Liu, Yanping Liu, Yanqing Liu, Yanqiu Liu, Yanquan Liu, Yanru Liu, Yansheng Liu, Yansong Liu, Yanting Liu, Yanwu Liu, Yanxiao Liu, Yanyan Liu, Yanyao Liu, Yanying Liu, Yanyun Liu, Yao Liu, Yao-Hui Liu, Yaobo Liu, Yaoquan Liu, Yaou Liu, Yaowen Liu, Yaoyao Liu, Yaozhong Liu, Yaping Liu, Yaqiong Liu, Yarong Liu, Yaru Liu, Yating Liu, Yaxin Liu, Ye Liu, Ye-Dan Liu, Yehai Liu, Yen-Chen Liu, Yen-Chun Liu, Yen-Nien Liu, Yeqing Liu, Yi Liu, Yi-Chang Liu, Yi-Chien Liu, Yi-Han Liu, Yi-Hung Liu, Yi-Jia Liu, Yi-Ling Liu, Yi-Meng Liu, Yi-Ming Liu, Yi-Yun Liu, Yi-Zhang Liu, YiRan Liu, Yibin Liu, Yibing Liu, Yicun Liu, Yidan Liu, Yidong Liu, Yifan Liu, Yifu Liu, Yihao Liu, Yiheng Liu, Yihui Liu, Yijing Liu, Yilei Liu, Yili Liu, Yilin Liu, Yimei Liu, Yiming Liu, Yin Liu, Yin-Ping Liu, Yinchu Liu, Yinfang Liu, Ying Liu, Ying Poi Liu, Yingchun Liu, Yinghua Liu, Yinghuan Liu, Yinghui Liu, Yingjun Liu, Yingli Liu, Yingwei Liu, Yingxia Liu, Yingyan Liu, Yingyi Liu, Yingying Liu, Yingzi Liu, Yinhe Liu, Yinhui Liu, Yining Liu, Yinjiang Liu, Yinping Liu, Yinuo Liu, Yiping Liu, Yiqing Liu, Yitian Liu, Yiting Liu, Yitong Liu, Yiwei Liu, Yiwen Liu, Yixiang Liu, Yixiao Liu, Yixuan Liu, Yiyang Liu, Yiyi Liu, Yiyuan Liu, Yiyun Liu, Yizhi Liu, Yizhuo Liu, Yong Liu, Yong Mei Liu, Yong-Chao Liu, Yong-Hong Liu, Yong-Jian Liu, Yong-Jun Liu, Yong-Tai Liu, Yong-da Liu, Yongchao Liu, Yonggang Liu, Yonggao Liu, Yonghong Liu, Yonghua Liu, Yongjian Liu, Yongjie Liu, Yongjun Liu, Yongli Liu, Yongmei Liu, Yongming Liu, Yongqiang Liu, Yongshuo Liu, Yongtai Liu, Yongtao Liu, Yongtong Liu, Yongxiao Liu, Yongyue Liu, You Liu, You-ping Liu, Youan Liu, Youbin Liu, Youdong Liu, Youhan Liu, Youlian Liu, Youwen Liu, Yu Liu, Yu Xuan Liu, Yu-Chen Liu, Yu-Ching Liu, Yu-Hui Liu, Yu-Li Liu, Yu-Lin Liu, Yu-Peng Liu, Yu-Wei Liu, Yu-Zhang Liu, YuHeng Liu, Yuan Liu, Yuan-Bo Liu, Yuan-Jie Liu, Yuan-Tao Liu, YuanHua Liu, Yuanchu Liu, Yuanfa Liu, Yuanhang Liu, Yuanhui Liu, Yuanjia Liu, Yuanjiao Liu, Yuanjun Liu, Yuanliang Liu, Yuantao Liu, Yuantong Liu, Yuanxiang Liu, Yuanxin Liu, Yuanxing Liu, Yuanying Liu, Yuanyuan Liu, Yubin Liu, Yuchen Liu, Yue Liu, Yuecheng Liu, Yuefang Liu, Yuehong Liu, Yueli Liu, Yueping Liu, Yuetong Liu, Yuexi Liu, Yuexin Liu, Yuexing Liu, Yueyang Liu, Yueyun Liu, Yufan Liu, Yufei Liu, Yufeng Liu, Yuhao Liu, Yuhe Liu, Yujia Liu, Yujiang Liu, Yujie Liu, Yujun Liu, Yulan Liu, Yuling Liu, Yulong Liu, Yumei Liu, Yumiao Liu, Yun Liu, Yun-Cai Liu, Yun-Qiang Liu, Yun-Ru Liu, Yun-Zi Liu, Yunfen Liu, Yunfeng Liu, Yuning Liu, Yunjie Liu, Yunlong Liu, Yunqi Liu, Yunqiang Liu, Yuntao Liu, Yunuan Liu, Yunuo Liu, Yunxia Liu, Yunyun Liu, Yuping Liu, Yupu Liu, Yuqi Liu, Yuqiang Liu, Yuqing Liu, Yurong Liu, Yuru Liu, Yusen Liu, Yutao Liu, Yutian Liu, Yuting Liu, Yutong Liu, Yuwei Liu, Yuxi Liu, Yuxia Liu, Yuxiang Liu, Yuxin Liu, Yuxuan Liu, Yuyan Liu, Yuyi Liu, Yuyu Liu, Yuyuan Liu, Yuzhen Liu, Yv-Xuan Liu, Z H Liu, Z Q Liu, Z Z Liu, Zaiqiang Liu, Zan Liu, Zaoqu Liu, Ze Liu, Zefeng Liu, Zekun Liu, Zeming Liu, Zengfu Liu, Zeyu Liu, Zezhou Liu, Zhangyu Liu, Zhangyuan Liu, Zhansheng Liu, Zhao Liu, Zhaoguo Liu, Zhaoli Liu, Zhaorui Liu, Zhaotian Liu, Zhaoxiang Liu, Zhaoxun Liu, Zhaoyang Liu, Zhe Liu, Zhekai Liu, Zheliang Liu, Zhen Liu, Zhen-Lin Liu, Zhendong Liu, Zhenfang Liu, Zhenfeng Liu, Zheng Liu, Zheng-Hong Liu, Zheng-Yu Liu, ZhengYi Liu, Zhengbing Liu, Zhengchuang Liu, Zhengdong Liu, Zhenghao Liu, Zhengkun Liu, Zhengtang Liu, Zhengting Liu, Zhenguo Liu, Zhengxia Liu, Zhengye Liu, Zhenhai Liu, Zhenhao Liu, Zhenhua Liu, Zhenjiang Liu, Zhenjiao Liu, Zhenjie Liu, Zhenkui Liu, Zhenlei Liu, Zhenmi Liu, Zhenming Liu, Zhenna Liu, Zhenqian Liu, Zhenqiu Liu, Zhenwei Liu, Zhenxing Liu, Zhenxiu Liu, Zhenzhen Liu, Zhenzhu Liu, Zhi Liu, Zhi Y Liu, Zhi-Fen Liu, Zhi-Guo Liu, Zhi-Jie Liu, Zhi-Kai Liu, Zhi-Ping Liu, Zhi-Ren Liu, Zhi-Wen Liu, Zhi-Ying Liu, Zhicheng Liu, Zhifang Liu, Zhigang Liu, Zhiguo Liu, Zhihan Liu, Zhihao Liu, Zhihong Liu, Zhihua Liu, Zhihui Liu, Zhijia Liu, Zhijie Liu, Zhikui Liu, Zhili Liu, Zhiming Liu, Zhipeng Liu, Zhiping Liu, Zhiqian Liu, Zhiqiang Liu, Zhiru Liu, Zhirui Liu, Zhishuo Liu, Zhitao Liu, Zhiteng Liu, Zhiwei Liu, Zhixiang Liu, Zhixue Liu, Zhiyan Liu, Zhiying Liu, Zhiyong Liu, Zhiyuan Liu, Zhong Liu, Zhong Wu Liu, Zhong-Hua Liu, Zhong-Min Liu, Zhong-Qiu Liu, Zhong-Wu Liu, Zhong-Ying Liu, Zhongchun Liu, Zhongguo Liu, Zhonghua Liu, Zhongjian Liu, Zhongjuan Liu, Zhongmin Liu, Zhongqi Liu, Zhongqiu Liu, Zhongwei Liu, Zhongyu Liu, Zhongyue Liu, Zhongzhong Liu, Zhou Liu, Zhou-di Liu, Zhu Liu, Zhuangjun Liu, Zhuanhua Liu, Zhuo Liu, Zhuoyuan Liu, Zi Hao Liu, Zi-Hao Liu, Zi-Lun Liu, Zi-Ye Liu, Zi-wen Liu, Zichuan Liu, Zihang Liu, Zihao Liu, Zihe Liu, Ziheng Liu, Zijia Liu, Zijian Liu, Zijing J Liu, Zimeng Liu, Ziqian Liu, Ziqin Liu, Ziteng Liu, Zitian Liu, Ziwei Liu, Zixi Liu, Zixuan Liu, Ziyang Liu, Ziying Liu, Ziyou Liu, Ziyuan Liu, Ziyue Liu, Zong-Chao Liu, Zong-Yuan Liu, Zonghua Liu, Zongjun Liu, Zongtao Liu, Zongxiang Liu, Zu-Guo Liu, Zuguo Liu, Zuohua Liu, Zuojin Liu, Zuolu Liu, Zuyi Liu, Zuyun Liu
articles

JMJD1C-mediated metabolic dysregulation contributes to HOXA9-dependent leukemogenesis.

Jennifer R Lynch, Basit Salik, Patrick Connerty +13 more · 2019 · Leukemia · Nature · added 2026-04-24
Abnormal metabolism is a fundamental hallmark of cancer and represents a therapeutic opportunity, yet its regulation by oncogenes remains poorly understood. Here, we uncover that JMJD1C, a jumonji C ( Show more
Abnormal metabolism is a fundamental hallmark of cancer and represents a therapeutic opportunity, yet its regulation by oncogenes remains poorly understood. Here, we uncover that JMJD1C, a jumonji C (JmjC)-containing H3K9 demethylase, is a critical regulator of aberrant metabolic processes in homeobox A9 (HOXA9)-dependent acute myeloid leukemia (AML). JMJD1C overexpression increases in vivo cell proliferation and tumorigenicity through demethylase-independent upregulation of a glycolytic and oxidative program, which sustains leukemic cell bioenergetics and contributes to an aggressive AML phenotype in vivo. Targeting JMJD1C-mediated metabolism via pharmacologic inhibition of glycolysis and oxidative phosphorylation led to ATP depletion, induced necrosis/apoptosis and decreased tumor growth in vivo in leukemias co-expressing JMJD1C and HOXA9. The anti-metabolic therapy effectively diminished AML stem/progenitor cells and reduced tumor burden in a primary AML patient-derived xenograft. Our data establish a direct link between drug responses and endogenous expression of JMJD1C and HOXA9 in human AML cell line- and patient-derived xenografts. These findings demonstrate a previously unappreciated role for JMJD1C in counteracting adverse metabolic changes and retaining the metabolic integrity during tumorigenesis, which can be exploited therapeutically. Show less
📄 PDF DOI: 10.1038/s41375-018-0354-z
JMJD1C

Paternal chronic folate supplementation induced the transgenerational inheritance of acquired developmental and metabolic changes in chickens.

Shengru Wu, Wei Guo, Xinyi Li +5 more · 2019 · Proceedings. Biological sciences · The Royal Society · added 2026-04-24
Increasing evidence indicates that paternal diet can result in metabolic changes in offspring, but the definite mechanism remains unclear in birds. Here, we fed breeder cocks five different diets cont Show more
Increasing evidence indicates that paternal diet can result in metabolic changes in offspring, but the definite mechanism remains unclear in birds. Here, we fed breeder cocks five different diets containing 0, 0.25, 1.25, 2.50 and 5.00 mg kg Show less
no PDF DOI: 10.1098/rspb.2019.1653
ANGPTL4

Epigallocatechin-3-Gallate Attenuates Adriamycin-Induced Focal Segmental Glomerulosclerosis via Suppression of Oxidant Stress and Apoptosis by Targeting Hypoxia-Inducible Factor-1α/ Angiopoietin-Like 4 Pathway.

Guoyong Liu, Liyu He · 2019 · Pharmacology · added 2026-04-24
Focal and segmental glomerular sclerosis (FSGS) is a common cause of nephrotic syndrome and end-stage renal disease. It has been reported that overproduction of reactive oxygen species (ROS) and cell Show more
Focal and segmental glomerular sclerosis (FSGS) is a common cause of nephrotic syndrome and end-stage renal disease. It has been reported that overproduction of reactive oxygen species (ROS) and cell apoptosis are associated with the development of FSGS. Epigallocatechin-3-gallate (EGCG) is a bioactive constituent accounting for more than 50% of the total catechins in green tea, which have anti-oxidative and anti-apoptotic effects. Based on this, this study was designed to evaluate the renoprotective effect of EGCG treatment on Adriamycin-induced FSGS. -Methods: In C57BL/6 mice, Adriamycin nephropathy (AN) was induced by Adriamycin (10 mg/kg body weight, diluted in normal saline) via a tail vein on day 0. Then the mice were given with EGCG (20 mg/kg body weight) or YC-1 (Lificiguat, a specific inhibitor of hypoxia-inducible factor-1α [HIF-1α], 50 mg/kg body weight) or both intraperitoneally. Both the EGCG and YC-1 were given on the day of Adriamycin injection and continued for 6 weeks. The animals were organized into the following 5 groups for the animal experiments: the control group, the AN group, the AN + EGCG group, the AN + YC-1 group and the AN + EGCG + YC-1 group. At 6 weeks, the mice were sacrificed; kidneys and blood samples were collected for further analysis. The HIF-1α and the angiopoietin-like 4 (ANGPTL4) expression were detected by Western blot, real-time PCR, immunohistochemistry or immunofluorescence. Dihydroethidium staining and NADPH oxidase 1 (Nox1) measurement were used to detect ROS production. Terminal deoxynucleotide transferase-mediated dUTP nick end-labeling (TUNEL) staining and caspase-3 measurement was used to detect cell apoptosis. When the animals were treated with Adriamycin, both the ROS production and TUNEL positive cells increased. Besides, the expression of HIF-1α, ANGPTL4, and caspase-3 were also up-regulated, while EGCG treatment could attenuate these changes. Interestingly, compared with treatment with YC-1 or EGCG alone, more pronounced inhibition of ANGPTL4, caspase-3 and Nox1 were obtained when YC-1 and EGCG were administered simultaneously. EGCG attenuates FSGS through the suppression of Oxidant Stress and apoptosis by targeting the HIF-1α/ANGPTL4 pathway. Show less
no PDF DOI: 10.1159/000496799
ANGPTL4

Delayed Feeding Alters Transcriptional and Post-Transcriptional Regulation of Hepatic Metabolic Pathways in Peri-Hatch Broiler Chicks.

Julie A Hicks, Tom E Porter, Nishanth E Sunny +1 more · 2019 · Genes · MDPI · added 2026-04-24
Hepatic fatty acid oxidation of yolk lipoproteins provides the main energy source for chick embryos. Post-hatching these yolk lipids are rapidly exhausted and metabolism switches to a carbohydrate-bas Show more
Hepatic fatty acid oxidation of yolk lipoproteins provides the main energy source for chick embryos. Post-hatching these yolk lipids are rapidly exhausted and metabolism switches to a carbohydrate-based energy source. We recently demonstrated that many microRNAs (miRNAs) are key regulators of hepatic metabolic pathways during this metabolic switching. MiRNAs are small non-coding RNAs that post-transcriptionally regulate gene expression in most eukaryotes. To further elucidate the roles of miRNAs in the metabolic switch, we used delayed feeding for 48 h to impede the hepatic metabolic switch. We found that hepatic expression of several miRNAs including Show less
📄 PDF DOI: 10.3390/genes10040272
FADS1

Heterozygous Ldlr-Deficient Hamster as a Model to Evaluate the Efficacy of PCSK9 Antibody in Hyperlipidemia and Atherosclerosis.

Yue Wu, Ming-Jiang Xu, Zhiyou Cao +9 more · 2019 · International journal of molecular sciences · MDPI · added 2026-04-24
Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a key role in cholesterol homeostasis and atherogenesis. However, there are only limited rodent models, with a functional low-density lipopr Show more
Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a key role in cholesterol homeostasis and atherogenesis. However, there are only limited rodent models, with a functional low-density lipoprotein receptor (LDLR) pathway and cholesteryl ester transfer protein (CETP) to evaluate the drug candidates targeting the PCSK9/LDLR pathway, that are translatable to humans. Here, by using our recently generated LDLR heterozygote ( Show less
📄 PDF DOI: 10.3390/ijms20235936
CETP

Molecular identification and function analysis of bactericidal permeability-increasing protein/LPS-binding protein 1 (BPI/LBP1) from turbot (Scophthalmus maximus).

Shun Zhou, Guangpeng Jiang, Ying Zhu +5 more · 2019 · Fish & shellfish immunology · Elsevier · added 2026-04-24
Bactericidal permeability-increasing protein (BPI) and lipopolysaccharide-binding protein (LBP) play important roles in host antimicrobial defense. In the present study, we identified one isoform of B Show more
Bactericidal permeability-increasing protein (BPI) and lipopolysaccharide-binding protein (LBP) play important roles in host antimicrobial defense. In the present study, we identified one isoform of BPI/LBP gene from turbot (Scophthalmus maximus), designated as SmBPI/LBP1. The full-length cDNA sequence of SmBPI/LBP1 was 1826 bp, which encoding one secreted protein with 480 amino acid residues. Structurally, the SmBPI/LBP1 showed high similarity to its homologs from other vertebrates or invertebrates, which all contained a signal peptide, a BPI/LBP/CETP N-terminal with a LPS-binding domain, and a BPI/LBP/CETP C-terminal domain. The deduced amino acid sequences of SmBPI/LBP1 shared significant similarity to BPI/LBP of Seriola lalandi dorsalis (71%) and Paralichthys olivaceus (69%). Phylogentic analysis further supported that SmBPI/LBP1 act as a new member of vertebrate BPI/LBP family. SmBPI/LBP1 was ubiquitously expressed in all tested tissues, with the highest expression level in spleen tissue. The mRNA expression of SmBPI/LBP1 in spleen and kidney were significantly up-regulated after Vibrio vulnificus challenge. Finally, the recombinant SmBPI/LBP1 showed high affinity to lipopolysaccharide, followed by peptidoglycan and lipoteichoic acid, which is the ubiquitous component of Gram-negative or Gram-positive bacteria. These results indicated that SmBPI/LBP1 probably played important roles in immune response against bacteria infection. Show less
no PDF DOI: 10.1016/j.fsi.2019.02.004
CETP

Ablation of gut microbiota alleviates obesity-induced hepatic steatosis and glucose intolerance by modulating bile acid metabolism in hamsters.

Lulu Sun, Yuanyuan Pang, Xuemei Wang +7 more · 2019 · Acta pharmaceutica Sinica. B · Elsevier · added 2026-04-24
Since metabolic process differs between humans and mice, studies were performed in hamsters, which are generally considered to be a more appropriate animal model for studies of obesity-related metabol Show more
Since metabolic process differs between humans and mice, studies were performed in hamsters, which are generally considered to be a more appropriate animal model for studies of obesity-related metabolic disorders. The modulation of gut microbiota, bile acids and the farnesoid X receptor (FXR) axis is correlated with obesity-induced insulin resistance and hepatic steatosis in mice. However, the interactions among the gut microbiota, bile acids and FXR in metabolic disorders remained largely unexplored in hamsters. In the current study, hamsters fed a 60% high-fat diet (HFD) were administered vehicle or an antibiotic cocktail by gavage twice a week for four weeks. Antibiotic treatment alleviated HFD-induced glucose intolerance, hepatic steatosis and inflammation accompanied with decreased hepatic lipogenesis and elevated thermogenesis in subcutaneous white adipose tissue (sWAT). In the livers of antibiotic-treated hamsters, cytochrome P450 family 7 subfamily B member 1 (CYP7B1) in the alternative bile acid synthesis pathway was upregulated, contributing to a more hydrophilic bile acid profile with increased tauro- Show less
📄 PDF DOI: 10.1016/j.apsb.2019.02.004
CETP

[One case report of 17β-hydroxysteroid dehydrogenase 3 deficiency].

P J Du, Y L Liu, F Liu +3 more · 2019 · Zhonghua nei ke za zhi · added 2026-04-24
no PDF DOI: 10.3760/cma.j.issn.0578-1426.2019.08.012
HSD17B12

[Screening of pathogenic genes in a Chinese familial dilated cardiomyopathy pedigree from Inner Mongolia].

X P Liu, Y B Feng, Y Zeng +7 more · 2019 · Zhonghua xin xue guan bing za zhi · added 2026-04-24
no PDF DOI: 10.3760/cma.j.issn.0253-3758.2019.03.004
MYBPC3

Severe biallelic loss-of-function mutations in nicotinamide mononucleotide adenylyltransferase 2 (NMNAT2) in two fetuses with fetal akinesia deformation sequence.

Marshall Lukacs, Jonathan Gilley, Yi Zhu +9 more · 2019 · Experimental neurology · Elsevier · added 2026-04-24
The three nicotinamide mononucleotide adenylyltransferase (NMNAT) family members synthesize the electron carrier nicotinamide adenine dinucleotide (NAD
📄 PDF DOI: 10.1016/j.expneurol.2019.112961
FADS1

Effects of lysine deficiency or excess on growth and the expression of lipid metabolism genes in slow-growing broilers.

D L Tian, R J Guo, Y M Li +8 more · 2019 · Poultry science · added 2026-04-24
This experiment was conducted to evaluate the effects of lysine deficiency or excess on growth and the expression of lipid metabolism genes in slow-growing birds. A total of 360 one-day-old chicks wer Show more
This experiment was conducted to evaluate the effects of lysine deficiency or excess on growth and the expression of lipid metabolism genes in slow-growing birds. A total of 360 one-day-old chicks were randomly divided into 3 groups, with 6 replicates of 20 birds each. The birds fed the basal diet with a total lysine 0.60% (LL), 1.00% (ML), or 1.40% (HL). The amount of lysine (ML) as the control group, LL and HL as the experimental group, the trial period last 3 wk. The results showed that compared with ML, LL significantly decreased average daily gain and average daily feed intake and remarkably increased feed conversion ratio of birds at 21 day old (P < 0.01), while the above indices in HL had no significant effects (P > 0.05). Besides, LL reduced the pectoral muscle rate (P < 0.01) and decreased the percentage of abdominal fat significantly (P < 0.05). In addition, compared with ML, the expression of fatty acid binding protein 1 (FABP1), acetyl-CoA carboxylase (ACC), malic enzyme (ME), and sterol regulatory element binding protein 1 (SREBP1c) mRNA of liver in LL was significantly decreased (P < 0.05), and the expression of cholesteryl ester transfer protein (CETP) mRNA was significantly increased (P < 0.01), whereas LL had no significant effects on the expression of peroxisome proliferator activated receptor alpha (PPARα) mRNA (P > 0.05). Moreover, compared with ML, HL significantly reduced the expression of FABP1, ACC, ME, SREBP-1c, and PPARα mRNA in the liver (P < 0.05), and had no significant effects on the expression of CETP mRNA (P > 0.05). The results of current research suggest that dietary lysine deficiency could reduce the growth and fat deposition of slow-growing broilers mainly by downregulating the expression of lipid synthesis genes. Show less
no PDF DOI: 10.3382/ps/pez041
CETP

Identification of genetic factors underlying persistent pulmonary hypertension of newborns in a cohort of Chinese neonates.

Xu Liu, Mei Mei, Xiang Chen +8 more · 2019 · Respiratory research · BioMed Central · added 2026-04-24
Persistent pulmonary hypertension of the newborn (PPHN) is a severe clinical problem among neonatal intensive care unit (NICU) patients. The genetic pathogenesis of PPHN is unclear. Only a few genetic Show more
Persistent pulmonary hypertension of the newborn (PPHN) is a severe clinical problem among neonatal intensive care unit (NICU) patients. The genetic pathogenesis of PPHN is unclear. Only a few genetic polymorphisms have been identified in infants with PPHN. Our study aimed to investigate the potential genetic etiology of PPHN. This study recruited PPHN patients admitted to the NICU of the Children's Hospital of Fudan University from Jan 2016 to Dec 2017. Exome sequencing was performed for all patients. Variants in reported PPHN/pulmonary arterial hypertension (PAH)-related genes were assessed. Single nucleotide polymorphism (SNP) association and gene-level analyses were carried out in 74 PPHN cases and 115 non-PPHN controls with matched baseline characteristics. Among the patient cohort, 74 (64.3%) patients were late preterm and term infants (≥ 34 weeks gestation) and 41 (35.7%) were preterm infants (< 34 weeks gestation). Preterm infants with PPHN exhibited low birth weight and a high frequency of bronchopulmonary dysplasia, respiratory distress syndrome (RDS) and mortality. Nine patients (only one preterm infant) were identified as harboring genetic variants, including three with pathogenic/likely pathogenic variants in TBX4 and BMPR2 and six with variants of unknown significance in BMPR2, SMAD9, TGFB1, KCNA5 and TRPC6. Three SNPs (rs192759073, rs1047883 and rs2229589) in CPS1 and one SNP (rs1044008) in NOTCH3 were significantly associated with PPHN (p < 0.05). CPS1 and SMAD9 were identified as risk genes for PPHN (p < 0.05). In this study, we identified genetic variants in PPHN patients, and we reported CPS1, NOTCH3 and SMAD9 as risk genes for late preterm and term PPHN in a single-center Chinese cohort. Our findings provide additional genetic evidence of the pathogenesis of PPHN and new insight into potential strategies for disease treatment. Show less
📄 PDF DOI: 10.1186/s12931-019-1148-1
CPS1

Dysregulated expressions of PTEN, NF-κB, WWP2, p53 and c-Myc in different subtypes of B cell lymphoma and reactive follicular hyperplasia.

Huan Fu, Chenghao Jin, Qingxiu Zhu +4 more · 2019 · American journal of translational research · added 2026-04-24
This study aimed to investigate the value of PTEN, NF-κB, WWP2, p53 and c-Myc expressions in distinguishing B cell lymphomas from reactive follicular hyperplasia (RFH), and their abilities to discrimi Show more
This study aimed to investigate the value of PTEN, NF-κB, WWP2, p53 and c-Myc expressions in distinguishing B cell lymphomas from reactive follicular hyperplasia (RFH), and their abilities to discriminate different B cell lymphoma subtypes. Lymphoma tissue samples were obtained from 30 follicular lymphoma (FL) patients, 30 germinal center B-cell like (GCB) diffuse large B cell lymphoma (DLBCL) patients, 30 non-GCB DLBCL patients and 30 Burkitt's lymphoma (BL) patients. And hyperplasia tissue samples were obtained from and 30 RFH patients. Immunohistochemistry was used to quantify the expressions of PTEN, NF-κB, WWP2, P53 and c-Myc. PTEN expression was elevated in GCB DLBCL and BL compared with RFH, and in GCB DLBCL, non-GCB DLBCL and BL than that in FL; WWP2 expression was higher in FL, GCB DLBCL, non-GCB DLBCL and BL compared with RFH; p53 expression increased in non-GCB DLBCL compared with RFH, and in BL compared with RFH, FL or GCB DLBCL; c-Myc expression was higher in GCB DLBCL, non-GCB DLBCL and BL compared with RFH; c-Myc expression was elevated in GCB DLBCL, non-GCB DLBCL and BL compared with FL. Additionally, PTEN negatively correlated with p53 expression in FL and CGB DLBCL, whereas NF-κB negatively correlated with WWP2 in GCB DLBCL, but positively associated with PTEN in RFH and c-Myc in BL. PTEN, WWP2, p53 and c-Myc expressions might be served as biomarkers for identification of B cell lymphomas from RFH as well as distinguishing different B cell lymphoma subtypes. Show less
no PDF
WWP2

Novel phenotype-genotype correlations of hypertrophic cardiomyopathy caused by mutation in α-actin and myosin-binding protein genes in three unrelated Chinese families.

Qian-Li Yang, Yang-Yang Bian, Bo Wang +5 more · 2019 · Journal of cardiology · Elsevier · added 2026-04-24
The correlations between genotype and phenotype in hypertrophic cardiomyopathy (HCM) have not been established. Mutation of α-actin gene (ACTC1) is a rare cause of HCM. This study aimed to explore nov Show more
The correlations between genotype and phenotype in hypertrophic cardiomyopathy (HCM) have not been established. Mutation of α-actin gene (ACTC1) is a rare cause of HCM. This study aimed to explore novel genotype-phenotype correlations in HCM patients with the variants in ACTC1 and myosin-binding protein (MYBPC3) genes in three unrelated Chinese families. Clinical, electrocardiographic, and echocardiographic examinations were performed in three Han pedigrees. Exon and boarding intron analysis of 96 cardio-disease-related genes was performed using second-generation sequencing on three probands. The candidate variants were validated in 14 available family members and 300 unrelated healthy controls by bi-directional Sanger sequencing. The pathogenicity and conservation were calculated using MutationTaster, PolyPhen-2, SIFT, and Clustal X. Pathogenicity classification of the variants was based on American College of Medical Genetics and Genomics (ACMG) guidelines. Nine members fulfilled diagnostic criteria for HCM with clinical characteristics, electrocardiographic, and echocardiographic findings. Two candidate variants in ACTC1 p.Asp26Asn (ACTC1-D26N) and MYBPC3 p.Arg215Cys (MYBPC3-R215C) were identified in patients. Only ACTC1-D26N strongly co-segregated with the HCM phenotype. Seven patients who harbored variant ACTC-D26N only were diagnosed with non-obstructive HCM, and four of these patients exhibited a triphasic left ventricular (LV) filling pattern. Two patients carrying both ACTC1-D26N and MYBPC3-R215C variants showed a higher LV outflow tract pressure gradient. Bioinformatics analysis revealed that the two variants were deleterious and highly conserved across species. According to ACMG guidelines, ACTC1-D26N is classified as a likely pathogenic mutation. The second variation MYBPC3-R215C may function as a genetic modifier, which remains uncertain here. Novel p.(Asp26Asn) mutation of ACTC1 was associated with HCM phenotype, and the penetrance is extremely high (∼81.8%) in adults. The second variation, MYBPC3-R215C may function as a genetic modifier, which remains uncertain here. Show less
no PDF DOI: 10.1016/j.jjcc.2018.09.005
MYBPC3

A Compound Heterozygous Mutation of Lipase Maturation Factor 1 is Responsible for Hypertriglyceridemia of a Patient.

Yihui Liu, Jiang Xu, Wanyun Tao +2 more · 2019 · Journal of atherosclerosis and thrombosis · added 2026-04-24
Dyslipidemia is the most common lipid metabolism disorder in humans, and its etiology remains elusive. Hypertriglyceridemia (HTG) is a type of dyslipidemia that contributes to atherosclerosis and coro Show more
Dyslipidemia is the most common lipid metabolism disorder in humans, and its etiology remains elusive. Hypertriglyceridemia (HTG) is a type of dyslipidemia that contributes to atherosclerosis and coronary heart disease. Previous studies have demonstrated that mutations in lipoprotein lipase (LPL), apolipoprotein CII (APOC2), apolipoprotein AV (APOA5), glycosylphosphatidylinositol anchored high-density lipoprotein-binding protein 1 (GPIHBP1), lipase maturation factor 1(LMF1), and glycerol-3 phosphate dehydrogenase 1 (GPD1) are responsible for HTG by using genomic microarrays and next-generation sequencing. The aim of this study was to identify genetic lesions in patients with HTG. Our study included a family of seven members from Jiangsu province across three generations. The proband was diagnosed with severe HTG, with a plasma triglyceride level of 38.70 mmol/L. Polymerase chain reaction (PCR) and Sanger sequencing were performed to explore the possible causative gene mutations for this patient. Furthermore, we measured the post-heparin LPL and hepatic lipase (HL) activities using an antiserum inhibition method. A compound heterozygous mutation in the LMF1 gene (c.257C＞T/p.P86L and c.1184C＞T/p.T395I) was identified and co-segregated with the affected patient in this family. Both mutations were predicted to be deleterious by three bioinformatics programs (Polymorphism Phenotyping-2, Sorting Intolerant From Tolerant, and MutationTaster). The levels of the plasma post-heparin LPL and HL activities in the proband (57 and 177 mU/mL) were reduced to 24% and 75%, respectively, compared with those assayed in the control subject with normal plasma triglycerides. A compound heterozygous mutation of LMF1 was identified in the presenting patient with severe HTG. These findings expand on the spectrum of LMF1 mutations and contribute to the genetic diagnosis and counseling of families with HTG. Show less
📄 PDF DOI: 10.5551/jat.44537
APOA5

Comprehensive Analysis of Aberrantly Expressed ceRNA network in gastric cancer with and without

Aining Chu, Jingwei Liu, Yuan Yuan +1 more · 2019 · Journal of Cancer · added 2026-04-24
📄 PDF DOI: 10.7150/jca.27803
APOC3

Precision Health Resource of Control iPSC Lines for Versatile Multilineage Differentiation.

Matthew R Hildebrandt, Miriam S Reuter, Wei Wei +25 more · 2019 · Stem cell reports · Elsevier · added 2026-04-24
Induced pluripotent stem cells (iPSC) derived from healthy individuals are important controls for disease-modeling studies. Here we apply precision health to create a high-quality resource of control Show more
Induced pluripotent stem cells (iPSC) derived from healthy individuals are important controls for disease-modeling studies. Here we apply precision health to create a high-quality resource of control iPSCs. Footprint-free lines were reprogrammed from four volunteers of the Personal Genome Project Canada (PGPC). Multilineage-directed differentiation efficiently produced functional cortical neurons, cardiomyocytes and hepatocytes. Pilot users demonstrated versatility by generating kidney organoids, T lymphocytes, and sensory neurons. A frameshift knockout was introduced into MYBPC3 and these cardiomyocytes exhibited the expected hypertrophic phenotype. Whole-genome sequencing-based annotation of PGPC lines revealed on average 20 coding variants. Importantly, nearly all annotated PGPC and HipSci lines harbored at least one pre-existing or acquired variant with cardiac, neurological, or other disease associations. Overall, PGPC lines were efficiently differentiated by multiple users into cells from six tissues for disease modeling, and variant-preferred healthy control lines were identified for specific disease settings. Show less
no PDF DOI: 10.1016/j.stemcr.2019.11.003
MYBPC3

Multi-omics Analysis of Microenvironment Characteristics and Immune Escape Mechanisms of Hepatocellular Carcinoma.

Wenli Li, Huimei Wang, Zhanzhong Ma +4 more · 2019 · Frontiers in oncology · Frontiers · added 2026-04-24
The immune environment in primary tumor has a profound impact on immunotherapy. However, the clinical relevance of immune environment in hepatocellular carcinoma (HCC) is largely unknown. Here, the im Show more
The immune environment in primary tumor has a profound impact on immunotherapy. However, the clinical relevance of immune environment in hepatocellular carcinoma (HCC) is largely unknown. Here, the immune profile and its clinical response in HCC were investigated. The gene expression profiles of 569 HCCs from three cohorts (The Cancer Genome Atlas, TCGA, Show less
📄 PDF DOI: 10.3389/fonc.2019.01019
AXIN1

Engineering Microbial Consortia for High-Performance Cellulosic Hydrolyzates-Fed Microbial Fuel Cells.

Feng Li, Xingjuan An, Deguang Wu +9 more · 2019 · Frontiers in microbiology · Frontiers · added 2026-04-24
Microbial fuel cells (MFCs) are eco-friendly bio-electrochemical reactors that use exoelectrogens as biocatalyst for electricity harvest from organic biomass, which could also be used as biosensors fo Show more
Microbial fuel cells (MFCs) are eco-friendly bio-electrochemical reactors that use exoelectrogens as biocatalyst for electricity harvest from organic biomass, which could also be used as biosensors for long-term environmental monitoring. Glucose and xylose, as the primary ingredients from cellulose hydrolyzates, is an appealing substrate for MFC. Nevertheless, neither xylose nor glucose can be utilized as carbon source by well-studied exoelectrogens such as Show less
📄 PDF DOI: 10.3389/fmicb.2019.00409
CPS1

Whole-genome resequencing from bulked-segregant analysis reveals gene set based association analyses for the Vibrio anguillarum resistance of turbot (Scophthalmus maximus).

Kai Zhang, Miao Han, Yuxiang Liu +6 more · 2019 · Fish & shellfish immunology · Elsevier · added 2026-04-24
Many achievements have been made to develop quantitative trait loci (QTLs) and gene-associated single nucleotide polymorphisms (SNPs) to facilitate practical marker-assisted selection (MAS) in aquatic Show more
Many achievements have been made to develop quantitative trait loci (QTLs) and gene-associated single nucleotide polymorphisms (SNPs) to facilitate practical marker-assisted selection (MAS) in aquatic animals. However, the systematic studies of SNPs associated with extreme threshold traits were poor in populations lacking of parental genomic information. Coupling next generation sequencing with bulked segregant analysis (BSA) should allow identification of numerous associated SNPs with extreme phenotypes. In the present study, using combination of SNP frequency difference and Euclidean distance, we conducted linkage analysis of SNPs located in genes involved in immune responses, and identified markers associated with Vibrio anguillarum resistance in turbot (Scophthalmus maximus). A total of 221 SNPs was found as candidate SNPs between resistant and susceptible individuals. Among these SNPs, 35 loci located in immune related genes were genotyped in verification population and 7 of them showed significant association with V. anguillarum resistance in both alleles and genotypes (P < 0.05). Among these 7 genes, PIK3CA-like, CYLD, VCAM1, RhoB and RhoGEF are involved in PI3K/Akt/mTOR pathway and NF-κB pathway, which influence the efficiency of bacteria entering the host and inflammation. SNP-SNP interaction analysis was performed by generalized multifactor dimensionality reduction (GMDR). The combination of SNP loci in RhoB, PIK3CA-like and ADCY3 showed a significant effect on V. anguillarum resistance with the verification rate in the sequencing population up to 70.8%. Taken all, our findings demonstrated the feasibility of BSA-seq approach in identifying genes responsible for the extreme phenotypes and will aid in performing MAS in turbot. Show less
no PDF DOI: 10.1016/j.fsi.2019.02.041
ADCY3

Polymorphisms of four candidate genes and their correlations with growth traits in blue fox (Alopex lagopus).

Dong-Yue Yu, Ru-Zi Wu, Yao Zhao +4 more · 2019 · Gene · Elsevier · added 2026-04-24
To improve the accuracy and genetic progress of blue fox breeding, the relationships between genetic polymorphisms and growth and reproductive traits of the blue fox were investigated. MC4R, MC3R, INH Show more
To improve the accuracy and genetic progress of blue fox breeding, the relationships between genetic polymorphisms and growth and reproductive traits of the blue fox were investigated. MC4R, MC3R, INHA and INHBA were selected as candidate genes for molecular evolution and statistical analyses. Single-factor variance analyses showed that the MC4R (g.267C > T, g.423C > T, and g.731C > A) and MC3R (g.677C > T) genotypes had significant impacts on body weight, chest circumference, abdominal perimeter and body mass index (BMI) (P < 0.05) in blue fox. The MC4R and MC3R combined genotypes had significant effects on the body weight and abdominal circumference. The different genotypes of INHA g.75G > A had significant effects on female fecundity, whereas the different genotypes of INHBA g.404G > T and g.467G > T and the INHA and INHBA combined genotypes had significant effects on male fecundity. The proteins encoded by the open reading frames (ORFs) of different polymorphic loci were predicted and analysed. The aims of this study were to identify genetic markers related to growth and reproduction in the blue fox and to provide an efficient, economical and accurate theoretical approach for auxiliary fox breeding. Show less
no PDF DOI: 10.1016/j.gene.2019.143987
MC4R

Novel Neonatal Variants of the Carbamoyl Phosphate Synthetase 1 Deficiency: Two Case Reports and Review of Literature.

Beibei Yan, Chao Wang, Kaihui Zhang +6 more · 2019 · Frontiers in genetics · Frontiers · added 2026-04-24
Carbamoyl phosphate synthetase I (CPS1) deficiency (CPS1D), is a rare autosomal recessive disorder, characterized by life-threatening hyperammonemia. In this study, we presented the detailed clinical Show more
Carbamoyl phosphate synthetase I (CPS1) deficiency (CPS1D), is a rare autosomal recessive disorder, characterized by life-threatening hyperammonemia. In this study, we presented the detailed clinical features and genetic analysis of two patients with neonatal-onset CPS1D carrying two compound heterozygous variants of c.1631C > T (p.T544M)/c.1981G > T (p.G661C), and c.2896G > T (p.E966X)/c622-3C > G in Show less
📄 PDF DOI: 10.3389/fgene.2019.00718
CPS1

Whole Genome Analyses of Chinese Population and De Novo Assembly of A Northern Han Genome.

Zhenglin Du, Liang Ma, Hongzhu Qu +27 more · 2019 · Genomics, proteomics & bioinformatics · Elsevier · added 2026-04-24
To unravel the genetic mechanisms of disease and physiological traits, it requires comprehensive sequencing analysis of large sample size in Chinese populations. Here, we report the primary results of Show more
To unravel the genetic mechanisms of disease and physiological traits, it requires comprehensive sequencing analysis of large sample size in Chinese populations. Here, we report the primary results of the Chinese Academy of Sciences Precision Medicine Initiative (CASPMI) project launched by the Chinese Academy of Sciences, including the de novo assembly of a northern Han reference genome (NH1.0) and whole genome analyses of 597 healthy people coming from most areas in China. Given the two existing reference genomes for Han Chinese (YH and HX1) were both from the south, we constructed NH1.0, a new reference genome from a northern individual, by combining the sequencing strategies of PacBio, 10× Genomics, and Bionano mapping. Using this integrated approach, we obtained an N50 scaffold size of 46.63 Mb for the NH1.0 genome and performed a comparative genome analysis of NH1.0 with YH and HX1. In order to generate a genomic variation map of Chinese populations, we performed the whole-genome sequencing of 597 participants and identified 24.85 million (M) single nucleotide variants (SNVs), 3.85 M small indels, and 106,382 structural variations. In the association analysis with collected phenotypes, we found that the T allele of rs1549293 in KAT8 significantly correlated with the waist circumference in northern Han males. Moreover, significant genetic diversity in MTHFR, TCN2, FADS1, and FADS2, which associate with circulating folate, vitamin B12, or lipid metabolism, was observed between northerners and southerners. Especially, for the homocysteine-increasing allele of rs1801133 (MTHFR 677T), we hypothesize that there exists a "comfort" zone for a high frequency of 677T between latitudes of 35-45 degree North. Taken together, our results provide a high-quality northern Han reference genome and novel population-specific data sets of genetic variants for use in the personalized and precision medicine. Show less
📄 PDF DOI: 10.1016/j.gpb.2019.07.002
FADS1

Axin-1 binds to Caveolin-1 to regulate the LPS-induced inflammatory response in AT-I cells.

Yongjuan Zhang, Haihua Luo, Xuejun Lv +5 more · 2019 · Biochemical and biophysical research communications · Elsevier · added 2026-04-24
Caveolin-1 has been reported to play an important role in the pathogenesis of acute respiratory distress syndrome (ARDS). This study was designed to identify Caveolin-1-interacting proteins to reveal Show more
Caveolin-1 has been reported to play an important role in the pathogenesis of acute respiratory distress syndrome (ARDS). This study was designed to identify Caveolin-1-interacting proteins to reveal the molecular mechanisms of ARDS. Yeast two-hybrid screening was performed using Caveolin-1 as the bait, and Axin-1 was identified as a binding partner for Caveolin-1. Co-immunoprecipitation demonstrated that the binding domains were located in the N-terminal region (1-100 aa) of Caveolin-1 and the C-terminal region (710-797 aa) of Axin-1. Caveolin-1 gene knockout or Axin-1 knockdown significantly decreased the levels of TNF-α and IL-6 in the supernatants of alveolar type I (AT-I) epithelial cells treated with LPS. Disrupting the interaction between Caveolin-1 and Axin-1 using CRISPR/Cas9 technology led to a significant increase in TNF-α and IL-6 from AT-I cells, along with a significant reduction in β-catenin expression. In conclusion, Axin-1 functions as an adaptor of Caveolin-1 and affects the production of inflammatory cytokines in AT-I cells challenged with LPS via β-catenin-mediated negative regulation. Show less
no PDF DOI: 10.1016/j.bbrc.2019.03.153
AXIN1

Genotype-by-environment interaction of fertility traits in Danish Holstein cattle using a single-step genomic reaction norm model.

Zhe Zhang, Morten Kargo, Aoxing Liu +3 more · 2019 · Heredity · Nature · added 2026-04-24
Genotype-by-environment (G × E) interactions could play an important role in cattle populations, and it should be considered in breeding programmes to select the best sires for different environments. Show more
Genotype-by-environment (G × E) interactions could play an important role in cattle populations, and it should be considered in breeding programmes to select the best sires for different environments. The objectives of this study were to study G × E interactions for female fertility traits in the Danish Holstein dairy cattle population using a reaction norm model (RNM), and to detect the particular genomic regions contributing to the performance of these traits and the G × E interactions. In total 4534 bulls were genotyped by an Illumina BovineSNP50 BeadChip. An RNM with a pedigree-based relationship matrix and a pedigree-genomic combined relationship matrix was used to explore the existence of G × E interactions. In the RNM, the environmental gradient (EG) was defined as herd effect. Further, the genomic regions affecting interval from calving to first insemination (ICF) and interval from first to last insemination (IFL) were detected using single-step genome-wide association study (ssGWAS). The genetic correlations between extreme EGs indicated that G × E interactions were sizable for ICF and IFL. The genomic RNM (pedigree-genomic combined relationship matrix) had higher prediction accuracy than the conventional RNM (pedigree-based relationship matrix). The top genomic regions affecting the slope of the reaction norm included immunity-related genes (IL17, IL17F and LIF), and growth-related genes (MC4R and LEP), while the top regions influencing the intercept of the reaction norm included fertility-related genes such as EREG, AREG and SMAD4. In conclusion, our findings validated the G × E interactions for fertility traits across different herds and were helpful in understanding the genetic background of G × E interactions for these traits. Show less
📄 PDF DOI: 10.1038/s41437-019-0192-4
MC4R

Cell division cycle 20 (CDC20) drives prostate cancer progression via stabilization of β-catenin in cancer stem-like cells.

Qin Zhang, Hai Huang, Ao Liu +7 more · 2019 · EBioMedicine · Elsevier · added 2026-04-24
Cell division cycle 20 (CDC20) is frequently overexpressed in malignant tumours and involved in the differentiation process of hematopoietic stem cells. However, the role of CDC20 in prostate cancer s Show more
Cell division cycle 20 (CDC20) is frequently overexpressed in malignant tumours and involved in the differentiation process of hematopoietic stem cells. However, the role of CDC20 in prostate cancer stem-like cells (CSCs) remains poorly understood. The expression of CDC20, CD44, β-catenin were examined in prostate cancer specimens by immunohistochemistry assay, the role of CDC20 on the stem-like properties of prostate CSCs was accessed by real-time quantitive PCR, spheroid formation, in vitro and in vivo limiting dilution assay. CDC20 was associated with malignant progression of prostate cancer, the patients with both high expression CDC20 and CD44 or β-catenin were associated with more aggressive clinicopathological features and poor prognosis. CDC20 was usually enriched in CD44 Our results indicated that CDC20 maintains the self-renewal ability of CD44 Show less
📄 PDF DOI: 10.1016/j.ebiom.2019.03.032
AXIN1

[Analysis of EXT1 and EXT2 gene mutations in two Chinese pedigrees affected with hereditary multiple exostosis].

Ying Bai, Ning Liu, Shuang Hu +2 more · 2019 · Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics · added 2026-04-24
To detect EXT1 and EXT2 gene mutations in two pedigrees affected with hereditary multiple exostosis (HME). The coding regions and exon/intron boundaries of the EXT1 and EXT2 genes were analyzed by tar Show more
To detect EXT1 and EXT2 gene mutations in two pedigrees affected with hereditary multiple exostosis (HME). The coding regions and exon/intron boundaries of the EXT1 and EXT2 genes were analyzed by targeted next-generation sequencing (NGS). Suspected mutations were confirmed by Sanger sequencing of the probands, their family members and 200 unrelated healthy controls. Gross deletion was confirmed by quantitative PCR (qPCR) analysis and multiple ligation-dependent probe amplification (MLPA) analysis. Two mutations were detected in the pedigrees, which included EXT2 gene c.337₃₃₈insG mutation in pedigree 1 and deletion of entire EXT1 in pedigree 2. Analysis of sequencing data revealed that a novel heterozygous mutation (c.337₃₃₈insG) in EXT2 gene in proband 1 and his father. The same mutation was not found among healthy family members and 200 unrelated healthy controls. As shown by NGS and MLPA analysis, proband 2 carried a heterozygous deletion of entire EXT1 gene. The same deletion was also found in her mother by qPCR. Mutations of the EXT1 and EXT2 genes probably underlie the HME in both pedigrees. NGS combined with Sanger sequencing, qPCR and MLPA is effective for attaining the diagnosis. Show less
no PDF DOI: 10.3760/cma.j.issn.1003-9406.2019.05.009
EXT1

Disentangling the genetics of lean mass.

David Karasik, M Carola Zillikens, Yi-Hsiang Hsu +154 more · 2019 · The American journal of clinical nutrition · Oxford University Press · added 2026-04-24
David Karasik, M Carola Zillikens, Yi-Hsiang Hsu, Ali Aghdassi, Kristina Akesson, Najaf Amin, Inês Barroso, David A Bennett, Lars Bertram, Murielle Bochud, Ingrid B Borecki, Linda Broer, Aron S Buchman, Liisa Byberg, Harry Campbell, Natalia Campos-Obando, Jane A Cauley, Peggy M Cawthon, John C Chambers, Zhao Chen, Nam H Cho, Hyung Jin Choi, Wen-Chi Chou, Steven R Cummings, Lisette C P G M de Groot, Phillip L De Jager, Ilja Demuth, Luda Diatchenko, Michael J Econs, Gudny Eiriksdottir, Anke W Enneman, Joel Eriksson, Johan G Eriksson, Karol Estrada, Daniel S Evans, Mary F Feitosa, Mao Fu, Christian Gieger, Harald Grallert, Vilmundur Gudnason, Launer J Lenore, Caroline Hayward, Albert Hofman, Georg Homuth, Kim M Huffman, Lise B Husted, Thomas Illig, Erik Ingelsson, Till Ittermann, John-Olov Jansson, Toby Johnson, Reiner Biffar, Joanne M Jordan, Antti Jula, Magnus Karlsson, Kay-Tee Khaw, Tuomas O Kilpeläinen, Norman Klopp, Jacqueline S L Kloth, Daniel L Koller, Jaspal S Kooner, William E Kraus, Stephen Kritchevsky, Zoltán Kutalik, Teemu Kuulasmaa, Johanna Kuusisto, Markku Laakso, Jari Lahti, Thomas Lang, Bente L Langdahl, Markus M Lerch, Joshua R Lewis, Christina Lill, Lars Lind, Cecilia Lindgren, Yongmei Liu, Gregory Livshits, Östen Ljunggren, Ruth J F Loos, Mattias Lorentzon, Jian'an Luan, Robert N Luben, Ida Malkin, Fiona E McGuigan, Carolina Medina-Gomez, Thomas Meitinger, Håkan Melhus, Dan Mellström, Karl Michaëlsson, Braxton D Mitchell, Andrew P Morris, Leif Mosekilde, Maria Nethander, Anne B Newman, Jeffery R O'Connell, Ben A Oostra, Eric S Orwoll, Aarno Palotie, Munro Peacock, Markus Perola, Annette Peters, Richard L Prince, Bruce M Psaty, Katri Räikkönen, Stuart H Ralston, Samuli Ripatti, Fernando Rivadeneira, John A Robbins, Jerome I Rotter, Igor Rudan, Veikko Salomaa, Suzanne Satterfield, Sabine Schipf, Chan Soo Shin, Albert V Smith, Shad B Smith, Nicole Soranzo, Timothy D Spector, Alena Stancáková, Kari Stefansson, Elisabeth Steinhagen-Thiessen, Lisette Stolk, Elizabeth A Streeten, Unnur Styrkarsdottir, Karin M A Swart, Patricia Thompson, Cynthia A Thomson, Gudmar Thorleifsson, Unnur Thorsteinsdottir, Emmi Tikkanen, Gregory J Tranah, André G Uitterlinden, Cornelia M Van Duijn, Natasja M van Schoor, Liesbeth Vandenput, Peter Vollenweider, Henry Völzke, Jean Wactawski-Wende, Mark Walker, Nicholas J Wareham, Dawn Waterworth, Michael N Weedon, H-Erich Wichmann, Elisabeth Widen, Frances M K Williams, James F Wilson, Nicole C Wright, Laura M Yerges-Armstrong, Lei Yu, Weihua Zhang, Jing Hua Zhao, Yanhua Zhou, Carrie M Nielson, Tamara B Harris, Serkalem Demissie, Douglas P Kiel, Claes Ohlsson Show less
Lean body mass (LM) plays an important role in mobility and metabolic function. We previously identified five loci associated with LM adjusted for fat mass in kilograms. Such an adjustment may reduce Show more
Lean body mass (LM) plays an important role in mobility and metabolic function. We previously identified five loci associated with LM adjusted for fat mass in kilograms. Such an adjustment may reduce the power to identify genetic signals having an association with both lean mass and fat mass. To determine the impact of different fat mass adjustments on genetic architecture of LM and identify additional LM loci. We performed genome-wide association analyses for whole-body LM (20 cohorts of European ancestry with n = 38,292) measured using dual-energy X-ray absorptiometry) or bioelectrical impedance analysis, adjusted for sex, age, age2, and height with or without fat mass adjustments (Model 1 no fat adjustment; Model 2 adjustment for fat mass as a percentage of body mass; Model 3 adjustment for fat mass in kilograms). Seven single-nucleotide polymorphisms (SNPs) in separate loci, including one novel LM locus (TNRC6B), were successfully replicated in an additional 47,227 individuals from 29 cohorts. Based on the strengths of the associations in Model 1 vs Model 3, we divided the LM loci into those with an effect on both lean mass and fat mass in the same direction and refer to those as "sumo wrestler" loci (FTO and MC4R). In contrast, loci with an impact specifically on LM were termed "body builder" loci (VCAN and ADAMTSL3). Using existing available genome-wide association study databases, LM increasing alleles of SNPs in sumo wrestler loci were associated with an adverse metabolic profile, whereas LM increasing alleles of SNPs in "body builder" loci were associated with metabolic protection. In conclusion, we identified one novel LM locus (TNRC6B). Our results suggest that a genetically determined increase in lean mass might exert either harmful or protective effects on metabolic traits, depending on its relation to fat mass. Show less
no PDF DOI: 10.1093/ajcn/nqy272
MC4R

FAM83A signaling induces epithelial-mesenchymal transition by the PI3K/AKT/Snail pathway in NSCLC.

Fengrui Zhou, Jianxiong Geng, Shanqi Xu +6 more · 2019 · Aging · Impact Journals · added 2026-04-24
Family with sequence similarity 83, member A (FAM83A), as a potential tumor promoter, was reported to contribute to the progression of several malignant tumors. However, the significance of FAM83A in Show more
Family with sequence similarity 83, member A (FAM83A), as a potential tumor promoter, was reported to contribute to the progression of several malignant tumors. However, the significance of FAM83A in invasion and metastasis of non-small cell lung cancer (NSCLC) remains largely unknown. In this study, we found that FAM83A expression was significantly increased in NSCLC tissues. High expression of FAM83A was positively associated with tumor metastasis and poor survival of NSCLC patients. Functional experiments revealed that FAM83A knockdown could suppress NSCLC cell migration and invasion both Show less
no PDF DOI: 10.18632/aging.102163
SNAI1

Promoter DNA hypermethylation - Implications for Alzheimer's disease.

Yiyuan Liu, Minghui Wang, Edoardo M Marcora +2 more · 2019 · Neuroscience letters · Elsevier · added 2026-04-24
Recent methylome-wide association studies (MWAS) in humans have solidified the concept that aberrant DNA methylation is associated with Alzheimer's disease (AD). We summarize these findings to improve Show more
Recent methylome-wide association studies (MWAS) in humans have solidified the concept that aberrant DNA methylation is associated with Alzheimer's disease (AD). We summarize these findings to improve the understanding of mechanisms governing DNA methylation pertinent to transcriptional regulation, with an emphasis of AD-associated promoter DNA hypermethylation, which establishes an epigenetic barrier for transcriptional activation. By considering brain cell type specific expression profiles that have been published only for non-demented individuals, we detail functional activities of selected neuron, microglia, and astrocyte-enriched genes (AGAP2, DUSP6 and GPR37L1, respectively), which are DNA hypermethylated at promoters in AD. We highlight future directions in MWAS including experimental confirmation, functional relevance to AD, cell type-specific temporal characterization, and mechanism investigation. Show less
📄 PDF DOI: 10.1016/j.neulet.2019.134403
DUSP6