👤 Mingsong Liu

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3182
Articles
1983
Name variants
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, 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, Shi-guo 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

LINGO1, C7orf31 and VEGFA are prognostic genes of primary glioblastoma: analysis of gene expression microarray.

S Liu, Y Xu, S Zhang · 2018 · Neoplasma · added 2026-04-24
Glioblastoma is the most prevalent malignant glioma in WHO grade IV and its median overall survival is 12-15 months. This study identifies the primary glioblastoma. prognostic genes. Gene expression d Show more
Glioblastoma is the most prevalent malignant glioma in WHO grade IV and its median overall survival is 12-15 months. This study identifies the primary glioblastoma. prognostic genes. Gene expression data in primary glioblastomas with short-term (36 months, N=23) overall survival were downloaded from Gene Expression Omnibus (GSE53733). Limma determined the differentially expressed genes (DEGs) between different groups (|log2 fold change| ≥0.5 and p-value DEG's degree, betweenness, sub-graph and closeness centralities. Long- term/short-term survival-related DEGs were defined as those with increased/decreased expression values and survival time. The following DEGs were identified; 161 between intermediate and short-term glioblastomas, 465 between long-term and short-term and 624 between long-term and intermediate tumors. The common FLRT1 and LINGO1 up-regulated DEGs and common down-regulated C7orf31 were identified in these three DEG sets. PPI networks were established, and VEGFA was the key DEG in each PPI network. The short-term survival-related DEGs were enriched in 3 cancer-related pathways. Moreover, FLRT1 and LINGO1 were long-term survival-related DEGs and C7orf31 and VEGFA were short-term survival DEGs. LINGO1, C7orf31, and VEGFA were confirmed using a further dataset, and we therefore conclude that LINGO1 might be a positive primary glioblastoma prognostic gene and C7orf31 and VEGFA might be negative prognosticators. Show less
no PDF DOI: 10.4149/neo_2018_170722N496
LINGO1

Genetic Variants Associated With Neurodegenerative Diseases Regulate Gene Expression in Immune Cell CD14+ Monocytes.

Jing-Yi Sun, Ya-Jun Hou, Yan Zhang +4 more · 2018 · Frontiers in genetics · Frontiers · added 2026-04-24
Until now, large-scale genome-wide association studies have identified 94 genes associated with Alzheimer's disease, Parkinson's disease, and multiple sclerosis. Expression quantitative trait locus (e Show more
Until now, large-scale genome-wide association studies have identified 94 genes associated with Alzheimer's disease, Parkinson's disease, and multiple sclerosis. Expression quantitative trait locus (eQTL) analysis showed that six genetic variants around six of these 94 genes could drive both disease susceptibility and altered expression of six nearby genes including CD33 (rs3865444), PILRB (rs1476679), NUP160 (rs10838725), LRRK2 (rs76904798), RGS1 (rs1323292), and METTL21B (rs701006). However, two of these six genetic variants rs1476679 and rs76904798 variants could regulate the expression of PILRB and LRRK2 only in the human monocyte-derived microglia-like (MDMi) cells, but not in human peripheral blood monocytes. Here, we aim to verify these findings using another two eQTL datasets in human peripheral blood immune cell CD14+ monocytes. The results that showed that rs1476679 and rs76904798 variants or their proxy variants could significantly regulate the expression of PILRB and LRRK2 in immune cell CD14+ monocytes and human peripheral blood. We believe that these findings provide important supplementary information about the regulatory mechanisms by which both variants influence PILRB and LRRK2 gene expression and neurodegenerative disease risk. Show less
no PDF DOI: 10.3389/fgene.2018.00666
NUP160

[The effect of lnc-RAP3 on 3T3-L1 preadipocyte differentiation in mouse].

Huan Li, Jin Chuan Feng, Gui Lin Li +3 more · 2018 · Yi chuan = Hereditas · added 2026-04-24
The long non-coding RNAs (lncRNAs) are a type of RNAs with more than 200nt in length and without any long open reading frame, but often have mRNA structural features. They can regulate the expression Show more
The long non-coding RNAs (lncRNAs) are a type of RNAs with more than 200nt in length and without any long open reading frame, but often have mRNA structural features. They can regulate the expression of target genes in different manners at the transcriptional and post-transcriptional levels. In recent years, various studies demonstrated that lncRNAs play crucial roles in adipogenesis. The long non-coding RNA, lnc-RAP3, located on the mouse chromosome 17, possesses a significantly differential expression pattern during mouse adipocyte differentiation; but its specific biological function (s) remains unclear. To investigate the effect of lnc-RAP3 on adipogenesis in the mouse 3T3-L1 preadipocytes, we first constructed a eukaryotic expression vector pcDNA3.1-RAP3. pcDNA3.1-RAP3 and synthetic RAP3-siRNAs were transfected individually into 3T3-L1 preadipocytes by Lipofectamine Show less
no PDF DOI: 10.16288/j.yczz.18-053
APOA5

Circular RNA regulatory network reveals cell-cell crosstalk in acute myeloid leukemia extramedullary infiltration.

Chengfang Lv, Lili Sun, Zhibo Guo +8 more · 2018 · Journal of translational medicine · BioMed Central · added 2026-04-24
Acute myeloid leukemia can develop as myoblasts infiltrate into organs and tissues anywhere other than the bone marrow, which called extramedullary infiltration (EMI), indicating a poor prognosis. Cir Show more
Acute myeloid leukemia can develop as myoblasts infiltrate into organs and tissues anywhere other than the bone marrow, which called extramedullary infiltration (EMI), indicating a poor prognosis. Circular RNAs (circRNAs) are a novel class of non-coding RNAs that feature covalently closed continuous loops, suggesting their potential as micro RNA (miRNA) "sponges" that can participate in biological processes and pathogenesis. However, investigations on circRNAs in EMI were conducted rarely. In this study, the overall alterations of circRNAs and their regulatory network between EMI and non-EMI AML were delineated. CircRNA and whole genome microarrays derived from EMI and non-EMI AML bone marrow mononuclear cells were carried out. Functional analysis was performed via Gene Ontology and KEGG test methods. The speculated functional roles of circRNAs were based on mRNAs and predicted miRNAs that played intermediate roles. Integrated bioinformatic analysis was conducted to further characterize the circRNA/miRNA/mRNA regulatory network and identify the functions of distinct circRNAs. The Cancer Genome Atlas (TCGA) data were acquired to evaluate the poor prognosis of distinct target genes of circRNAs. Reverse transcription-quantitative polymerase chain reaction was conducted to identify the expression of has_circRNA₀₀₀₄₅₂₀. Connectivity map (CMap) analysis was further performed to predict potential therapeutic agents for EMI. 253 circRNAs and 663 genes were upregulated and 259 circRNAs and 838 genes were downregulated in EMI compared to non-EMI AML samples. GO pathways were enriched in progress including cell adhesion (GO:0030155; GO:0007155), migration (GO:0016477; GO:0030334), signal transduction (GO:0009966; GO:0007165) and cell-cell communication. Overlapping circRNAs envolved in pathways related to regulate cell-cell crosstalk, 17 circRNAs were chosen based on their putative roles. 7 target genes of 17 circRNAs (LRRK1, PLXNB2, OLFML2A, LYPD5, APOL3, ZNF511, and ASB2) indicated a poor prognosis, while overexpression of PAPLN and NRXN3 indicated a better one based on data from TCGA. LY-294002, trichostatin A and SB-202190 were identified as therapeutic candidates for EMI by the CMap analysis. Taken together, this study reveals the overall alterations of circRNA and mRNA involved in EMI and suggests potential circRNAs may act as biomarkers and targets for early diagnosis and treatment of EMI. Show less
no PDF DOI: 10.1186/s12967-018-1726-x
NRXN3

Screening and identification of potential protein biomarkers for evaluating the efficacy of intensive therapy in pulmonary tuberculosis.

Ting-Ting Jiang, Li-Ying Shi, Jing Chen +9 more · 2018 · Biochemical and biophysical research communications · Elsevier · added 2026-04-24
This research aimed to discover potential biomarkers for evaluating the therapeutic efficacy of intensive therapy in pulmonary tuberculosis (TB). Protein profiles in 2-months intensively treated TB pa Show more
This research aimed to discover potential biomarkers for evaluating the therapeutic efficacy of intensive therapy in pulmonary tuberculosis (TB). Protein profiles in 2-months intensively treated TB patients, untreated TB patients, and healthy controls were investigated with iTRAQ-2DLC-MS/MS technique. 71 differential proteins were identified in 2-months intensively treated TB patients. Significant differences in complement component C7 (CO7), apolipoprotein A-IV (APOA4), apolipoprotein C-II (APOC2), and angiotensinogen (ANGT) were found by ELISA validation. CO7 and ANGT were also found significantly different in sputum negative patients, compared with sputum positive patients after intensive treatment. Clinical analysis showed that after 2-months intensive treatment several indicators were significantly changed, and the one-year cure rate of sputum negative patients were significantly higher than sputum positive patients. Diagnostic models consisting of APOC2, CO7 and APOA4 were established to distinguish intensively treated TB patients from untreated TB patients and healthy controls with the AUC value of 0.910 and 0.935. Meanwhile, ANGT and CO7 were combined to identify sputum negative and sputum positive TB patients after intensive treatment with 89.36% sensitivity, 71.43% specificity, and the AUC value of 0.853. The results showed that APOC2, CO7, APOA4, and ANGT may be potential biomarkers for evaluating the efficacy of intensive anti-TB therapy. Show less
no PDF DOI: 10.1016/j.bbrc.2018.06.147
APOA4

Apolipoprotein A5 gene polymorphisms are associated with non-alcoholic fatty liver disease.

Yue Xu, Lin-Lin Lu, Shou-Sheng Liu +4 more · 2018 · Hepatobiliary & pancreatic diseases international : HBPD INT · Elsevier · added 2026-04-24
Several studies have reported that apolipoprotein A5 (APOA5) is involved in the development of non-alcoholic fatty liver disease (NAFLD). However, no research has been performed regarding the associat Show more
Several studies have reported that apolipoprotein A5 (APOA5) is involved in the development of non-alcoholic fatty liver disease (NAFLD). However, no research has been performed regarding the association between APOA5 polymorphisms and the risk of NAFLD. This study aimed to explore the association between APOA5 gene polymorphisms and NAFLD in a Chinese Han population. Genotypes of the SNPs (rs10750097, rs1263173, rs17120035, rs3135507 and rs662799) of APOA5 in 232 NAFLD patients and 188 healthy controls were determined using polymerase chain reaction (PCR) analysis. Clinical characteristics were measured using biochemical methods. The five single nucleotide polymorphisms (SNPs) (rs10750097, rs1263173, rs17120035, rs3135507 and rs662799) of APOA5 showed no significant association with NAFLD (P > 0.05). The rs10750097 with G allele showed a higher serum level of alkaline phosphatase (ALP) compared with C allele in overall series and NAFLD patients (P < 0.05). The rs1263173(A/A) carriers showed a higher level of glucose compared to the non-carriers in overall series (P < 0.05). The rs17120035(T/T) carriers showed a lower plasma TG level in overall series and NAFLD patients (P < 0.05), and the rs662799(G/G) carriers showed higher levels of plasma triglyceride (TG), ALP, and lower level of high-density lipoprotein (HDL) compared to non-carriers in NAFLD patients (P < 0.05). No significant difference were observed on the clinic parameters of APOA5 rs3135507(T/T) carriers in both group of overall series and NAFLD patients (P > 0.05). The five SNPs (rs10750097, rs1263173, rs17120035, rs3135507 and rs662799) of APOA5 gene are not associated with the risk of NAFLD in the Chinese Han population. The genotypes of rs10750097(G/G), rs1263173(A/A), rs17120035(T/T), and rs662799(G/G) performed a significant effect on clinic characteristics in overall series and NAFLD patients, indicating that these polymorphisms may be associated with NAFLD. Show less
no PDF DOI: 10.1016/j.hbpd.2018.04.004
APOA5

Characteristics of genomic alterations of lung adenocarcinoma in young never-smokers.

Wenxin Luo, Panwen Tian, Yue Wang +15 more · 2018 · International journal of cancer · Wiley · added 2026-04-24
Non-small-cell lung cancer (NSCLC) has been recognized as a highly heterogeneous disease with phenotypic and genotypic diversity in each subgroup. While never-smoker patients with NSCLC have been well Show more
Non-small-cell lung cancer (NSCLC) has been recognized as a highly heterogeneous disease with phenotypic and genotypic diversity in each subgroup. While never-smoker patients with NSCLC have been well studied through next generation sequencing, we have yet to recognize the potentially unique molecular features of young never-smoker patients with NSCLC. In this study, we conducted whole genome sequencing (WGS) to characterize the genomic alterations of 36 never-smoker Chinese patients, who were diagnosed with lung adenocarcinoma (LUAD) at 45 years or younger. Besides the well-known gene mutations (e.g., TP53 and EGFR), our study identified several potential lung cancer-associated gene mutations that were rarely reported (e.g., HOXA4 and MST1). The lung cancer-related copy number variations (e.g., EGFR and CDKN2A) were enriched in our cohort (41.7%, 15/36) and the lung cancer-related structural variations (e.g., EML4-ALK and KIF5B-RET) were commonly observed (22.2%, 8/36). Notably, new fusion partners of ALK (SMG6-ALK) and RET (JMJD1C-RET) were found. Furthermore, we observed a high prevalence (63.9%, 23/36) of potentially targetable genomic alterations in our cohort. Finally, we identified germline mutations in BPIFB1 (rs6141383, p.V284M), CHD4 (rs74790047, p.D140E), PARP1 (rs3219145, p.K940R), NUDT1 (rs4866, p.V83M), RAD52 (rs4987207, p.S346*), and MFI2 (rs17129219, p.A559T) were significantly enriched in the young never-smoker patients with LUAD when compared with the in-house noncancer database (p < 0.05). Our study provides a detailed mutational portrait of LUAD occurring in young never-smokers and gives insights into the molecular pathogenesis of this distinct subgroup of NSCLC. Show less
📄 PDF DOI: 10.1002/ijc.31542
JMJD1C

Silencing steroid receptor coactivator-1 in the nucleus of the solitary tract reduces estrogenic effects on feeding and apolipoprotein A-IV expression.

Ling Shen, Yin Liu, Patrick Tso +4 more · 2018 · The Journal of biological chemistry · American Society for Biochemistry and Molecular Biology · added 2026-04-24
We previously found that 17β-estradiol (E2) stimulates
no PDF DOI: 10.1074/jbc.RA117.000237
APOA4

An opposing function of paralogs in balancing developmental synapse maturation.

Plinio D Favaro, Xiaojie Huang, Leon Hosang +8 more · 2018 · PLoS biology · PLOS · added 2026-04-24
The disc-large (DLG)-membrane-associated guanylate kinase (MAGUK) family of proteins forms a central signaling hub of the glutamate receptor complex. Among this family, some proteins regulate developm Show more
The disc-large (DLG)-membrane-associated guanylate kinase (MAGUK) family of proteins forms a central signaling hub of the glutamate receptor complex. Among this family, some proteins regulate developmental maturation of glutamatergic synapses, a process vulnerable to aberrations, which may lead to neurodevelopmental disorders. As is typical for paralogs, the DLG-MAGUK proteins postsynaptic density (PSD)-95 and PSD-93 share similar functional domains and were previously thought to regulate glutamatergic synapses similarly. Here, we show that they play opposing roles in glutamatergic synapse maturation. Specifically, PSD-95 promoted, whereas PSD-93 inhibited maturation of immature α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid-type glutamate receptor (AMPAR)-silent synapses in mouse cortex during development. Furthermore, through experience-dependent regulation of its protein levels, PSD-93 directly inhibited PSD-95's promoting effect on silent synapse maturation in the visual cortex. The concerted function of these two paralogs governed the critical period of juvenile ocular dominance plasticity (jODP), and fine-tuned visual perception during development. In contrast to the silent synapse-based mechanism of adjusting visual perception, visual acuity improved by different mechanisms. Thus, by controlling the pace of silent synapse maturation, the opposing but properly balanced actions of PSD-93 and PSD-95 are essential for fine-tuning cortical networks for receptive field integration during developmental critical periods, and imply aberrations in either direction of this process as potential causes for neurodevelopmental disorders. Show less
📄 PDF DOI: 10.1371/journal.pbio.2006838
DLG2

Genome-wide analysis of disease progression in age-related macular degeneration.

Qi Yan, Ying Ding, Yi Liu +15 more · 2018 · Human molecular genetics · Oxford University Press · added 2026-04-24
Family- and population-based genetic studies have successfully identified multiple disease-susceptibility loci for Age-related macular degeneration (AMD), one of the first batch and most successful ex Show more
Family- and population-based genetic studies have successfully identified multiple disease-susceptibility loci for Age-related macular degeneration (AMD), one of the first batch and most successful examples of genome-wide association study. However, most genetic studies to date have focused on case-control studies of late AMD (choroidal neovascularization or geographic atrophy). The genetic influences on disease progression are largely unexplored. We assembled unique resources to perform a genome-wide bivariate time-to-event analysis to test for association of time-to-late-AMD with ∼9 million variants on 2721 Caucasians from a large multi-center randomized clinical trial, the Age-Related Eye Disease Study. To our knowledge, this is the first genome-wide association study of disease progression (bivariate survival outcome) in AMD genetic studies, thus providing novel insights to AMD genetics. We used a robust Cox proportional hazards model to appropriately account for between-eye correlation when analyzing the progression time in the two eyes of each participant. We identified four previously reported susceptibility loci showing genome-wide significant association with AMD progression: ARMS2-HTRA1 (P = 8.1 × 10-43), CFH (P = 3.5 × 10-37), C2-CFB-SKIV2L (P = 8.1 × 10-10) and C3 (P = 1.2 × 10-9). Furthermore, we detected association of rs58978565 near TNR (P = 2.3 × 10-8), rs28368872 near ATF7IP2 (P = 2.9 × 10-8) and rs142450006 near MMP9 (P = 0.0006) with progression to choroidal neovascularization but not geographic atrophy. Secondary analysis limited to 34 reported risk variants revealed that LIPC and CTRB2-CTRB1 were also associated with AMD progression (P < 0.0015). Our genome-wide analysis thus expands the genetics in both development and progression of AMD and should assist in early identification of high risk individuals. Show less
no PDF DOI: 10.1093/hmg/ddy002
POC5

The therapeutic potential of CETP inhibitors: a patent review.

Xinran Wang, Wei Li, Lijuan Hao +6 more · 2018 · Expert opinion on therapeutic patents · Taylor & Francis · added 2026-04-24
Epidemiological studies have identified that high levels of low-density lipoprotein-cholesterol (LDL-C) and low levels of high-density lipoprotein-cholesterol (HDL-C) are two independent causes of car Show more
Epidemiological studies have identified that high levels of low-density lipoprotein-cholesterol (LDL-C) and low levels of high-density lipoprotein-cholesterol (HDL-C) are two independent causes of cardiovascular disease (CVD). Statins, niacin and fibrate are used for the treatment of CVD. However, some defects are shown in the treatment process. Thus, there is a demand for better treatment strategies that confer preferable efficacy with fewer side effects. Cholesteryl ester transfer protein (CETP) promotes the movement of CEs from HDL to LDL and VLDL in exchange for triglycerides (TGs). In this review, we reviewed the development and therapeutic applications of CETP inhibitors. A comprehensive review of the patents and pharmaceutical applications between 2009 and 2017 has been highlighted. Recently, CETP inhibitors have attracted considerable interest in atherosclerosis-related disease. There are four drugs (torcetrapib, anacetrapib, evacetrapib and dalcetrapib) that have been clinically evaluated in phase III clinical trials and showed promising results in raising HDL-C levels, but there were suboptimal performances in reducing the risk of cardiovascular events with all the compounds. The correlation between plasma HDL-C levels and CVD incidence needs further verification. The timeline is still long for CETP inhibitors to emerge from the treatment of CVD. Show less
no PDF DOI: 10.1080/13543776.2018.1439476
CETP

A 44-gene set constructed for predicting the prognosis of clear cell renal cell carcinoma.

Yonggang Wang, Yao Wang, Feng Liu · 2018 · International journal of molecular medicine · added 2026-04-24
Clear cell renal cell carcinoma (ccRCC) is the most frequent type of renal cell carcinoma (RCC). The present study aimed to examine prognostic markers and construct a prognostic prediction system for Show more
Clear cell renal cell carcinoma (ccRCC) is the most frequent type of renal cell carcinoma (RCC). The present study aimed to examine prognostic markers and construct a prognostic prediction system for ccRCC. The mRNA sequencing data of ccRCC was downloaded from The Cancer Genome Atlas (TCGA) database, and the GSE40435 dataset was obtained from the Gene Expression Omnibus database. Using the Limma package, the differentially expressed genes (DEGs) in the TCGA dataset and GSE40435 dataset were obtained, respectively, and the overlapped DEGs were selected. Subsequently, Cox regression analysis was applied for screening prognosis‑associated genes. Following visualization of the co‑expression network using Cytoscape software, the network modules were examined using the GraphWeb tool. Functional annotation for genes in the network was performed using the clusterProfiler package. Finally, a prognostic prediction system was constructed through Bayes discriminant analysis and confirmed with the GSE29609 validation dataset. The results revealed a total of 263 overlapped DEGs and 161 prognosis‑associated genes. Following construction of the co‑expression network, 16 functional terms and three pathways were obtained for genes in the network. In addition, red, yellow (involving chemokine ligand 10 (CXCL10), CD27 molecule (CD27) and runt‑related transcription factor 3 (RUNX3)], green (involving angiopoietin‑like 4 (ANGPTL4), stanniocalcin 2 (STC2), and sperm associated antigen 4 (SPAG4)], and cyan modules were extracted from the co‑expression network. Additionally, the prognostic prediction system involving 44 signature genes, including ANGPTL4, STC2, CXCL10, SPAG4, CD27, matrix metalloproteinase (MMP9) and RUNX3, was identified and confirmed. In conclusion, the 44‑gene prognostic prediction system involving ANGPTL4, STC2, CXCL10, SPAG4, CD27, MMP9 and RUNX3 may be utilized for predicting the prognosis of patients with ccRCC. Show less
📄 PDF DOI: 10.3892/ijmm.2018.3899
ANGPTL4

miR-1322 regulates ChREBP expression via binding a 3'-UTR variant (rs1051943).

Ying Zhang, Sen-Lin Hu, Dong Hu +4 more · 2018 · Journal of cellular and molecular medicine · Blackwell Publishing · added 2026-04-24
The carbohydrate response element-binding protein (ChREBP), also referred to as MLXIPL, plays a crucial role in the regulation of glucose and lipid metabolism. Existing studies have shown an associati Show more
The carbohydrate response element-binding protein (ChREBP), also referred to as MLXIPL, plays a crucial role in the regulation of glucose and lipid metabolism. Existing studies have shown an association between genetic variations of the ChREBP gene and lipid levels, such as triglycerides and high-density lipoprotein cholesterol. However, mechanistic studies of this association are limited. In this study, bioinformatic analysis revealed that the polymorphism rs1051943A occurs in the complementary binding sequence of miR-1322 in the ChREBP 3'-untranslated region (UTR). Studies of potential mechanisms showed that the A allele could facilitate miR-1322 binding, and luciferase activity significantly decreased when co-transfected with a ChREBP 3'-UTR luciferase reporter vector and miR-1322 mimics in HepG2 cells. Furthermore, miR-1322 significantly regulated the expression of ChREBP downstream genes and reduced the synthesis of lipids. The expression of miR-1322 was up-regulated by glucose and palmitic acid stimulation. Population studies showed that rs1051943-A allele was only found in the Han Chinese and Uighur ethnic groups, different from European populations (G allele frequency = 0.07). In summary, we provide evidence that the rs1051943 A allele creates a functional miR-1322 binding site in ChREBP 3'-UTR and post-transcriptionally down-regulates its expression, possibly associated with levels of plasma lipids and glucose. Show less
📄 PDF DOI: 10.1111/jcmm.13805
MLXIPL

SIRT1 activator E1231 protects from experimental atherosclerosis and lowers plasma cholesterol and triglycerides by enhancing ABCA1 expression.

Tingting Feng, Peng Liu, Xiao Wang +12 more · 2018 · Atherosclerosis · Elsevier · added 2026-04-24
Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent protein deacetylase. Recent studies have demonstrated that enhancing SIRT1 expression or activity may modulate cholesterol and lipid Show more
Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent protein deacetylase. Recent studies have demonstrated that enhancing SIRT1 expression or activity may modulate cholesterol and lipid metabolism. However, pharmacological and molecular regulators for SIRT1 are scarce. Here, we aimed to find novel small molecule modulators of SIRT1 to regulate cholesterol and lipid metabolism. A high-throughput screening assay was established to identify SIRT1 activators. Surface plasmon resonance and immunoprecipitation were performed to confirm the interaction of E1231 with SIRT1. Cholesterol assay was performed to demonstrate the in vitro effect of E1231. The in vivo effect of E1231 was evaluated in experimental models. E1231, a piperazine 1,4-diamide compound, was identified as a SIRT1 activator with EC We identified a novel SIRT1 activator E1231 and elucidated its beneficial effects on lipid and cholesterol metabolism. Our study suggests that E1231 might be developed as a novel drug for treating atherosclerosis. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2018.04.039
NR1H3

Functional Comparison of High and Low Molecular Weight Chitosan on Lipid Metabolism and Signals in High-Fat Diet-Fed Rats.

Shing-Hwa Liu, Chen-Yuan Chiu, Ching-Ming Shi +1 more · 2018 · Marine drugs · MDPI · added 2026-04-24
The present study examined and compared the effects of low- and high-molecular weight (MW) chitosan, a nutraceutical, on lipid metabolism in the intestine and liver of high-fat (HF) diet-fed rats. Hig Show more
The present study examined and compared the effects of low- and high-molecular weight (MW) chitosan, a nutraceutical, on lipid metabolism in the intestine and liver of high-fat (HF) diet-fed rats. High-MW chitosan as well as low-MW chitosan decreased liver weight, elongated the small intestine, improved the dysregulation of blood lipids and liver fat accumulation, and increased fecal lipid excretion in rats fed with HF diets. Supplementation of both high- and low-MW chitosan markedly inhibited the suppressed phosphorylated adenosine monophosphate (AMP)-activated protein kinase-α (AMPKα) and peroxisome proliferator-activated receptor-α (PPARα) protein expressions, and the increased lipogenesis/cholesterogenesis-associated protein expressions [peroxisome proliferator-activated receptor-γ (PPARγ), sterol regulatory element binding protein-1c and -2 (SREBP1c and SREBP2)] and the suppressed apolipoprotein E (ApoE) and microsomal triglyceride transfer protein (MTTP) protein expressions in the livers of rats fed with HF diets. Supplementation with both a low- and high-MW chitosan could also suppress the increased MTTP protein expression and the decreased angiopoietin-like protein-4 (Angptl4) expression in the intestines of rats fed with HF diets. In comparison between low- and high-MW chitosan, high-MW chitosan exhibits a higher efficiency than low-MW chitosan on the inhibition of intestinal lipid absorption and an increase of hepatic fatty acid oxidation, which can improve liver lipid biosynthesis and accumulation. Show less
📄 PDF DOI: 10.3390/md16080251
ANGPTL4

Gene-diet interaction effects on BMI levels in the Singapore Chinese population.

Xuling Chang, Rajkumar Dorajoo, Ye Sun +11 more · 2018 · Nutrition journal · BioMed Central · added 2026-04-24
Recent genome-wide association studies (GWAS) have identified 97 body-mass index (BMI) associated loci. We aimed to evaluate if dietary intake modifies BMI associations at these loci in the Singapore Show more
Recent genome-wide association studies (GWAS) have identified 97 body-mass index (BMI) associated loci. We aimed to evaluate if dietary intake modifies BMI associations at these loci in the Singapore Chinese population. We utilized GWAS information from six data subsets from two adult Chinese population (N = 7817). Seventy-eight genotyped or imputed index BMI single nucleotide polymorphisms (SNPs) that passed quality control procedures were available in all datasets. Alternative Healthy Eating Index (AHEI)-2010 score and ten nutrient variables were evaluated. Linear regression analyses between z score transformed BMI (Z-BMI) and dietary factors were performed. Interaction analyses were performed by introducing the interaction term (diet x SNP) in the same regression model. Analysis was carried out in each cohort individually and subsequently meta-analyzed using the inverse-variance weighted method. Analyses were also evaluated with a weighted gene-risk score (wGRS) contructed by BMI index SNPs from recent large-scale GWAS studies. Nominal associations between Z-BMI and AHEI-2010 and some dietary factors were identified (P = 0.047-0.010). The BMI wGRS was robustly associated with Z-BMI (P = 1.55 × 10 The CCDC171 gene locus may interact with cholesterol intake to increase BMI in the Singaporean Chinese population, however most known obesity risk loci were not associated with dietary intake and did not interact with diet to modify BMI levels. Show less
📄 PDF DOI: 10.1186/s12937-018-0340-3
CCDC171

A rare exonic NRXN3 deletion segregating with neurodevelopmental and neuropsychiatric conditions in a three-generation Chinese family.

Haiming Yuan, Qingming Wang, Yanhui Liu +5 more · 2018 · American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · Wiley · added 2026-04-24
Members of the neurexin gene family, neurexin 1 (NRXN1), neurexin 2 (NRXN2), and neurexin 3 (NRXN3) encode important components of synaptic function implicated in autism and other neurodevelopmental/n Show more
Members of the neurexin gene family, neurexin 1 (NRXN1), neurexin 2 (NRXN2), and neurexin 3 (NRXN3) encode important components of synaptic function implicated in autism and other neurodevelopmental/neuropsychiatric disorders. Loss of function variants have been reported predominantly in NRXN1, with fewer such variants detected in NRXN2 and NRXN3. Evidence for segregating NRNX3 variants has particularly been lacking. Here, we report identification by chromosomal microarray analysis of a rare exonic deletion affecting the NRXN3 alpha isoform in a three-generation Chinese family. The proband, a 7-year-old boy, presented with motor and language delay and met the clinical diagnostic criteria for autism. He also presented with moderate intellectual disability, attention-deficit hyperactivity disorder and facial dysmorphic features. The mother and maternal grandfather, both deletion carriers, presented with variable degrees of language and communication difficulties, as well as neuropsychiatric problems such as schizophrenia and temper tantrums. A compilation of sporadic cases with deletions involving part or all of NRXN3 revealed that 9 of 23 individuals (39%) displayed features of autism. The evidence for cosegregation in our family further supports a role for NRXN3 in autism and neurodevelopmental/neuropsychiatric disorders but demonstrates intrafamily variable expressivity due to this NRXN3 deletion, with schizophrenia and facial dysmorphism being potential novel features of NRXN3 haploinsufficiency. Show less
no PDF DOI: 10.1002/ajmg.b.32673
NRXN3

Replicated associations of FADS1, MAD1L1, and a rare variant at 10q26.13 with bipolar disorder in Chinese population.

Lijuan Zhao, Hong Chang, Dong-Sheng Zhou +15 more · 2018 · Translational psychiatry · Nature · added 2026-04-24
Genetic analyses of psychiatric illnesses, such as bipolar disorder (BPD), have revealed essential information regarding the underlying pathological mechanisms. While such studies in populations of Eu Show more
Genetic analyses of psychiatric illnesses, such as bipolar disorder (BPD), have revealed essential information regarding the underlying pathological mechanisms. While such studies in populations of European ancestry have achieved prominent success, understanding the genetic risk factors of these illnesses (especially BPD) in Chinese population remains an urgent task. Given the lack of genome-wide association study (GWAS) of BPD in Chinese population from Mainland China, replicating the previously reported GWAS hits in distinct populations will provide valuable information for future GWAS analysis in Han Chinese. In the present study, we have recruited 1146 BPD cases and 1956 controls from Mainland China for genetic analyses, as well as 65 Han Chinese brain amygdala tissues for mRNA expression analyses. Using this clinical sample, one of the largest Han Chinese BPD samples till now, we have conducted replication analyses of 21 single nucleotide polymorphisms (SNPs) extracted from previous GWAS of distinct populations. Among the 21 tested SNPs, 16 showed the same direction of allelic effects in our samples compared with previous studies; 6 SNPs achieved nominal significance (p < 0.05) at one-tailed test, and 2 additional SNPs showed marginal significance (p < 0.10). Aside from replicating previously reported BPD risk SNPs, we herein also report several intriguing findings: (1) the SNP rs174576 was associated with BPD in our Chinese sample and in the overall global meta-analysis, and was significantly correlated with FADS1 mRNA in diverse public RNA-seq datasets as well as our in house collected Chinese amygdala samples; (2) two (partially) independent SNPs in MAD1L1 were both significantly associated with BPD in our Chinese sample, which was also supported by haplotype analysis; (3) a rare SNP rs78089757 in 10q26.13 region was a genome-wide significant variant for BPD in East Asians, and this SNP was near monomorphic in Europeans. In sum, these results confirmed several significant BPD risk genes. We hope this Chinese BPD case-control sample and the current brain amygdala tissues (with continuous increasing sample size in the near future) will provide helpful resources in elucidating the genetic and molecular basis of BPD in this major world population. Show less
📄 PDF DOI: 10.1038/s41398-018-0337-x
FADS1

Fatty acid elongase 5 (ELOVL5) alters the synthesis of long-chain unsaturated fatty acids in goat mammary epithelial cells.

H B Shi, Y Du, C H Zhang +6 more · 2018 · Journal of dairy science · added 2026-04-24
Increased production of long-chain unsaturated fatty acids (LCUFA) can have a positive effect on the nutritional value of ruminant milk for human consumption. In nonruminant species, fatty acid elonga Show more
Increased production of long-chain unsaturated fatty acids (LCUFA) can have a positive effect on the nutritional value of ruminant milk for human consumption. In nonruminant species, fatty acid elongase 5 (ELOVL5) is a key enzyme for endogenous synthesis of long-chain unsaturated fatty acids. However, whether ELOVL5 protein plays a role (if any) in ruminant mammary tissue remains unclear. In the present study, we assessed the mRNA abundance of ELOVL5 at 3 stages of lactation in goat mammary tissue. Results revealed that ELOVL5 had the lowest expression at peak lactation compared with the nonlactating and late-lactating periods. The ELOVL5 was overexpressed or knocked down to assess its role in goat mammary epithelial cells. Results revealed that ELOVL5 overexpression increased the expression of perilipin2 (PLIN2) and decreased diacylglycerolacyltransferase 2 (DGAT2) and fatty acid desaturase 2 (FADS2) mRNA, but had no effect on the expression of DGAT1, FADS1, and stearoyl-CoA desaturase 1 (SCD1). Overexpression of ELOVL5 decreased the concentration of C16:1n-7, whereas no significant change in C18:1n-7 and C18:1n-9 was observed. Knockdown of ELOVL5 decreased the expression of PLIN2 but had no effect on DGAT1, DGAT2, FADS1, FADS2, and SCD1 mRNA expression. Knockdown of ELOVL5 increased the concentration of C16:1n-7 and decreased that of C18:1n-7. The alterations of expression of genes related to lipid metabolism after overexpression or knockdown of ELOVL5 suggested a negative feedback regulation by the products of ELOVL5 activation. However, the content of triacylglycerol was not altered by knockdown or overexpression of ELOVL5 in goat mammary epithelial cells, which might have been due to the insufficient availability of substrate in vitro. Collectively, these are the first in vitro results highlighting an important role of ELOVL5 in the elongation of 16-carbon to 18-carbon unsaturated fatty acids in ruminant mammary cells. Show less
no PDF DOI: 10.3168/jds.2017-14061
FADS1

Clioquinol induces G

Chongjia Yan, Song Wang, Jian Wang +6 more · 2018 · Microbiological research · Elsevier · added 2026-04-24
Clioquinol (CQ) has been used as a classical antimicrobial agent for many years. However, its mode of action is still unclear. In our study, the growth of Candida albicans and Saccharomyces cerevisiae Show more
Clioquinol (CQ) has been used as a classical antimicrobial agent for many years. However, its mode of action is still unclear. In our study, the growth of Candida albicans and Saccharomyces cerevisiae was inhibited by CQ. It did not kill yeast cells, but shortened G1 phase and arrested cell cycle at G2/M phase. By using two-dimensional electrophoresis based proteomic approach, six proteins were found to be significantly affected by CQ. Among them, four (PDC1, ADH1, TDH3, IPP1) were up-regulated and the other two (TDH1 and PGK1) were down-regulated. According to the Saccharomyces Genome Database (SGD), these proteins were involved in various biological processes including glycolytic fermentation, gluconeogenesis, glycolytic process, amino acid catabolism, redox reaction and reactive oxygen species metabolic process. It was noted that there was a link between TDH3 and cell cycle. The overexpression of TDH3 phenocopied CQ treatment and arrested the cell cycle at G2/M phase. RT-PCR analysis showed that the mRNA levels of CLN3 and CDC28, critical genes for passage through G1 phase, were up-regulated after the treatment of CQ as well as the overexpression of TDH3. It demonstrates that CQ inhibits the growth of yeast by up-regulating the expression of TDH3 to influence the cell cycle. It is expected to provide new insights for the antimicrobial mechanism of CQ. Show less
no PDF DOI: 10.1016/j.micres.2018.05.006
CLN3

Genetic variants regulate NR1H3 expression and contribute to multiple sclerosis risk.

Yan Zhang, Longcai Wang, Haiyang Jia +5 more · 2018 · Journal of the neurological sciences · Elsevier · added 2026-04-24
A recent study analyzed 2053 multiple sclerosis (MS) cases and 799 healthy controls to investigate whether five genetic variants (rs11039149, rs12221497, rs2279238, rs7120118 and rs7114704) in NR1H3 a Show more
A recent study analyzed 2053 multiple sclerosis (MS) cases and 799 healthy controls to investigate whether five genetic variants (rs11039149, rs12221497, rs2279238, rs7120118 and rs7114704) in NR1H3 are associated with MS risk. However this study reported negative results. It is very important that the appropriate samples and approach should be used in replication studies, which may provide the correct interpretation of the results. Here, we evaluated the above findings using large-scale MS genome-wide association studies with a total of 27,148 samples including 9772 MS cases and 17,376 controls, and multiple expression quantitative trait loci datasets. The results suggest that rs7120118 and rs2279238 variants are significantly associated with MS risk, and could significantly regulate NR1H3 expression in kinds of human tissues and cells. In summary, these findings provide important supplementary information about the association between NR1H3 variants and MS risk. Show less
no PDF DOI: 10.1016/j.jns.2018.04.037
NR1H3

Transcriptome analysis reveals the molecular mechanism of hepatic metabolism disorder caused by chromium poisoning in chickens.

Xinxin Tian, Hui Zhang, Yali Zhao +8 more · 2018 · Environmental science and pollution research international · Springer · added 2026-04-24
Chromium (Cr) is one of the most important environmental pollutants which are released into the environment due to their wide usage in numerous industries. The excess of Cr (VI) can induce hepatotoxic Show more
Chromium (Cr) is one of the most important environmental pollutants which are released into the environment due to their wide usage in numerous industries. The excess of Cr (VI) can induce hepatotoxicity, while the molecular mechanism that is involved in Cr (VI)-induced hepatotoxicity is unclear. We demonstrated the induction of chromium poisoning model in chickens to identify the differentially expressed genes (DEGs), and their functions were analyzed under different physiological and pathological conditions. Histopathological examination and transcriptome data for chromium-poisoned livers and control livers were annotated with Illumina® HiSeq 2000. The histopathological examination in chromium poisoning groups showed diapedesis, hemolysis, degeneration, nucleus pycnosis, and central phlebectasia in the liver. A total of 334 genes were upregulated and 509 genes were downregulated. The most strongly upregulated genes were HKDC1, DDX4, ACACA, FDFT1, CYYR1, PPP1R3C, and SLC16A14, while the most downregulated genes were MYBPC3, CCKAR, PCK1, and CPT1A. A Gene Ontology (GO) term with the highest enrichment of DEGs is small molecule metabolic process. In cell component domain, the term with the highest enrichment is extracellular matrix. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways showed that glucose metabolism, lipid metabolism, and protein metabolism were the most important metabolic pathways in the liver. The current study first time provides important clues and evidence for identifying the differentially expressed genes in livers due to Cr (VI)-induced liver injury in chickens. Show less
no PDF DOI: 10.1007/s11356-018-1653-7
MYBPC3

The Bardet-Biedl syndrome protein complex is an adapter expanding the cargo range of intraflagellar transport trains for ciliary export.

Peiwei Liu, Karl F Lechtreck · 2018 · Proceedings of the National Academy of Sciences of the United States of America · National Academy of Sciences · added 2026-04-24
Bardet-Biedl syndrome (BBS) is a ciliopathy resulting from defects in the BBSome, a conserved protein complex. BBSome mutations affect ciliary membrane composition, impairing cilia-based signaling. Th Show more
Bardet-Biedl syndrome (BBS) is a ciliopathy resulting from defects in the BBSome, a conserved protein complex. BBSome mutations affect ciliary membrane composition, impairing cilia-based signaling. The mechanism by which the BBSome regulates ciliary membrane content remains unknown. Show less
no PDF DOI: 10.1073/pnas.1713226115
BBS4

Knockdown of NUP160 inhibits cell proliferation, induces apoptosis, autophagy and cell migration, and alters the expression and localization of podocyte associated molecules in mouse podocytes.

Ping Wang, Feng Zhao, Xiaojing Nie +2 more · 2018 · Gene · Elsevier · added 2026-04-24
Genetic mutations in dozens of monogenic genes can lead to serious podocyte dysfunction, which is a major cause of steroid-resistant nephrotic syndrome (SRNS). The NUP160 gene is expressed in both hum Show more
Genetic mutations in dozens of monogenic genes can lead to serious podocyte dysfunction, which is a major cause of steroid-resistant nephrotic syndrome (SRNS). The NUP160 gene is expressed in both human kidney and mouse kidney. However, whether knockdown of NUP160 impairs podocytes has not yet been established. Therefore, we knocked down NUP160 by targeted short hairpin RNA (shRNA) in conditionally immortalized mouse podocytes and observed the effect of NUP160 knockdown on the proliferation, apoptosis, autophagy and cell migration of podocytes. We also investigated the effect of NUP160 knockdown on the expression and localization of podocyte associated molecules, such as nephrin, podocin, CD2AP and α-actinin-4. The knockdown of NUP160 significantly inhibited the proliferation of podocytes by decreasing the expression of both cyclin D1 and CDK4, increasing the expression of p27, and inducing S phase arrest. The knockdown of NUP160 promoted the apoptosis and autophagy of podocytes, and enhanced cell migration. The knockdown of NUP160 decreased the expression of nephrin, podocin and CD2AP in podocytes, and increased the expression of α-actinin-4. The knockdown of NUP160 also altered the subcellular localization of nephrin, podocin and CD2AP in podocytes. These results suggest that the knockdown of NUP160 impairs mouse podocytes, i.e. inhibiting cell proliferation, inducing apoptosis, autophagy and cell migration of mouse podocytes, and altering the expression and localization of podocyte associated molecules, including nephrin, podocin, CD2AP and α-actinin-4. Show less
no PDF DOI: 10.1016/j.gene.2018.04.067
NUP160

MicroRNA-34a modulates the Notch signaling pathway in mice with congenital heart disease and its role in heart development.

Kai-Hong Wu, Qian-Ru Xiao, Yu Yang +6 more · 2018 · Journal of molecular and cellular cardiology · Elsevier · added 2026-04-24
The objective of the study was to elucidate the mechanism by which microRNA-34a (miR-34a) influences heart development and participates in the pathogenesis of congenital heart disease (CHD) by targeti Show more
The objective of the study was to elucidate the mechanism by which microRNA-34a (miR-34a) influences heart development and participates in the pathogenesis of congenital heart disease (CHD) by targeting NOTCH-1 through the Notch signaling pathway. Forty D7 pregnant mice were recruited for the purposes of the study and served as the CHD (n=20, successfully established as CHD model) and normal (n=20) groups. The positive expression of the NOTCH-1 protein was evaluated by means of immunohistochemistry. Embryonic endocardial cells (ECCs) were assigned into the normal, blank, negative control (NC), miR-34a mimics, miR-34a inhibitors, miR-34a inhibitors+siRNA-NOTCH-1, siRNA-NOTCH-1, miR-34a mimics+NOTCH-1 OE and miR-34a mimics+crispr/cas9 (mutant NOTCH-1) groups. The expressions of miR-34a, NOTCH-1, Jagged1, Hes1, Hey2 and Csx in cardiac tissues and ECCs were determined by both RT-qPCR and western blotting methods. MTT assay and flow cytometry were conducted for cell proliferation and apoptosis measurement. A dual luciferase reporter assay was applied to demonstrate that NOTCH-1 was the target gene of miR-34a. In comparison to the normal group, the expressions of miR-34a, Jagged1, Hes1 and Hey2 displayed up-regulated levels, while the expressions of NOTCH-1 and Csx were down-regulated in the CHD group. Compared with the blank and NC groups, the miR-34a mimics and siRNA-NOTCH-1 groups displayed reduced expressions of NOTCH-1 and Csx as well as a decreased proliferation rate, higher miR-34a, Jagged1, Hes1 and Hey2 expressions and an increased rate of apoptosis; while an reverse trend was observed in the miR-34a inhibitors group. The expressions of MiR-34a recorded increased levels in the miR-34a mimics+NOTCH-1 OE and miR-34a mimics+crispr/cas9 (mutant NOTCH-1) groups, however no changes in the expressions of NOTCH-1, Jagged1, Hes1, Hey2, Csx, as well as cell proliferation and apoptosis were observed when compared to the blank and NC groups. The results of our study demonstrated that miR-34a increases the risk of CHD through its downregulation of NOTCH-1 by modulating the Notch signaling pathway. Show less
no PDF DOI: 10.1016/j.yjmcc.2017.11.015
HEY2

RGS17 inhibits tumorigenesis and improves 5-fluorouracil sensitivity in nasopharyngeal carcinoma.

Qianqian Yu, Niankai Zhang, Yan Jiang +5 more · 2018 · OncoTargets and therapy · added 2026-04-24
Nasopharyngeal carcinoma (NPC) is a poorly differentiated malignant tumor, and 5-fluorouracil (5-FU) is one of the most effective chemotherapeutic drugs used for the treatment of NPC. Abnormal express Show more
Nasopharyngeal carcinoma (NPC) is a poorly differentiated malignant tumor, and 5-fluorouracil (5-FU) is one of the most effective chemotherapeutic drugs used for the treatment of NPC. Abnormal expression of RGS17 had been shown to improve the sensitivity of many cancers to chemotherapy; however, the effects of RGS17 on NPC remain unclear. We cultured NPC cell lines and altered the RGS17 expression with vector. Subsequently colony formation assays and CCK8 cell viability assay was used to test the proliferation of NPC cells, flow cytometry was used to determine the percentage of apoptotic cells, MMP kit and flow cytometry was used to measure the mitochondrial membrane potential, and a xenograft tumour model was attached to investigate the effects of RGS17 on the growth of NPC cells in vivo. Additionally, RT-PCR and western blot was induced to examine the expression of RGS17 and the mechanism. Here, we report for the first time that RGS17 is downregulated in NPC cell lines and that RGS17 overexpression significantly reduces cell proliferation, decreases the mitochondrial membrane potential, and induces cell apoptosis in NPC cells. In vivo, RGS17 also inhibits the tumorigenicity of NPC. In addition, RGS17 could significantly improve the sensitivity of NPC cells to 5-FU. Furthermore, investigation into the underlying mechanisms showed that RGS17 upregulated the levels of IRE1α, p53, and active caspase-3 and cleaved PARP. These results indicate that RGS17 could play important roles in the proliferation, apoptosis, and chemotherapeutic sensitivity of NPC cells. Show less
no PDF DOI: 10.2147/OTT.S176002
RGS17

Folic acid supplementation alters the DNA methylation profile and improves insulin resistance in high-fat-diet-fed mice.

Wei Li, Renqiao Tang, Feifei Ma +3 more · 2018 · The Journal of nutritional biochemistry · Elsevier · added 2026-04-24
Folic acid (FA) supplementation may protect from obesity and insulin resistance, the effects and mechanism of FA on chronic high-fat-diet-induced obesity-related metabolic disorders are not well eluci Show more
Folic acid (FA) supplementation may protect from obesity and insulin resistance, the effects and mechanism of FA on chronic high-fat-diet-induced obesity-related metabolic disorders are not well elucidated. We adopted a genome-wide approach to directly examine whether FA supplementation affects the DNA methylation profile of mouse adipose tissue and identify the functional consequences of these changes. Mice were fed a high-fat diet (HFD), normal diet (ND) or an HFD supplemented with folic acid (20 μg/ml in drinking water) for 10 weeks, epididymal fat was harvested, and genome-wide DNA methylation analyses were performed using methylated DNA immunoprecipitation sequencing (MeDIP-seq). Mice exposed to the HFD expanded their adipose mass, which was accompanied by a significant increase in circulating glucose and insulin levels. FA supplementation reduced the fat mass and serum glucose levels and improved insulin resistance in HFD-fed mice. MeDIP-seq revealed distribution of differentially methylated regions (DMRs) throughout the adipocyte genome, with more hypermethylated regions in HFD mice. Methylome profiling identified DMRs associated with 3787 annotated genes from HFD mice in response to FA supplementation. Pathway analyses showed novel DNA methylation changes in adipose genes associated with insulin secretion, pancreatic secretion and type 2 diabetes. The differential DNA methylation corresponded to changes in the adipose tissue gene expression of Adcy3 and Rapgef4 in mice exposed to a diet containing FA. FA supplementation improved insulin resistance, decreased the fat mass, and induced DNA methylation and gene expression changes in genes associated with obesity and insulin secretion in obese mice fed a HFD. Show less
no PDF DOI: 10.1016/j.jnutbio.2018.05.010
ADCY3

LXRα is expressed at higher levels in healthy people compared to atherosclerosis patients and its over-expression polarizes macrophages towards an anti-inflammatory MΦ2 phenotype.

Mengyuan Liu, Weijian Yang, Shuling Liu +5 more · 2018 · Clinical and experimental hypertension (New York, N.Y. : 1993) · Taylor & Francis · added 2026-04-24
(1) To investigate the expression patterns of MΦ1 and MΦ2 phenotype markers of peripheral blood monocyte (PBMC)-derived macrophages in atherosclerosis patients and healthy controls, as well as the exp Show more
(1) To investigate the expression patterns of MΦ1 and MΦ2 phenotype markers of peripheral blood monocyte (PBMC)-derived macrophages in atherosclerosis patients and healthy controls, as well as the expression correlation among these genes. (2) To elucidate whether a high level of liver X receptor α (LXRα) expression is associated with anti-inflammatory MΦ2-type polarization. Peripheral blood monocytes (PBMCs) were obtained from 28 patients with carotid artery plaques and 10 normal persons, who did not have carotid artery plaques. M1 and M2 phenotype markers were analyzed after cellular differentiation into macrophages. Human macrophages derived from healthy donors were transfected with plasmid DNA encoding LXRα and control null-plasmids. Gene expression levels were quantified after further differentiation. Three genes (LXRα, CD68, and CD36) were expressed at a significantly lower rate in the atherosclerotic group than normal patients. There were correlations between the expression of LXRα, CD68, and peroxisome proliferator-activated receptor (PPARγ), and between CD163, CD36 and scavenger receptor class A (SRA1). Macrophages over-expressing LXRα exhibited enhanced expression level of MΦ2-type genes and decreased expression level of MΦ1-type genes. PBMCs from healthy persons were predisposed to the MΦ2 differentiation phenotype, which exhibits elevated cholesterol uptake and anti-inflammatory properties. LXRα over-expression polarizes macrophages towards the anti-inflammatory MΦ2 phenotype. Show less
no PDF DOI: 10.1080/10641963.2017.1288740
NR1H3

Dual Allosteric Inhibition of SHP2 Phosphatase.

Michelle Fodor, Edmund Price, Ping Wang +23 more · 2018 · ACS chemical biology · ACS Publications · added 2026-04-24
SHP2 is a cytoplasmic protein tyrosine phosphatase encoded by the PTPN11 gene and is involved in cell proliferation, differentiation, and survival. Recently, we reported an allosteric mechanism of inh Show more
SHP2 is a cytoplasmic protein tyrosine phosphatase encoded by the PTPN11 gene and is involved in cell proliferation, differentiation, and survival. Recently, we reported an allosteric mechanism of inhibition that stabilizes the auto-inhibited conformation of SHP2. SHP099 (1) was identified and characterized as a moderately potent, orally bioavailable, allosteric small molecule inhibitor, which binds to a tunnel-like pocket formed by the confluence of three domains of SHP2. In this report, we describe further screening strategies that enabled the identification of a second, distinct small molecule allosteric site. SHP244 (2) was identified as a weak inhibitor of SHP2 with modest thermal stabilization of the enzyme. X-ray crystallography revealed that 2 binds and stabilizes the inactive, closed conformation of SHP2, at a distinct, previously unexplored binding site-a cleft formed at the interface of the N-terminal SH2 and PTP domains. Derivatization of 2 using structure-based design resulted in an increase in SHP2 thermal stabilization, biochemical inhibition, and subsequent MAPK pathway modulation. Downregulation of DUSP6 mRNA, a downstream MAPK pathway marker, was observed in KYSE-520 cancer cells. Remarkably, simultaneous occupation of both allosteric sites by 1 and 2 was possible, as characterized by cooperative biochemical inhibition experiments and X-ray crystallography. Combining an allosteric site 1 inhibitor with an allosteric site 2 inhibitor led to enhanced pharmacological pathway inhibition in cells. This work illustrates a rare example of dual allosteric targeted protein inhibition, demonstrates screening methodology and tactics to identify allosteric inhibitors, and enables further interrogation of SHP2 in cancer and related pathologies. Show less
no PDF DOI: 10.1021/acschembio.7b00980
DUSP6

Influence of Fatty Acid Modification on Uptake of Lovastatin-Loaded Reconstituted High Density Lipoprotein by Foam Cells.

Yun Yang, Ji Wang, Hongliang He +3 more · 2018 · Pharmaceutical research · Springer · added 2026-04-24
Spherical reconstituted high density lipoprotein (rHDL) can target atherosclerotic lesions by the very low density lipoprotein (VLDL) receptor, which is seldom expressed in liver. By promoting this pa Show more
Spherical reconstituted high density lipoprotein (rHDL) can target atherosclerotic lesions by the very low density lipoprotein (VLDL) receptor, which is seldom expressed in liver. By promoting this pathway, the targeting efficiency was hyphothesized to be improved due to avoiding undesired uptake in liver mediated by the scavenger receptor class B type I (SR-BI). In this study, how fatty acid modification in spherical rHDL influenced the VLDL receptor-mediated endocytosis pathway was investigated. Stearic acid (SA) and arachidonic acid (AA) with different saturation levels were utilized to modify the lovastatin-loaded rHDL (LS-rHDL). Phagocytosis test on foam cells with or without cholesteryl ester transfer protein (CETP) expression was conducted to observe the cellular uptake of the SA or AA modified rHDL and the non-modified one. Raman spectroscopy, guanidine hydrochloride (Gdn-HCl) denaturation experiment and in vitro evaluation of drug release were used to analyze the related mechanism. In comparison with the non-modified rHDL, AA modification could reduce the packing order of the rHDL phospholipid acyl chains, leading to the decreased apoA-I binding extent with lipid and the increased drug release, while the opposite was true for SA modification. The AA-modified rHDL exhibited a higher uptake of foam cells expressing CETP than the non-modified one, while the SA-modified one showed the lowest cellular uptake among the three rHDLs. Increased unsaturation level can facilitate lipid-interchange process where the cargo in rHDL core may transfer to VLDL more easily, and then promote the endocytosis mediated by the VLDL receptor. Show less
no PDF DOI: 10.1007/s11095-018-2419-0
CETP