👤 Yao 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, 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, 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-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

Unveiling the bioinformatic genes and their involved regulatory mechanisms in type 2 diabetes combined with osteoarthritis.

Guangming Mao, Wenhao Xu, Lingli Wan +8 more · 2024 · Frontiers in immunology · Frontiers · added 2026-04-24
Type 2 Diabetes Mellitus (T2D) and Osteoarthritis (OA) are both prevalent diseases that significantly impact the health of patients. Increasing evidence suggests that there is a big correlation betwee Show more
Type 2 Diabetes Mellitus (T2D) and Osteoarthritis (OA) are both prevalent diseases that significantly impact the health of patients. Increasing evidence suggests that there is a big correlation between T2D and OA, but the molecular mechanisms remain elusive. The aims of this study are to investigate the shared biomarkers and potential molecular mechanisms in T2D combined with OA. T2D and OA-related differentially expressed genes (DEGs) were identified via bioinformatic analysis on Gene Expression Omnibus (GEO) datasets GSE26168 and GSE114007 respectively. Subsequently, extensive target prediction and network analysis were finished with Gene Ontology (GO), protein-protein interaction (PPI), and pathway enrichment with DEGs. The transcription factors (TFs) and miRNAs coupled in co-expressed DEGs involved in T2D and OA were predicted as well. The key genes expressed both in the clinical tissues of T2D and OA were detected with western blot and qRT-PCR assay. Finally, the most promising candidate compounds were predicted with the Drug-Gene Interaction Database (DGIdb) and molecular docking. In this study, 209 shared DEGs between T2D and OA were identified. Functional analysis disclosed that these DEGs are predominantly related to ossification, regulation of leukocyte migration, extracellular matrix (ECM) structural constituents, PI3K/AKT, and Wnt signaling pathways. Further analysis via Protein-Protein Interaction (PPI) analysis and validation with external datasets emphasized MMP9 and ANGPTL4 as crucial genes in both T2D and OA. Our findings were validated through qRT-PCR and Western blot analyses, which indicated high expression levels of these pivotal genes in T2D, OA, and T2D combined with OA cases. Additionally, the analysis of Transcription Factors (TFs)-miRNA interactions identified 7 TFs and one miRNA that jointly regulate these important genes. The Receiver Operating characteristic (ROC) analysis demonstrated the significant diagnostic potential of MMP9 and ANGPTL4.Moreover, we identified raloxifene, ezetimibe, and S-3304 as promising agents for patients with both T2D and OA. This study uncovers the shared signaling pathways, biomarkers, potential therapeutics, and diagnostic models for individuals suffering from both T2D and OA. These findings not only present novel perspectives on the complex interplay between T2D and OA but also hold significant promise for improving the clinical management and prognosis of patients with this concurrent condition. Show less
📄 PDF DOI: 10.3389/fimmu.2024.1353915
ANGPTL4

Finerenone attenuates downregulation of the kidney GLP-1 receptor and glucagon receptor and cardiac GIP receptor in mice with comorbid diabetes.

Duc Tin Tran, Emily S H Yeung, Lisa Y Q Hong +6 more · 2024 · Diabetology & metabolic syndrome · BioMed Central · added 2026-04-24
Several new treatments have recently been shown to have heart and kidney protective benefits in people with diabetes. Because these treatments were developed in parallel, it is unclear how the differe Show more
Several new treatments have recently been shown to have heart and kidney protective benefits in people with diabetes. Because these treatments were developed in parallel, it is unclear how the different molecular pathways affected by the therapies may overlap. Here, we examined the effects of the mineralocorticoid receptor antagonist finerenone in mice with comorbid diabetes, focusing on the regulation of expression of the glucagon-like peptide-1 receptor (GLP-1R), gastric inhibitory polypeptide receptor (GIPR) and glucagon receptor (GCGR), which are targets of approved or investigational therapies in diabetes. Male C57BL/6J mice were fed a high fat diet for 26 weeks. Twelve weeks into the high fat diet feeding period, mice received an intraperitoneal injection of streptozotocin before being followed for the remaining 14 weeks (DMHFD mice). After 26 weeks, mice were fed a high fat diet containing finerenone (100 mg/kg diet) or high fat diet alone for a further 2 weeks. Cell culture experiments were performed in primary vascular smooth muscle cells (VSMCs), NRK-49 F fibroblasts, HK-2 cells, and MDCK cells. DMHFD mice developed albuminuria, glomerular mesangial expansion, and diastolic dysfunction (decreased E/A ratio). Glp1r and Gcgr were predominantly expressed in arteriolar VSMCs and distal nephron structures of mouse kidneys respectively, whereas Gipr was the predominant of the three transcripts in mouse hearts. Kidney Glp1r and Gcgr and cardiac Gipr mRNA levels were reduced in DMHFD mice and this reduction was negated or attenuated with finerenone. Mechanistically, finerenone attenuated upregulation of the profibrotic growth factor Ccn2 in DMHFD kidneys, whereas recombinant CCN2 downregulated Glp1r and Gcgr in VSMCs and MDCK cells respectively. Through its anti-fibrotic actions, finerenone reverses Glp1r and Gcgr downregulation in the diabetic kidney. Both finerenone and GLP-1R agonists have proven cardiorenal benefits, whereas receptor co-agonists are approved or under development. The current findings provide preclinical rationale for the combined use of finerenone with the GLP-1R agonist family. They also provide mechanism of action insights into the potential benefit of finerenone in people with diabetes for whom GLP-1R agonists or co-agonists may not be indicated. Show less
📄 PDF DOI: 10.1186/s13098-024-01525-3
GIPR

Methionine restriction alleviates diabetes-associated cognitive impairment via activation of FGF21.

Yuyu Zhang, Yajie Wang, Yiju Li +9 more · 2024 · Redox biology · Elsevier · added 2026-04-24
Glucose metabolism disturbances may result in diabetes-associated cognitive decline (DACI). Methionine restriction (MR) diet has emerged as a potential dietary strategy for managing glucose homeostasi Show more
Glucose metabolism disturbances may result in diabetes-associated cognitive decline (DACI). Methionine restriction (MR) diet has emerged as a potential dietary strategy for managing glucose homeostasis. However, the effects and underlying mechanisms of MR on DACI have not been fully elucidated. Here, we found that a 13-week MR (0.17 % methionine, w/w) intervention starting at 8 weeks of age improved peripheral insulin sensitivity in male db/db mice, a model for type 2 diabetes. Notably, MR significantly improved working as well as long-term memory in db/db mice, accompanied by increased PSD-95 level and reduced neuroinflammatory factors, malondialdehyde (MDA), and 8-hydroxy-2'-deoxyguanosine (8-OHdG). We speculate that this effect may be mediated by MR activating hepatic fibroblast growth factor 21 (FGF21) and the brain FGFR1/AMPK/GLUT4 signaling pathway to enhance brain glucose metabolism. To further delineate the mechanism, we used intracerebroventricular injection of adeno-associated virus to specifically knock down FGFR1 in the brain to verify the role of FGFR1 in MR-mediated DACI. It was found that the positive effects of MR on DACI were offset, reflected in decreased cognitive function, impaired synaptic plasticity, upregulated neuroinflammation, and balanced enzymes regulating reactive oxygen species (Sod1, Sod2, Nox4). Of note, the FGFR1/AMPK/GLUT4 signaling pathway and brain glucose metabolism were inhibited. In summary, our study demonstrated that MR increased peripheral insulin sensitivity, activated brain FGFR1/AMPK/GLUT4 signaling through FGF21, maintained normal glucose metabolism and redox balance in the brain, and thereby alleviated DACI. These results provide new insights into the effects of MR diet on cognitive dysfunction caused by impaired brain energy metabolism. Show less
📄 PDF DOI: 10.1016/j.redox.2024.103390
FGFR1

EphB1 causes retinal damage through inflammatory pathways in the retina and retinal Müller cells.

Li Liu, Youde Jiang, Mohamed Al-Shabrawey +3 more · 2024 · Molecular vision · added 2026-04-24
To examine whether increased ephrin type-B receptor 1 (EphB1) leads to inflammatory mediators in retinal Müller cells. Diabetic human and mouse retinal samples were examined for EphB1 protein levels. Show more
To examine whether increased ephrin type-B receptor 1 (EphB1) leads to inflammatory mediators in retinal Müller cells. Diabetic human and mouse retinal samples were examined for EphB1 protein levels. Rat Müller cells (rMC-1) were grown in culture and treated with EphB1 siRNA or ephrin B1-Fc to explore inflammatory mediators in cells grown in high glucose. An EphB1 overexpression adeno-associated virus (AAV) was used to increase EphB1 in Müller cells in vivo. Ischemia/reperfusion (I/R) was performed on mice treated with the EphB1 overexpression AAV to explore the actions of EphB1 on retinal neuronal changes in vivo. EphB1 protein levels were increased in diabetic human and mouse retinal samples. Knockdown of EphB1 reduced inflammatory mediator levels in Müller cells grown in high glucose. Ephrin B1-Fc increased inflammatory proteins in rMC-1 cells grown in normal and high glucose. Treatment of mice with I/R caused retinal thinning and loss of cell numbers in the ganglion cell layer. This was increased in mice exposed to I/R and treated with the EphB1 overexpressing AAVs. EphB1 is increased in the retinas of diabetic humans and mice and in high glucose-treated Müller cells. This increase leads to inflammatory proteins. EphB1 also enhanced retinal damage in response to I/R. Taken together, inhibition of EphB1 may offer a new therapeutic option for diabetic retinopathy. Show less
no PDF
RMC1

Global RNA Interaction and Transcriptome Profiles Demonstrate the Potential Anti-Oncogenic Targets and Pathways of RBM6 in HeLa Cells.

Ping Peng, Qingqing Yin, Wei Sun +4 more · 2024 · Frontiers in bioscience (Landmark edition) · added 2026-04-24
The fate and functions of RNAs are coordinately regulated by RNA-binding proteins (RBPs), which are often dysregulated in various cancers. Known as a splicing regulator, RNA-binding motif protein 6 (R Show more
The fate and functions of RNAs are coordinately regulated by RNA-binding proteins (RBPs), which are often dysregulated in various cancers. Known as a splicing regulator, RNA-binding motif protein 6 (RBM6) harbors tumor-suppressor activity in many cancers; however, there is a lack of research on the molecular targets and regulatory mechanisms of RBM6. In this study, we constructed an Using The Cancer Genome Atlas dataset, we found that higher expression of In summary, our study highlights the important role of RBM6, as well as the downstream targets and regulated pathways, suggesting the potential regulatory mechanisms of RBM6 in the development of cancer. Show less
no PDF DOI: 10.31083/j.fbl2909330
RBM6

Transcriptomic and proteomic features of a mouse model of sperm DNA damage induced by benzo(a)pyrene.

Chenming Zhang, Yunfeng Ma, Wenbang Liu +5 more · 2024 · Reproductive toxicology (Elmsford, N.Y.) · Elsevier · added 2026-04-24
This study replicated a mouse model of sperm DNA damage induced by benzo(a)pyrene (BaP), and the transcriptomic and proteomic features of the model were examined to clarify the pathways related to BaP Show more
This study replicated a mouse model of sperm DNA damage induced by benzo(a)pyrene (BaP), and the transcriptomic and proteomic features of the model were examined to clarify the pathways related to BaP-induced damage to sperm DNA. Male mice in the BaP group were subjected to BaP at a dosage of 100 mg/kg/d or an equivalent quantity of saline solution in the control group for 60 days. Subsequently, the DNA fragmentation index (DFI) in sperm was assessed using a sperm chromatin structure assay (SCSA). RNA-seq and data-independent acquisition (DIA) were used to identify the mRNA and protein expression patterns in the testis. The sperm DFI significantly increased in the BaP group. Compared to the control group, the BaP group exhibited differential expression of 240 genes (referred to as DEGs) and 616 proteins (referred to as DEPs). These molecules included Aldh1a1, Cyb5r3, Fads1, Oxsm, Rcn3, and Prss45. Pathways in cancer, the PI3K-Akt signaling pathway, metabolic pathways, and the MAPK signaling pathway were the primary areas where these genes showed enrichment. BaP can damage the DNA of sperm and affect metabolism, the PI3K-Akt pathway, and pathways associated with cancer signaling. Show less
no PDF DOI: 10.1016/j.reprotox.2024.108596
FADS1

Reprogramming of DNA methylation patterns mediates perfluorooctane sulfonate-induced fetal cardiac dysplasia.

Min Qiu, Jing Chen, Mingqin Liu +7 more · 2024 · The Science of the total environment · Elsevier · added 2026-04-24
Prenatal exposure to perfluorooctane sulfonate (PFOS) is associated with adverse health effects, including congenital heart disease, yet the underlying mechanisms remain elusive. Herein, we aimed to e Show more
Prenatal exposure to perfluorooctane sulfonate (PFOS) is associated with adverse health effects, including congenital heart disease, yet the underlying mechanisms remain elusive. Herein, we aimed to evaluate the embryotoxicity of PFOS using C57BL/6 J mice to characterize fetal heart defects after PFOS exposure, with the induction of human embryonic stem cells (hESC) into cardiomyocytes (CMs) as a model of early-stage heart development. We also performed DNA methylation analysis to clarify potential underlying mechanisms and identify targets of PFOS. Our results revealed that PFOS caused septal defects and excessive ventricular trabeculation cardiomyopathy at 5 mg/kg/day in embryonic mice and inhibited the proliferation and pluripotency of ESCs at concentrations >20 μM. Moreover, it decreased the beating rate and the population of CMs during cardiac differentiation. Decreases were observed in the abundances of NPPA+ trabecular and HEY2+ compact CMs. Additionally, DNA methyl transferases and ten-eleven translocation (TET) dioxygenases were regulated dynamically by PFOS, with TETs inhibitor treatment inducing significant decreases similar as PFOS. 850 K DNA methylation analysis combined with expression analysis revealed several potential targets of PFOS, including SORBS2, FHOD1, SLIT2, SLIT3, ADCY9, and HDAC9. In conclusion, PFOS may reprogram DNA methylation, especially demethylation, to induce cardiac toxicity, causing ventricular defects in vivo and abnormal cardiac differentiation in vitro. Show less
no PDF DOI: 10.1016/j.scitotenv.2024.170905
HEY2

Correction: Resistance to FGFR1-targeted therapy leads to autophagy via TAK1/AMPK activation in gastric cancer.

Rui Peng, Yan Chen, Liangnian Wei +6 more · 2024 · Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · Springer · added 2026-04-24
no PDF DOI: 10.1007/s10120-024-01489-3
FGFR1

Genetic association of lipids and lipid lowering drug target genes with sarcoidosis.

Wei Tan, Zicheng Liang, Yu Liu +2 more · 2024 · Scientific reports · Nature · added 2026-04-24
To determine the potential causal association between serum lipid levels and sarcoidosis, and to investigate the potential impact of lipid lowering agents on sarcoidosis. Two-sample Mendelian randomiz Show more
To determine the potential causal association between serum lipid levels and sarcoidosis, and to investigate the potential impact of lipid lowering agents on sarcoidosis. Two-sample Mendelian randomization (TSMR) was used to investigate the association between lipid levels (including LDL-c, HDL-c, TG, and TC) and sarcoidosis risk. In addition, we used Mendelian drug target randomization (DMR) to analyze the relationship between drug targets for lowering LDL-c levels (HMGCR, PCSK9, and NPC1L1) and drug targets for lowering TG levels (LPL and APOC3) and the risk of sarcoidosis. According to the TSMR analysis, a positive correlation was observed between the serum LDL-c concentration and sarcoidosis incidence (n = 153 SNPs, OR = 1.232, 95% CI = 1.018-1.491; p = 0.031). Similarly, serum TG concentration was found to be positively associated with sarcoidosis (n = 52 SNPs, OR = 1.287, 95% CI = 1.024-1.617; p = 0.03). The DMR results demonstrated a positive correlation between PCSK9-mediated serum LDL-c levels and sarcoidosis (n = 35 SNPs, OR = 1.681, 95% CI = 1.220-2.315; p = 0.001). Similarly, serum TG levels mediated by LPL were positively associated with sarcoidosis (n = 28 SNPs, OR = 1.569, 95% CI = 1.223-2.012; p = 0.0003). This study suggested that elevated serum TG and LDL-c levels may increase the risk of sarcoidosis. PCSK9-mediated reduction of LDL-C levels (simulating the effects of PCSK9 inhibitors) and LPL-mediated reduction of TG levels (simulating the effects of LPL-related lipid lowering drugs) can decrease the risk of developing sarcoidosis. Show less
📄 PDF DOI: 10.1038/s41598-024-75322-3
APOC3

Integrated Single-Cell Analysis Revealed Novel Subpopulations of Foamy Macrophages in Human Atherosclerotic Plaques.

Yunrui Lu, Shuang Wu, Shiyu Zhu +7 more · 2024 · Biomolecules · MDPI · added 2026-04-24
Foam cell formation is a hallmark of atherosclerosis, yet the cellular complexity within foam cells in human plaques remains unexplored. Here, we integrate published single-cell RNA-sequencing, spatia Show more
Foam cell formation is a hallmark of atherosclerosis, yet the cellular complexity within foam cells in human plaques remains unexplored. Here, we integrate published single-cell RNA-sequencing, spatial transcriptomic, and chromatin accessibility sequencing datasets of human atherosclerotic lesions across eight distinct studies. Through this large-scale integration of patient-derived information, we identified foamy macrophages enriched for genes characteristic of the foamy signature. We further re-clustered the foamy macrophages into five unique subsets with distinct potential functions: (i) pro-foamy macrophages, exhibiting relatively high inflammatory and adhesive properties; (ii) phagocytic foamy macrophages, specialized in efferocytosis; (iii) high-efflux foamy macrophages marked by high Show less
no PDF DOI: 10.3390/biom14121606
NR1H3

Integrating tertiary lymphoid structure-associated genes into computational models to evaluate prognostication and immune infiltration in pancreatic cancer.

Ying Ma, Xuesong Li, Jin Zhang +6 more · 2024 · Journal of leukocyte biology · Oxford University Press · added 2026-04-24
Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor response to all therapeutic modalities and dismal prognosis. The presence of tertiary lymphoid structures (TLSs) in various solid cance Show more
Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor response to all therapeutic modalities and dismal prognosis. The presence of tertiary lymphoid structures (TLSs) in various solid cancers is of crucial prognostic significance, highlighting the intricate interplay between the tumor microenvironment and immune cells aggregation. However, the extent to which TLSs and immune status affect PDAC prognosis remains incompletely understood. Here, we sought to unveil the unique properties of TLSs in PDAC by leveraging both single-cell and bulk transcriptomics, culminating in a risk model that predicts clinical outcomes. We used TLS scores based on a 12-gene (CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13) and 9-gene (PTGDS, RBP5, EIF1AY, CETP, SKAP1, LAT, CCR6, CD1D, and CD79B) signature, respectively, and examined their distribution in cell clusters of single-cell data from PDAC samples. The markers involved in these clusters were selected to develop a prognostic model using The Cancer Genome Atlas Program database as the training cohort and Gene Expression Omnibus database as the validation cohort. Further, we compared the immune infiltration, drug sensitivity, and enriched and differentially expressed genes between the high- and low-risk groups in our model. Therefore, we established a risk model that has significant implications for the prognostic assessment of PADC patients with remarkable differences in immune infiltration and chemosensitivity between the low- and high-risk groups. This paradigm established by TLS-related cell marker genes provides a prognostic prediction and a panel of novel therapeutic targets for exploring potential immunotherapy. Show less
no PDF DOI: 10.1093/jleuko/qiae067
CETP

Joint Genomic and Transcriptomic Analysis Reveals Candidate Genes Associated with Plumage Color Traits in Matahu Ducks.

Pengwei Ren, Liu Yang, Muhammad Zahoor Khan +8 more · 2024 · Animals : an open access journal from MDPI · MDPI · added 2026-04-24
Plumage color is a key trait for identifying waterfowl breeds with significant economic importance. A white-feathered group has recently emerged within the native Matahu duck population, presenting an Show more
Plumage color is a key trait for identifying waterfowl breeds with significant economic importance. A white-feathered group has recently emerged within the native Matahu duck population, presenting an opportunity for breeding new lines. However, the genetic basis for this plumage variation is still unknown, necessitating further research. This study aims to identify the genetic mechanisms underlying the emergence of white-feathered individuals in the Matahu duck population through combined genome and transcriptome analysis, providing insights for selective breeding and the development of new white-feathered lines. In this study, a total of 1344 selected genes and 1406 significantly differentially expressed genes were identified through selection signal analysis and transcriptomic analysis, respectively. The functional enrichment of these genes revealed several key signaling pathways, including those related to cGMP-PKG, cAMP, PI3K-Akt, and MAPK. Furthermore, important candidate genes involved in melanin biosynthesis, such as Show less
📄 PDF DOI: 10.3390/ani14213111
GPRC5B

Suppression of AGRN enhances CD8+ T cell recruitment and inhibits breast cancer progression.

Jing Tao, Li Shen, Minyu Zhuang +4 more · 2024 · FASEB journal : official publication of the Federation of American Societies for Experimental Biology · added 2026-04-24
Breast cancer (BC) stands as a prominent contributor to global cancer-related mortality, with an increasing incidence annually. This study aims to investigate AGRN gene expression in BC, as well as ex Show more
Breast cancer (BC) stands as a prominent contributor to global cancer-related mortality, with an increasing incidence annually. This study aims to investigate AGRN gene expression in BC, as well as explore its influence on the tumor immune microenvironment. AGRN displayed a pronounced upregulation in BC tissues relative to paracancerous tissues. Single-cell RNA analysis highlighted AGRN-specific elevation within cancer cell clusters and also showed expression expressed in stromal as well as immune cell clusters. AGRN upregulation was positively correlated with clinicopathological stage and negatively correlated with BC prognosis. As revealed by the in vitro experiment, AGRN knockdown effectively hinders BC cells in terms of proliferation, invasion as well as migration. AGRN protein, which may interact with EXT1, LRP4, RAPSN, etc., was primarily distributed in the cell cytoplasm. Notably, immune factors might interact with AGRN in BC, evidenced by its discernible associations with immunofactors like IL10, CD274, and PVRL2. Mass spectrometry and immunohistochemistry revealed that the reduction of AGRN led to an increase in CD8 Show less
no PDF DOI: 10.1096/fj.202302288R
EXT1

Unraveling the rapid progression of non-target lesions: risk factors and the therapeutic potential of PCSK9 inhibitors in post-PCI patients.

Jiajie Mei, Xiaodan Fu, Zhenzhu Liu +9 more · 2024 · BMC cardiovascular disorders · BioMed Central · added 2026-04-24
Rapid progression of non-target lesions (NTLs) leads to a high incidence of NTL related cardiac events post-PCI, which accounting half of the recurrent cardiac events. It is important to identify the Show more
Rapid progression of non-target lesions (NTLs) leads to a high incidence of NTL related cardiac events post-PCI, which accounting half of the recurrent cardiac events. It is important to identify the risk factors and establish an accurate clinical prediction model for the rapid progression of NTLs post-PCI. PCSK9 inhibitors lower LDL-c levels significantly, also show the anti-inflammation effect, and may have the potential to reduce the rapid progression of NTLs post-PCI. We tried to test this hypothesis and explore the potential mechanisms. This retrospective study included 1250 patients who underwent the first PCI and underwent repeat coronary angiography for recurrence of chest pain within 24 months. General characteristics, laboratory tests and inflammatory factors(IL-10, IL-6, IL-8, IL-1β, sIL-2R, and TNF-α) were collected. Machine learning (LASSO regression) was mainly employed to select the important characteristic risk factors for the rapid progression of NTLs post-PCI and build prediction models. Finally, mediator analysis was employed to explore the potential mechanisms by which PCSK9 inhibitors reduce the rapid progression of NTLs post-PCI. There were more diabetes, less beta-blockers and PCSK9 inhibitors application, higher HbA1c, LDL-c, ApoB, TG, TC, uric acid, hs-CRP, TNF-α, IL-6, IL-8, and sIL-2R in NTL progressed group. LDL-c, hs-CRP, IL-8, and sIL-2R were characteristic risk factors for the rapid progression of NTLs post-PCI, combining LDL-c, hs-CRP, IL-8, and sIL-2R builds the optimal model for predicting the rapid progression of NTLs post-PCI (AUC = 0.632). LDL-c had a clear and incomplete mediating effect (95% CI, mediating effect: 51.56%) in the reduction of the progression of NTLs by PCSK9 inhibitors, and there was a possible mediating effect of IL-8 (90% CI), and sIL-2R (90% CI). LDL-c, hs-CRP, IL-8, and sIL-2R may be the key characteristic risk factors for the rapid progression of NTLs post-PCI, and combining these parameters might predict the rapid progression of NTLs post-PCI. The application of PCSK9 inhibitors had a negative correlation with the rapid progression of NTLs. In addition to the significant LDL-c-lowering, PCSK9 inhibitors may reduce the rapid progression of NTLs by reducing local inflammation of plaque. ChiCTR2200058529; Date of registration: 2022-04-10. Show less
📄 PDF DOI: 10.1186/s12872-024-04186-2
APOB

Functional coupling between MC4R and Kir7.1 contributes to clozapine-induced hyperphagia.

Li Li, Ciria C Hernandez, Luis E Gimenez +6 more · 2024 · bioRxiv : the preprint server for biology · Cold Spring Harbor Laboratory · added 2026-04-24
Most antipsychotic drugs (APDs) induce hyperphagia and weight gain. However, the neural mechanisms are poorly understood, partly due to challenges replicating their metabolic effects in rodents. Here, Show more
Most antipsychotic drugs (APDs) induce hyperphagia and weight gain. However, the neural mechanisms are poorly understood, partly due to challenges replicating their metabolic effects in rodents. Here, we report a new mouse model that recapitulates overeating induced by clozapine, a widely prescribed APD. Our study shows that clozapine boosts food intake by inhibiting melanocortin 4 receptor (MC4R) expressing neurons in the paraventricular nucleus of the hypothalamus. Interestingly, neither clozapine nor risperidone, another commonly used APD, affects receptor-ligand binding or the canonical Gαs signaling of MC4R. Instead, they inhibit neuronal activity by enhancing the coupling between MC4R and Kir7.1, leading to the open state of the inwardly rectifying potassium channel. Deletion of Show less
no PDF DOI: 10.1101/2024.06.07.597973
MC4R

Roles of Single Nucleotide Polymorphisms of

Shajidan Abudureyimu, Chunhui He, Dilihumaer Abulaiti +8 more · 2024 · Reviews in cardiovascular medicine · added 2026-04-24
This study aims to investigate the association between nine tag single nucleotide polymorphisms (SNPs) in the A case-control study was conducted to investigate the association between CAD and Results Show more
This study aims to investigate the association between nine tag single nucleotide polymorphisms (SNPs) in the A case-control study was conducted to investigate the association between CAD and Results of the polymorphism study indicated that the The Show less
📄 PDF DOI: 10.31083/j.rcm2504147
APOB

OTUD4 promotes the progression of glioblastoma by deubiquitinating CDK1 and activating MAPK signaling pathway.

Mingxin Ci, Gaichao Zhao, Chongyang Li +9 more · 2024 · Cell death & disease · Nature · added 2026-04-24
Glioblastoma, IDH-Wild type (GBM, CNS WHO Grade 4) is a highly heterogeneous and aggressive primary malignant brain tumor with high morbidity, high mortality, and poor patient prognosis. The global bu Show more
Glioblastoma, IDH-Wild type (GBM, CNS WHO Grade 4) is a highly heterogeneous and aggressive primary malignant brain tumor with high morbidity, high mortality, and poor patient prognosis. The global burden of GBM is increasing notably due to limited treatment options, drug delivery problems, and the lack of characteristic molecular targets. OTU deubiquitinase 4 (OTUD4) is a potential predictive factor for several cancers such as breast cancer, liver cancer, and lung cancer. However, its function in GBM remains unknown. In this study, we found that high expression of OTUD4 is positively associated with poor prognosis in GBM patients. Moreover, we provided in vitro and in vivo evidence that OTUD4 promotes the proliferation and invasion of GBM cells. Mechanism studies showed that, on the one hand, OTUD4 directly interacts with cyclin-dependent kinase 1 (CDK1) and stabilizes CDK1 by removing its K11, K29, and K33-linked polyubiquitination. On the other hand, OTUD4 binds to fibroblast growth factor receptor 1 (FGFR1) and reduces FGFR1's K6 and K27-linked polyubiquitination, thereby indirectly stabilizing CDK1, ultimately influencing the activation of the downstream MAPK signaling pathway. Collectively, our results revealed that OTUD4 promotes GBM progression via OTUD4-CDK1-MAPK axis, and may be a prospective therapeutic target for GBM treatment. Show less
📄 PDF DOI: 10.1038/s41419-024-06569-x
FGFR1

Identification of a Compound Heterozygous LMF1 Variants in a Patient with Severe Hypertriglyceridemia - Case Report and Literature Review.

Conghui Cao, Yuqi Liu, Lu Liu +1 more · 2024 · Journal of atherosclerosis and thrombosis · added 2026-04-24
Familial chylomicronemia syndrome (FCS) and multifactorial chylomicronemia (MCM), characterized by highly variable triglyceride levels with acute episodes of severe hypertriglyceridemia (HTG), are cau Show more
Familial chylomicronemia syndrome (FCS) and multifactorial chylomicronemia (MCM), characterized by highly variable triglyceride levels with acute episodes of severe hypertriglyceridemia (HTG), are caused by rare variants in genes associated with the catabolism of circulating lipoprotein triglycerides, mainly including LPL, APOC2, APOA5, GPIHBP1, and LMF1. Among them, the LMF1 gene only accounts for 1%. This study described a Chinese patient with severe HTG carrying compound heterozygous variants of a rare nonsense variant p.W168X in exon 3 and a missense variant p.R416Q in exon 9 in the LMF1 gene. These heterozygous variants account for his family's decreased lipase activity and mass, which caused the FCS phenotype. Show less
📄 PDF DOI: 10.5551/jat.64697
APOA5

Study of the Interactions between Muscle Fatty Acid Composition, Meat Quality-Related Genes and the Ileum Microbiota in Tibetan Sheep at Different Ages.

Fanxiong Wang, Yuzhu Sha, Xiu Liu +10 more · 2024 · Foods (Basel, Switzerland) · MDPI · added 2026-04-24
The intestinal microbiota of ruminants is an important factor affecting animal production and health. Research on the association mechanism between the intestinal microbiota and meat quality of rumina Show more
The intestinal microbiota of ruminants is an important factor affecting animal production and health. Research on the association mechanism between the intestinal microbiota and meat quality of ruminants will play a positive role in understanding the formation mechanism of meat quality in ruminants and improving production efficiency. In this study, the fatty acid composition and content, expression of related genes, and structural characteristics of the ileum microbiota of ewes of Tibetan sheep at different ages (4 months, 1.5 years, 3.5 years, and 6 years) were detected and analyzed. The results revealed significant differences in fatty acid composition and content in the muscle of Tibetan sheep at different ages ( Show less
📄 PDF DOI: 10.3390/foods13050679
LPL

CRKL dictates anti-PD-1 resistance by mediating tumor-associated neutrophil infiltration in hepatocellular carcinoma.

Peiyi Xie, Mincheng Yu, Bo Zhang +19 more · 2024 · Journal of hepatology · Elsevier · added 2026-04-24
The effectiveness of immune checkpoint inhibitor (ICI) therapy for hepatocellular carcinoma (HCC) is limited by treatment resistance. However, the mechanisms underlying immunotherapy resistance remain Show more
The effectiveness of immune checkpoint inhibitor (ICI) therapy for hepatocellular carcinoma (HCC) is limited by treatment resistance. However, the mechanisms underlying immunotherapy resistance remain elusive. We aimed to identify the role of CT10 regulator of kinase-like (CRKL) in resistance to anti-PD-1 therapy in HCC. Gene expression in HCC specimens from 10 patients receiving anti-PD-1 therapy was identified by RNA-sequencing. A total of 404 HCC samples from tissue microarrays were analyzed by immunohistochemistry. Transgenic mice (Alb-Cre/Trp53 CRKL was identified as a candidate anti-PD-1-resistance gene using a pooled genetic screen. CRKL overexpression nullifies anti-PD-1 treatment efficacy by mobilizing tumor-associated neutrophils (TANs), which block the infiltration and function of CD8 Activation of the CRKL/β-catenin/VEGFα and CXCL1 axis is a critical obstacle to successful anti-PD-1 therapy. Therefore, CRKL inhibitors combined with anti-PD-1 could be useful for the treatment of HCC. Here, we found that CRKL was overexpressed in anti-PD-1-resistant hepatocellular carcinoma (HCC) and that CRKL upregulation promotes anti-PD-1 resistance in HCC. We identified that upregulation of the CRKL/β-catenin/VEGFα and CXCL1 axis contributes to anti-PD-1 tolerance by promoting infiltration of tumor-associated neutrophils. These findings support the strategy of bevacizumab-based immune checkpoint inhibitor combination therapy, and CRKL inhibitors combined with anti-PD-1 therapy may be developed for the treatment of HCC. Show less
no PDF DOI: 10.1016/j.jhep.2024.02.009
AXIN1

Identification of genomic regions associated with fatty acid metabolism across blood, liver, backfat and muscle in pigs.

Junhui Liu, Cristina Sebastià, Teodor Jové-Juncà +7 more · 2024 · Genetics, selection, evolution : GSE · BioMed Central · added 2026-04-24
The composition and distribution of fatty acids (FA) are important factors determining the quality, flavor, and nutrient value of meat. In addition, FAs synthesized in the body participate in energy m Show more
The composition and distribution of fatty acids (FA) are important factors determining the quality, flavor, and nutrient value of meat. In addition, FAs synthesized in the body participate in energy metabolism and are involved in different regulatory pathways in the form of signaling molecules or by acting as agonist or antagonist ligands of different nuclear receptors. Finally, synthesis and catabolism of FAs affect adaptive immunity by regulating lymphocyte metabolism. The present study performed genome-wide association studies using FA profiles of blood, liver, backfat and muscle from 432 commercial Duroc pigs. Twenty-five genomic regions located on 15 Sus scrofa chromosomes (SSC) were detected. Annotation of the quantitative trait locus (QTL) regions identified 49 lipid metabolism-related candidate genes. Among these QTLs, four were identified in more than one tissue. The ratio of C20:4n-6/C20:3n-6 was associated with the region on SSC2 at 7.56-14.26 Mb for backfat, liver, and muscle. Members of the fatty acid desaturase gene cluster (FADS1, FADS2, and FADS3) are the most promising candidate genes in this region. Two QTL regions on SSC14 (103.81-115.64 Mb and 100.91-128.14 Mb) were identified for FA desaturation in backfat and muscle. In addition, two separate regions on SSC9 at 0 - 14.55 Mb and on SSC12 at 0-1.91 Mb were both associated with the same multiple FA traits for backfat, with candidate genes involved in de novo FA synthesis and triacylglycerol (TAG) metabolism, such as DGAT2 and FASN. The ratio C20:0/C18:0 was associated with the region on SSC5 at 64.84-78.32 Mb for backfat. Furthermore, the association of the C16:0 content with the region at 118.92-123.95 Mb on SSC4 was blood specific. Finally, candidate genes involved in de novo lipogenesis regulate T cell differentiation and promote the generation of palmitoleate, an adipokine that alleviates inflammation. Several SNPs and candidate genes were associated with lipid metabolism in blood, liver, backfat, and muscle. These results contribute to elucidating the molecular mechanisms implicated in the determination of the FA profile in different pig tissues and can be useful in selection programs that aim to improve health and energy metabolism in pigs. Show less
📄 PDF DOI: 10.1186/s12711-024-00933-3
FADS1

Characterizing and identifying of miRNAs involved in berberine modulating glucose metabolism of Megalobrama amblycephala.

Mingyang Liu, Chang He, Tingting Zhu +8 more · 2024 · Fish physiology and biochemistry · Springer · added 2026-04-24
The present study, as one part of a larger project that aimed to investigate the effects of dietary berberine (BBR) on fish growth and glucose regulation, mainly focused on whether miRNAs involve in B Show more
The present study, as one part of a larger project that aimed to investigate the effects of dietary berberine (BBR) on fish growth and glucose regulation, mainly focused on whether miRNAs involve in BBR's modulation of glucose metabolism in fish. Blunt snout bream Megalobrama amblycephala (average weight of 20.36 ± 1.44 g) were exposed to the control diet (NCD, 30% carbohydrate), the high-carbohydrate diet (HCD, 43% carbohydrate) and the berberine diet (HCB, HCD supplemented with 50 mg/kg BBR). After 10 weeks' feeding trial, intraperitoneal injection of glucose was conducted, and then, the plasma and liver were sampled at 0 h, 1 h, 2 h, 6 h, and 12 h. The results showed the plasma glucose levels in all groups rose sharply and peaked at 1 h after glucose injection. Unlike the NCD and HCB groups, the plasma glucose in the HCD group did not decrease after 1 h, while remained high level until at 2 h. The NCD group significantly increased liver glycogen content at times 0-2 h compared to the other two groups and then liver glycogen decreased sharply until at times 6-12 h. To investigate the role of BBR that may cause the changes in plasma glucose and liver glycogen, miRNA high-throughput sequencing was performed on three groups of liver tissues at 2 h time point. Eventually, 20 and 12 differentially expressed miRNAs (DEMs) were obtained in HCD vs NCD and HCB vs HCD, respectively. Through function analyzing, we found that HCD may affect liver metabolism under glucose loading through the NF-κB pathway; and miRNAs regulated by BBR mainly play roles in adipocyte lipolysis, niacin and nicotinamide metabolism, and amino acid transmembrane transport. In the functional exploration of newly discovered novel:Chr12₁₈₈₉₂, we found its target gene, adenylate cyclase 3 (adcy3), was widely involved in lipid decomposition, amino acid metabolism, and other pathways. Furthermore, a targeting relationship of novel:Chr12₁₈₈₉₂ and adcy3 was confirmed by double luciferase assay. Thus, BBR may promote novel:Chr12₁₈₈₉₂ to regulate the expression of adcy3 and participate in glucose metabolism. Show less
📄 PDF DOI: 10.1007/s10695-024-01362-1
ADCY3

Dicerandrol C Suppresses Proliferation and Induces Apoptosis of HepG2 and Hela Cancer Cells by Inhibiting Wnt/β-Catenin Signaling Pathway.

Dongdong Zhou, Dandan Chen, Jingwan Wu +3 more · 2024 · Marine drugs · MDPI · added 2026-04-24
Overwhelming evidence points to an aberrant Wnt/β-catenin signaling as a critical factor in hepatocellular carcinoma (HCC) and cervical cancer (CC) pathogenesis. Dicerandrol C (DD-9), a dimeric tetrah Show more
Overwhelming evidence points to an aberrant Wnt/β-catenin signaling as a critical factor in hepatocellular carcinoma (HCC) and cervical cancer (CC) pathogenesis. Dicerandrol C (DD-9), a dimeric tetrahydroxanthenone isolated from the endophytic fungus Show less
📄 PDF DOI: 10.3390/md22060278
AXIN1

Causal effects of circulating lipids and lipid-lowering drugs on the risk of atopic dermatitis: a mendelian randomization study.

Guangquan Xu, Mengyang Chu, Shengxian Shen +10 more · 2024 · Archives of dermatological research · Springer · added 2026-04-24
Lipid metabolism disorders are frequently noted in atopic dermatitis (AD) patients, prompting the long-term use of lipid-lowering drugs. However, the causal effects of circulating lipids and different Show more
Lipid metabolism disorders are frequently noted in atopic dermatitis (AD) patients, prompting the long-term use of lipid-lowering drugs. However, the causal effects of circulating lipids and different lipid-lowering drugs on the risk of AD are not thoroughly understood. Using publicly available genome-wide association studies (GWAS) summary data from two different cohorts, a series of Mendelian randomization (MR) analyses were conducted to explore the causal effects of genetically proxied circulating lipids and lipid-lowering drugs on the risk of AD. Statistically, the random-effects inverse-variance-weighted (IVW) model was used as main analysis and several methods were conducted for sensitivity analysis to test the robustness of our results. Our findings revealed reduced risks of AD related to genetically proxied subtilisin/kexin type 9 (PCSK9) inhibition and lipoprotein lipase (LPL) agonist, while an increased AD risk associated with Niemann-Pick C1-like 1 (NPC1L1) inhibition. Circulating lipids and other drug targets did not show significant associations with AD risk. These results were replicated in the validation cohort; sensitivity analyses confirmed the robustness. This MR study suggests that, independent of circulating lipids, the use of PCSK9 inhibitors and LPL agonists may be associated with a decreased risk of AD, while inhibition of NPC1L1 is implicated in an increased risk. These findings may help optimize personalized selection of lipid-lowering drugs for AD patients and those at risk of AD. Show less
📄 PDF DOI: 10.1007/s00403-024-03635-4
LPL

Single-nucleus multiome analysis of human cerebellum in Alzheimer's disease-related dementia.

Feixiong Cheng, Yayan Feng, Margaret Flanagan +14 more · 2024 · Research square · added 2026-04-24
Although human cerebellum is known to be neuropathologically impaired in Alzheimer's disease (AD) and AD-related dementias (ADRD), the cell type-specific transcriptional and epigenomic changes that co Show more
Although human cerebellum is known to be neuropathologically impaired in Alzheimer's disease (AD) and AD-related dementias (ADRD), the cell type-specific transcriptional and epigenomic changes that contribute to this pathology are not well understood. Here, we report single-nucleus multiome (snRNA-seq and snATAC-seq) analysis of 103,861 nuclei isolated from cerebellum from 9 human cases of AD/ADRD and 8 controls, and with frontal cortex of 6 AD donors for additional comparison. Using peak-to-gene linkage analysis, we identified 431,834 significant linkages between gene expression and cell subtype-specific chromatin accessibility regions enriched for candidate cis-regulatory elements (cCREs). These cCREs were associated with AD/ADRD-specific transcriptomic changes and disease-related gene regulatory networks, especially for RAR Related Orphan Receptor A (RORA) and E74 Like ETS Transcription Factor 1 (ELF1) in cerebellar Purkinje cells and granule cells, respectively. Trajectory analysis of granule cell populations further identified disease-relevant transcription factors, such as RORA, and their regulatory targets. Finally, we prioritized two likely causal genes, including Seizure Related 6 Homolog Like 2 (SEZ6L2) in Purkinje cells and KAT8 Regulatory NSL Complex Subunit 1 (KANSL1) in granule cells, through integrative analysis of cCREs derived from snATAC-seq, genome-wide AD/ADRD loci, and Hi-C looping data. This first cell subtype-specific regulatory landscape in the human cerebellum identified here offer novel genomic and epigenomic insights into the neuropathology and pathobiology of AD/ADRD and other neurological disorders if broadly applied. Show less
no PDF DOI: 10.21203/rs.3.rs-4871032/v1
KANSL1

Deubiquitinating enzyme OTUD4 regulates metastasis in triple-negative breast cancer by stabilizing Snail1.

Xiuqing Ma, Rui Wan, Yalei Wen +3 more · 2024 · Experimental cell research · Elsevier · added 2026-04-24
Metastasis is the primary cause of cancer-related deaths and remains poorly understood. Deubiquitinase OTU domain containing 4 (OTUD4) has been reported to regulate antiviral immune responses and resi Show more
Metastasis is the primary cause of cancer-related deaths and remains poorly understood. Deubiquitinase OTU domain containing 4 (OTUD4) has been reported to regulate antiviral immune responses and resistance to radio- or chemo-therapies in certain cancers. However, the role of OTUD4 in cancer metastasis remain unknown. Here, we demonstrate that the depletion of OTUD4 in triple-negative breast cancer (TNBC) cells markedly suppress cell clonogenic ability, migration, invasion and cancer stem cell population in vitro as well as metastasis in vivo. Mechanistically, the tumor promoting function of OTUD4 is mainly mediated by deuiquitinating and stabilizing Snail1, one key transcriptional factor in the epithelial-mesenchymal transition. The inhibitory effect of targeting OTUD4 could be largely reversed by the reconstitution of Snail1 in OTUD4-deficient cells. Overall, our study establishes the OTUD4-Snail1 axis as an important regulatory mechanism of breast cancer metastasis and provides a rationale for potential therapeutic interventions in the treatment of TNBC. Show less
no PDF DOI: 10.1016/j.yexcr.2023.113864
SNAI1

FGF1

Qunwu Tang, Zhewei Cheng, Sixiu Liu +6 more · 2024 · Biochemical pharmacology · Elsevier · added 2026-04-24
Translocator protein (18 kDa) (TSPO) plays an important role in retinal neuroinflammation in the early stage of diabetic retinopathy (DR). Studies have found that a FGF1 variant (FGF1
no PDF DOI: 10.1016/j.bcp.2024.116039
FGFR1

The association between blood glucose levels and lipids in the general adult population: results from NHANES (2005-2016).

Chenghao Yang, Zongjun Liu, Lingxiao Zhang +1 more · 2024 · Journal of health, population, and nutrition · BioMed Central · added 2026-04-24
Although abnormal lipid metabolism is one of the major risk factors for diabetes, the correlation between lipids and glucose is rarely discussed in the general population. The differences in lipid-glu Show more
Although abnormal lipid metabolism is one of the major risk factors for diabetes, the correlation between lipids and glucose is rarely discussed in the general population. The differences in lipid-glucose correlations across gender and ethnicity have been even more rarely studied. We examined the association between fasting blood glucose (FBG) and lipids, including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and apolipoprotein B (ApoB), using 6,093 participants aged 20 years or older from the National Health and Nutrition Examination Survey (NHANES). Analyses were performed using multiple logistic regression and generalised additive models. When other confounders were considered, we found that fasting glucose was positively correlated with triglycerides and negatively correlated with HDL-C, whereas total cholesterol, LDL-C cholesterol, and fasting glucose were related to each other in a U-curve fashion, with inflection points of 5.17 mmol/L and 2.3 mmol/L, respectively.This relationship persisted in subgroups of different sexes and races. A positive correlation was found between fasting glucose and ApoB, but subgroup analyses revealed that this relationship was not correlated across gender and race. In the general population, fasting blood glucose levels were positively correlated with TG, negatively correlated with HDL-C, and U-shaped with total cholesterol and LDL-C. The likelihood of developing diabetes was 40% higher when LDL-C was greater than 2.3 mmol/L than in patients with LDL-C less than 2.3 mmol/L. Show less
📄 PDF DOI: 10.1186/s41043-024-00660-x
APOB

Genetic Associations of Lipids and Lipid-Modifying Drug Targets With Type 2 Diabetes in the Chinese Population.

Jiawen Lu, Yang Liu, Zhenqian Wang +4 more · 2024 · JACC. Asia · Elsevier · added 2026-04-24
Dyslipidemia is a recognized risk factor for type 2 diabetes (T2D), yet the genetic basis and causal nature remain unclear, particularly in Chinese populations. The authors investigated the causal eff Show more
Dyslipidemia is a recognized risk factor for type 2 diabetes (T2D), yet the genetic basis and causal nature remain unclear, particularly in Chinese populations. The authors investigated the causal effects of genetically predicted lipid levels on T2D risk and explored the potential effects of lipid-modifying drugs. Leveraging data from the Kunshan Community cohort in China, we analyzed the associations between low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides (TGs) with T2D risk using genetic risk scores, 1-sample univariable, multivariable, and nonlinear Mendelian randomization (MR) analyses. Two-sample MR using summary-level data from Global Lipid Genetics Consortium and Biobank Japan was used for validation. Drug-target MR was used to examine the impact of lipid-modifying drug targets on T2D. Lower genetic risk scores of LDL-C (OR per SD: 0.97 [95% CI: 0.95-0.99]; Our findings suggested potential adverse effects of lower LDL-C, TG levels, as well as long-term use of APOC3 inhibitors on T2D risk in Chinese populations. These findings highlight the need for cautious lipid management strategies in T2D prevention. Show less
📄 PDF DOI: 10.1016/j.jacasi.2024.07.011
APOC3

The waist-to-height ratio is a good predictor for insulin resistance in women with polycystic ovary syndrome.

Mengyi Zhu, Kaiyue Wang, Jiaxing Feng +4 more · 2024 · Frontiers in endocrinology · Frontiers · added 2026-04-24
This study aimed to explore the role of the waist-to-height ratio (WHtR) in assessing insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS). We enrolled 882 PCOS-afflicted women in Show more
This study aimed to explore the role of the waist-to-height ratio (WHtR) in assessing insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS). We enrolled 882 PCOS-afflicted women in a cross-sectional analysis to evaluate the association of the WHtR with IR. Their demographic characteristics, anthropometric parameters, and fasting blood samples were collected and measured. Moreover, IR was evaluated by homeostatic model assessment of insulin resistance (HOMA-IR). We estimated the relationship between the WHtR and IR and the cut-off thresholds of the WHtR for IR using multivariable linear regression and logistic regression models, respectively. The prevalence rate of IR was 51.9%. The patients with PCOS and IR displayed significantly increased values for body mass index (BMI), waist circumference (WC), WHtR, systolic blood pressure (SBP), diastolic blood pressure (DBP), free androgen index (FAI), HOMA-IR, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein B (ApoB). However, the patients with PCOS and IR showed a reduction in estradiol (E2), luteinizing hormone (LH), LH/FSH ratio, sex hormone binding globulin (SHBG), and high-density lipoprotein (HDL-C) values than those without IR. Moreover, BMI (log-transformed), WC, and HOMA-IR (log-transformed) were positively correlated with the WHtR. When adjusting for potential confounding variables, the WHtR was significantly associated with HOMA-IR (log-transformed), with a standardized regression coefficient of 0.271. Furthermore, the WHtR was significantly associated with an increased risk of IR, with the adjusted odds ratio (OR) of 3.15 (WHtR multiplied by 10). Additionally, the WHtR helped to identify IR in women with PCOS with an optimal cut-off point of 0.519 (Youden index = 0.433). The WHtR had a positive association with IR in women with PCOS. Hence, we suggest that the WHtR, as a simple, practical, and reliable anthropometric measure, can be used to predict the risk of IR in patients with PCOS. Show less
📄 PDF DOI: 10.3389/fendo.2024.1502321
APOB