Han, Leng - McGovern Medical School at the University of Texas - Department of Biochemistry and Molecular Biology

个人简介

UNDERGRADUATE EDUCATION: B.S. in Biotechnology, Wuhan University, China GRADUATE EDUCATION: Ph.D. in Genetics, Chinese Academy of Sciences, China POSTGRADUATE TRAINING: Stanford University, Stanford, CA The University of Texas MD Anderson Cancer Center, Houston, TX ACADEMIC & ADMINISTRATIVE APPOINTMENTS: Assistant Professor, 2015-Present Department of Biochemistry and Molecular Biology McGovern Medical School at UTHealth PROFESSIONAL ORGANIZATIONS: NATIONAL Co-chair of Local Organization Committee, International Conference on Intelligent Biology and Medicine (ICIBM 2016), December 8-10, 2016, Houston, TX, USA

研究领域

Description of Research Our lab utilized the high-throughput technologies to dissect the molecular mechanism in complex diseases. We are interested on exciting topics but not limited to: 1. Integrative Analysis of Complex Diseases Recent advances in genomic technologies and the ensuing deluge of genomic information related to cancer have accelerated the convergence of discovery science with clinical medicine. Successful translations of genomics into therapeutics and diagnostics reinforce its potential for personalizing medicine. For example, as one of the most important cancer genomic data resources, the Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies (Han et al., Cancer Cell, 2015; Han et al., Nature Communications, 2014; TCGA, Nature Genetics, 2013). We also interested in other complex diseases, such as stem cells (Wang et al., Cell Stem Cell, 2014; Wang et al., Cell Stem Cell, 2013), cardiovascular diseases (Dey*, Han* et al., Circulation Research, 2013; Lan et al., Cell Stem Cell, 2013), psychiatric diseases (Luo*, Huang*, Han* et al., Schizophrenia Bulletin, 2014), etc. 2. High-throughput Data Mining High-throughput technologies have greatly improved our ability to evaluate the molecular changes that occur during various biological processes. With the development of next-generation sequencing, our understanding has been advanced through the use of a variety of platforms: methy-seq, ChIP-seq, exome-seq and RNA-seq. The large amount of publicly available next-generation sequencing data, such as datasets from TCGA and ENCODE, has created enormous opportunities for researchers to conduct genomic analysis beyond the traditional sequencing analysis. Transforming genomic information into biomedical and biological knowledge requires creative and innovative computational methods for all aspects of genomics. Therefore, the research in my lab will focus on computational analysis from genomic sequences to other post-genomic data, including both DNA and RNA sequences, protein profiling, and epigenetic profiling, in an ongoing effort to find hidden treasures (Han et al., Briefings in Bioinformatics, 2014; Samuels*, Han* et al., Trends in Genetics, 2013).

近期论文

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Zhou B, Wang L, Zhang S, Bennett BD, He F, Zhang Y, Xiong C, Han L, Diao L, Li P, Fargo DC, Cox AD, Hu G: INO80 governs superenhancer-mediated oncogenic transcription and tumor growth in melanoma. Genes & Development. 2016, 30:1440- 53 Liu X, Xiao ZD, Han L, Zhang J, Lee SW, Wang W, Lee H, Zhuang L, Chen J, Lin HK, Wang J, Liang H, Gan B: LncRNA NBR2 engages a metabolic checkpoint by regulating AMPK under energy stress. Nature Cell Biology, 2016, 18: 431-442 Redis RS, Vela LE, Lu W, Ferreira de Oliveira J, Ivan C, Rodriguez-Aguayo C, Adamoski D, Pasculli B, Taguchi A, Chen Y, Fernandez AF, Valledor L, Van Roosbroeck K, Chang S, Shah M, Kinnebrew G, Han L, Atlasi Y, Cheung LH, Huang GY, Monroig P, Ramirez MS, Catela Ivkovic T, Van L, Ling H, Gafà R, Kapitanovic S, Lanza G, Bankson JA, Huang P, Lai SY, Bast RC, Rosenblum MG, Radovich M, Ivan M, Bartholomeusz G, Liang H, Fraga MF, Widger WR, Hanash S, Berindan-Neagoe I, Lopez-Berestein G, Ambrosio AL, Gomes Dias SM, Calin GA: Allele-Specific Reprogramming of Cancer Metabolism by the Long Non-coding RNA CCAT2. Molecular Cell, 2016, 18: 520-534. Lin A, Li C, Xing Z, Ma G, Hu Q, Liang K, Han L, Wang C, Hawke DH, Wang S, Zhang Y, Wei Y, Park P, Zhou J, Zhou Y, Marks J, Hu Z, Hung MC, Liang H, Zou Y, Lin C, Yang L: The LINK-A lncRNA Activates Normoxic HIF1α Signaling in Triple-negative Breast Cancer. Nature Cell Biology, 2016, 18: 213-224. Jiang HJ, Lee HS, Burt BM, Lee GK, Yoon KA, Park YY, Sohn BH, Kim SB, Kim MS,Lee JM, Joo J, Kim SC, Yoo JS, Na KJ, Choi YL, Oark JL, Kim SY, Lee YS, Han L,Liang H, Mak D, Burks JK, Zo J, Sugarbaker DJ, Shim YM, Lee JS: Integrated Genomic Analysis of Recurrence-Associated Small Non-coding RNAs in 9 Oesophageal Cancer. Gut. 2016 (In press) Marini A, Lena AM, Panatta E, Ivan C, Han L, Liang H, Annicchiarico-Petruzzelli M, Di Daniele N, Calin GA, Candi E, Melino G: Ultraconserved long non-coding RNA uc.63 in breast cancer. Oncotarget. 2016 (In press) Xia S, Feng J, Lei L, Hu J, Xia L, Wang J, Xiang Y, Liu L, Zhong S, Han L#, He C#:Comprehensive characterization of tissue-specific circular RNAs in the human and mouse genomes. Briefings in Bioinformatics. 2016, bbw081. Gong J, Liu C, Liu W, Xiang Y, Diao L, Guo AY#, Han L#: LNCediting: a database for functional effects of RNA editing in lncRNAs. Nucleaid Acid Research, 2017: D79-D84 Wang Y, Xu X, Yu S, Kang KJ, Zhou Z, Han L, Tsang YH, Li J, Chen H, Mangala LS, Yuan Y, Eterovic AK, Lu Y, Sood AK, Scott KL, Mills GB, Liang H: Systematic characterization of A-to-I RNA editing hotspots in microRNAs across human cancers.Genome Research, 2017 The Cancer Genome Atlas Reseach Network (Including Han L): Integrated genomic and molecular characterization of cervical cancer. Nature, 2017,10.1038/nature21386 Lei L, Xia S, Liu D, Li X, Feng J, Zhu Y, Hu J, Xia L, Guo L, Chen F, Cheng H, Chen K, Hu H, Chen X, Li F, Zhong S, Mittal N, Yang G, Qian Z, Han L#, He C#: Genomewide characterization of lncRNAs in acute myeloid leukemia. Briefings in Bioinformatics, 2017, bbx007 Lin A, Hu Q, Li C, Xing Z, Ma G, Wang C, Li J, Yao J, Liang K, Wang S, Park P, Marks J, Zhou Y, Zhou J, Hung MC, Liang H, Hu Z, Shen H, Hawke DH, Han L, Zhou Y, Lin C, Yang L: PtdIns (3,4,5)P3-lncRNA interaction exaggerates AKT activation and confers resistance to AKT inhibitors. Nature Cell Biology, 2017, 19: 238-251 Li C, Wang S, Xing Z, Lin A, Liang K, Song J, Hu Q, Yao J, Chen Z, Park P, Hawke DH, Zhou J, Zhou Y, Zhang S, Liang H, Hung MC, Gallick G, Han L, Lin C, and Yang L: A ROR1-HER3-LncRNA signaling axis modulates the Hippo-YAP pathway to regulate bone metastasis. Nature Cell Biology, 2017, 19: 106-119 Song A, Zhang Y, Han L, Yegutkin G, Liu H, Sun K, D'Alessandro A, Li J, KarmoutyQuintana H, Iriyama T, Weng T, Zhao S, Wang W, Wu H, Nemkov T, Subudhi A, Jameson-Van Houten S, Julian CG, Lovering AT, Hansen KC, Zhang H, Bogdanov M, Dowhan W, Jin J, Kellems RE, Eltzschig HK, Blackburn M, Roach RC, Xia Y:Erythrocytes retain hypoxic adenosine response for faster acclimatization upon reascent.Nature Communications, 2017, 8: 14108 Xiao Z, Han L, Lee H, Zhuang L, Zhang Y, Baddour J, Nagrath D, Wood C, Gu J, Wu 10 X, Liang H, Gan B: Energy stress-induced lncRNA FILNC1 represses c-Myc-mediated energy metabolism and inhibits renal tumor development. Nature Communications,In press Feng J*, Xiang Y*, Xia S*, Liu H, Wang J, Ozguc FM, Lei L, Kong R, Diao L, He C, Han L#: CircView: A Visualization and Exploration Tool for Circular RNAs. Briefings in Bioinformatics, 2017, bbx070 Chen C-Y, Zhang Y, Xiang Y, Han L, Shyu A-B: Antagonistic actions of two human Pan3 isoforms on global mRNA turnover. RNA, In press Xiang Y, Ye Y, Lou Y, Yang Y, Cai C, Zhang Z, Mills T, Chen NY, Kim Y, Ozguc FM, Diao L, Karmouty-Quintana H, Xia Y, Blackburn M, Kellems R, Chen Z, Yoo SH, Shyu AB, Mills G, Han L#: Comprehensive characterization of alternative polyadenylation in human cancer. Journal of the National Cancer Institute (Accept). Gong J, Mei S, Liu C, Xiang Y,Ye Y, Zhang Z, Feng J, Liu R, Diao L, Guo AY, Miao X, Han L#: PancanQTL: systematic identification of cis-eQTLs and trans-eQTLs in 33 cancer types. Nucleic Acids Research, 2017, gkx861 Xia S, Feng J, Chen K, Ma Y, Gong J, Cai F, Jin Y, Gao Y, Xia L, Chang H, Wei L, Han L#, He C#: CSCD: A database for cancer-specific circular RNAs. Nucleic Acids Research, 2017, gkx863