Erming Wang
Icahn School of Medicine at Mount Sinai
6 Papers
Erming Wang is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Exome sequencing & Gene knockdown. The author has an hindex of 4, co-authored 6 publications. Previous affiliations of Erming Wang include Mount Sinai Hospital.
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Papers
The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer's disease.
Minghui Wang,Noam D. Beckmann,Panos Roussos,Erming Wang,Xianxiao Zhou,Qian Wang,Chen Ming,Ryan Neff,Weiping Ma,John F. Fullard,Mads E. Hauberg,Jaroslav Bendl,Mette A. Peters,Ben Logsdon,Pei Wang,Milind Mahajan,Lara M. Mangravite,Eric B. Dammer,Duc M. Duong,James J. Lah,Nicholas T. Seyfried,Allan I. Levey,Joseph D. Buxbaum,Michelle E. Ehrlich,Sam Gandy,Sam Gandy,Pavel Katsel,Pavel Katsel,Vahram Haroutunian,Eric E. Schadt,Bin Zhang +30 more
TL;DR: This cohort study generated large-scale matched multi-Omics data in AD and control brains for exploring novel molecular underpinnings of AD through whole genome sequencing, whole exome sequencing, transcriptome sequencing and proteome profiling data.
Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets
Ryan Neff,Minghui Wang,Sezen Vatansever,Lei Guo,Chen Ming,Qian Wang,Erming Wang,Emrin Horgusluoglu-Moloch,Won-Min Song,Aiqun Li,Emilie L. Castranio,Julia Tcw,Lap Ho,Alison Goate,Valentina Fossati,Scott Noggle,Sam Gandy,Michelle E. Ehrlich,Pavel Katsel,Pavel Katsel,Eric E. Schadt,Dongming Cai,Dongming Cai,Kristen J. Brennand,Vahram Haroutunian,Bin Zhang +25 more
TL;DR: In this article, the authors identify three major molecular subtypes of Alzheimer's disease corresponding to different combinations of multiple dysregulated pathways, such as susceptibility to tau-mediated neurodegeneration, amyloid-β neuroinflammation, synaptic signaling, immune activity, mitochondria organization, and myelination.
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Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s Disease
Minghui Wang,Aiqun Li,Michiko Sekiya,Noam D. Beckmann,Xiuming Quan,Nadine Schrode,Michael B. Fernando,Alex J. Yu,Li Zhu,Li Zhu,Jiqing Cao,Jiqing Cao,Liwei Lyu,Emrin Horgusluoglu,Qian Wang,Lei Guo,Yuan-shuo Wang,Ryan Neff,Won-Min Song,Erming Wang,Qi Shen,Xianxiao Zhou,Chen Ming,Seok-Man Ho,Sezen Vatansever,H. Ümit Kaniskan,Jian Jin,Ming-Ming Zhou,Kanae Ando,Lap Ho,Paul A. Slesinger,Zhenyu Yue,Jun Zhu,Pavel Katsel,Pavel Katsel,Sam Gandy,Michelle E. Ehrlich,Valentina Fossati,Scott Noggle,Dongming Cai,Dongming Cai,Vahram Haroutunian,Koichi M. Iijima,Eric E. Schadt,Kristen J. Brennand,Bin Zhang +45 more
TL;DR: This study provides not only a global landscape but also detailed signaling circuits of complex molecular interactions in key brain regions affected by LOAD, and the resulting network models will serve as a blueprint for developing next-generation therapeutic agents against LOAD.
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GJA1 (connexin43) is a key regulator of Alzheimer’s disease pathogenesis
Yuji Kajiwara,Erming Wang,Minghui Wang,Wun Chey Sin,Kristen J. Brennand,Eric E. Schadt,Christian C. Naus,Joseph D. Buxbaum,Bin Zhang +8 more
TL;DR: RNA sequencing analysis of Gja1−/− astrocytes validated that GJA1 regulated the subnetwork identified in AD, and many genes involved in Aβ metabolism, and its potential for further investigation as a promising pharmacological target in AD.
Molecular Networks and Key Regulators of the Dysregulated Neuronal System in Alzheimer’s Disease
Wang M,Li A,Michiko Sekiya,Noam D. Beckmann,Quan X,Nadine Schrode,Michael B. Fernando,Yu A,Lin Zhu,Lin Zhu,Jiqing Cao,Jiqing Cao,Lyu L,Emrin Horgusluoglu,Qing Jun Wang,Lei Guo,Ying-Chih Wang,Ryan Neff,Won-Min Song,Erming Wang,Qi Shen,Xianxiao Zhou,Chen Ming,Seok-Man Ho,Sezen Vatansever,H. U. Kaniskan,Jian Jin,Ming-Ming Zhou,Kanae Ando,Lap Ho,Paul A. Slesinger,Zhenyu Yue,Jun Zhu,Sam Gandy,Michelle E. Ehrlich,Dongming Cai,Dongming Cai,Haroutunian,Koichi M. Iijima,Eric E. Schadt,Kristen J. Brennand,Bin Zhang +41 more
TL;DR: This study advances the understanding of LOAD pathogenesis by providing the global landscape and detailed circuits of complex molecular interactions and regulations in several key brain regions affected by LOAD and the resulting network models provide a blueprint for developing next generation therapeutics against LOAD.