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College of Pharmacy

Faculty and Staff

Michael Shtutman, Ph.D.

Title: Associate Professor
Director, Functional Genomics Core
Department: Drug Discovery & Biomedical Sciences (DDBS)
College of Pharmacy
Email: shtutmanm@cop.sc.edu
Phone: 803-777-8988
Office: College of Pharmacy
715 Sumter Street - CLS 713A
Columbia, SC 29208
Michael Shtutman portrait

Education

Ph.D.  Experimental Oncology, Cancer Research Center, Moscow, Russia, 1996
M.S.  Biotechnology, Moscow Mendeleev Chemical - Technological Institute, 1988 

Postdoctoral Fellowship  Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel, 1997-1999 

Background

Michael Shtutman, received his Ph.D. in Cancer Research Center in Moscow, Russia in 1996 and completed his postdoctoral fellowship at the Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel, in 1999. Since 2011, he has received grant funding from the National Institutes of Health for his research laboratory, as well as in collaboration with other academic institutions.

Dr. Shtutman is director of the NIH-funded Functional Genomics Core, part of the COBRE Center for Targeted Therapeutics, which supports multiple genomics-centered projects across the University of South Carolina and statewide, such as single-cell transcriptomics application and genome polymorphism analysis.

Research Interests

  • Cancer research
  • HIV research
  • Neurodegenerative diseases
  • Drug repurposing
  • Artificial intelligence (AI)
  • Induced pluripotent stem cells
  • Genomics and Transcriptomics

Research Lab

Our laboratory's primary interest is the discovery of druggable target genes and the repurposing of existing drugs for the treatment of cancer and neurodegenerative diseases. With advanced genomics and functional screening approaches, we discovered several candidates: DDX24, IGSF8, and  COPZ2 for anti-cancer drug development.

Currently, our research has centered on the application of Artificial Intelligence (AI) for drug repurposing with a focus on the discovery of the treatment of HIV-associated neurocognitive disorder. In collaboration with the University of Delaware, the laboratory developed MOLIER and AGATHA, AI-based systems for automatic hypothesis generation through the mining of biomedical literature. This system was applied for the discovery of compounds for HIV-associated neurocognitive disorders' (HAND) treatment repurposing, including FDA-approved drugs and small molecules in the late stage of clinical development.

We are using neuronal cultures and induced pluripotent stem cells (iPSCs) as a disease model and applying transcriptomics, including single-cell transcriptomics and proteomics approaches to discover the molecular mechanisms of the drug's activity.


Selected Publications

Marina Aksenova, Justin Sybrandt, Biyun Cui, Vitali Sikirzhytski, Hao Ji, Diana Odhiambo, Mathew Lucius, Jill R. Turner, Eugenia Broude, Edsel Pena, Sofia Lizarraga, Jun Zhu, Ilya Safro, Michael D Wyatt, Michael Shtutman "Inhibition of the DDX3 prevents HIV-1 Tat and cocaine-induced neurotoxicity by targeting microglia activation", Journal of Neuroimmune Pharmacology, 2020 Jun;15(2):209-223

Celia Cui, B., V. Sikirzhytski, M. Aksenova, M.D. Lucius, G.H. Levon, Z.T. Mack, C. Pollack, D. Odhiambo, E. Broude, S.B. Lizarraga, M.D. Wyatt, and M. Shtutman. Pharmacological inhibition of DEAD-Box RNA Helicase 3 attenuates stress granule assembly. Biochem. Pharmacol. 2020, 114280. PMID: 33049245.

Gasparian A, Aksenova M, Oliver D, Levina E, Doran R, Lucius M, Piroli G, Oleinik N, Ogretmen B, Mythreye K, Frizzell N, Broude E, Wyatt MD, Shtutman. Depletion of COPI in cancer cells: the role of reactive oxygen species in the induction of lipid accumulation, noncanonical lipophagy and apoptosis. M. Mol Biol Cell. 2022 Dec 1;33(14):ar135. doi: 10.1091/mbc.E21-08-0420. Epub 2022 Oct 12. PMID: 36222847

J. Sybrandt, M. Shtutman, I. Safro "MOLIERE: Automatic Biomedical Hypothesis Generation System", Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1633-1642, 2017

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