Metabolism and Histone Deacetylases: A Systems Biology Perspective – UROP Spring Symposium 2021

Metabolism and Histone Deacetylases: A Systems Biology Perspective

Namit Padgaonkar

Namit Padgaonkar

Pronouns: he/him/his

Research Mentor(s): Sriram Chandrasekaran, Assistant Professor
Research Mentor School/College/Department: Biomedical Engineering, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 2 (11am – 11:50am)
Breakout Room: Room 14
Presenter: 6

Event Link


Histones are proteins that provide structural support to chromosomes and help condense nuclear DNA into chromatin. Histone acetylation is a process that diminishes the affinity between histones and DNA so that gene transcription is more permissive. This process is regulated by the opposing actions of histone acetyltransferases (HATs) and histone deacetylases (HDACs), both of which are critical to many cellular processes such as DNA damage repair and proper transcription. HDACs are sensitive to the metabolic state of the cell, and the dynamic between metabolism and histone acetylation impacts several biological processes, including development and immune function. HDAC inhibitor drugs are currently being explored for treating various conditions, including cancers, viral infections, inflammation, neurodegenerative diseases, and metabolic disorders. However, it is currently a significant clinical challenge to identify subsets of patients sensitive to HDAC inhibitors. Thus, this review compiles recent applications of systems biology methods such as high throughput drug screens, cancer cell-line profiling, single cell sequencing, proteomics, and metabolomics that can help to determine the interplay between metabolism, HDACs, and HDAC inhibitors. Compiling these systems approaches can ultimately help identify epigenomic and metabolic biomarkers for patient stratification, enable the design of synergistic combination therapies, and illuminate the gaps in our knowledge regarding this interplay. For future studies, metabolic modeling and machine learning methods can be used to analyze gaps in our current understanding and identify additional potential biomarkers for patients sensitive to HDAC inhibitors.

Authors: Namit Padgaonkar, Maya Patel, Sriram Chandrasekaran
Research Method: Computer Programming

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