The proposed research is to investigate a new epigenetic mechanism for the control of metabolic reprogramming in cancer cells. Pseudouridylation is a common epigenetic modification of human mRNA, which is catalyzed by the family of pseudouridine synthases (PUS) that convert uridine to pseudouridine via base- specific isomerization. The molecular and functional consequences of mRNA pseudouridylation are poorly understood. A major barrier is the lack of suitable and robust experimental models to study the function and regulation of mRNA pseudouridylation. We approached this problem by focusing on the PUS family of enzymes in cancer. Our preliminary studies revealed that the expression of one of the enzymes, PUS7, is transcriptionally upregulated by the oncogenes MYC and MYCN, which are commonly activated in various types of human cancers. Increased expression of PUS7 promotes cancer cell proliferation and tumorigenesis. A key downstream target of PUS7 is ATF4, a master regulator of stress responses and cellular metabolism, which is also targeted by MYCN in reprogramming cellular metabolism to sustain cancer cell proliferation. PUS7 catalyzes pseudouridylation at specific sites in ATF4 mRNA and upregulates ATF4 expression. Based on these findings, we hypothesize that PUS7 is an effector of MYC/MYCN in cancer metabolic reprogramming by boosting ATF4 expression via ATF4 mRNA pseudouridylation. We further hypothesize that this PUS7-ATF4 axis has a key role in the control of stress-induced metabolic reprogramming. The proposed research will test these hypotheses.
Aim 1 studies will be focused on the biological relevance of PUS7-dependent ATF4 mRNA pseudouridylation in the model system of stress responses. We will define the molecular mechanism by which PUS7-dependent ATF4 mRNA pseudouridylation increases ATF4 expression (Aim 1.1). We will determine if PUS7-dependent ATF4 mRNA pseudouridylation is regulated by stress signals and is required for stress induction of ATF4 (Aim 1.2). We will determine if PUS7 controls stress-induced metabolic reprogramming by regulating ATF4 expression (Aim 1.3).
Aim 2 studies will be focused on the cancer relevance of PUS7-dependent ATF4 mRNA pseudouridylation using MYC/MYCN cancer cell lines and tumor models. We will determine if PUS7 is an effector of MYC/MYCN in metabolic reprogramming (Aim 2.1). We will determine whether PUS7-mediated ATF4 mRNA pseudouridylation is regulated by MYC/MYCN and is an epigenetic event during MYC-driven tumor development (Aim 2.2). We will investigate if PUS7 knockdown creates a metabolic vulnerability in MYC/MYCN-driven tumors that could be exploited for cancer therapy (Aim 2.3). Successful completion of this project will shed light on the biological function and cancer relevance of PUS7-dependent mRNA pseudouridylation, which might be exploited for better cancer treatment.
The MYC family of oncogenes drive the development and progression of many human cancers. However, targeting MYC family oncogenes directly for cancer therapy has been unsuccessful so far. In this application, we will investigate a mRNA modification enzyme as a mediator of MYC action in cancer, which could be a druggable target for treatment of MYC-driven cancers.
|Piao, Yongjun; Lee, Seong Keon; Lee, Eun-Joon et al. (2017) CAME: identification of chromatin accessibility from nucleosome occupancy and methylome sequencing. Bioinformatics 33:1139-1146
|Alptekin, Ahmet; Ye, Bingwei; Ding, Han-Fei (2017) Transcriptional Regulation of Stem Cell and Cancer Stem Cell Metabolism. Curr Stem Cell Rep 3:19-27
|Liu, Mengling; Xia, Yingfeng; Ding, Jane et al. (2016) Transcriptional Profiling Reveals a Common Metabolic Program in High-Risk Human Neuroblastoma and Mouse Neuroblastoma Sphere-Forming Cells. Cell Rep 17:609-623
|Zhao, Erhu; Ding, Jane; Xia, Yingfeng et al. (2016) KDM4C and ATF4 Cooperate in Transcriptional Control of Amino Acid Metabolism. Cell Rep 14:506-519
|Kim, Sung-Hwan; Ezenwoye, Onyeka; Cho, Hwan-Gue et al. (2015) iTagPlot: an accurate computation and interactive drawing tool for tag density plot. Bioinformatics 31:2384-7