Biology subjects

Chung, J. Y.

Publications and source records attributed to Chung, J. Y..

3 recordsLinked to original sources

bioRxiv2025

Dual RNA Polymerase I Inhibition with CX-5461 and BMH-21 Synergizes in Breast Cancer by Activating p53-Dependent Stress

1Hyperactivated ribosomal RNA (rRNA) transcription by RNA polymerase I (Pol I) is a hallmark of cancer and drives elevated ribosome biogenesis required for rapid tumor growth. Several Pol I inhibitors have been identified that induce potent anti-cancer effects. However, clinical application of the first-in-class Pol I inhibitor, CX-5461, has been limited by patient toxicity, which is comparable to other chemotherapies. Identifying synergistic drug combinations offers a promising strategy to maintain on-target anti-cancer effects while minimizing adverse reactions. Synergistic drug combinations involve drugs that enhance each others effect, enabling dose reduction while preserving efficacy. Synergistic drug combinations of Pol I inhibitors and other anti-cancer agents have been reported; however, it remains unclear whether Pol I inhibitors can synergize with each other. We therefore explored whether two Pol I inhibitors synergize in cancer treatment. We found that CX-5461 and BMH-21 significantly reduced MCF-7 breast cancer cell viability at clinically relevant doses. Combined treatment with these inhibitors led to profound viability defects at sub-micromolar concentrations. Our biochemical analysis showed that CX-5461 and BMH-21 combination therapy enhanced Pol I inhibition and p53 activation compared to monotherapy, promoting growth arrest and apoptosis. Collectively, our findings demonstrate that CX-5461 and BMH-21 are complementary in inhibiting Pol I, activating p53, and suppressing cancer cell growth. Based on these pre-clinical findings, dual Pol I inhibition with CX-5461 and BMH-21 represents a promising therapeutic strategy for treating cancer that is potentially both broadly applicable and tolerable.

cancer biology↗

bioRxiv2025

Multimodal gene and targeted drug therapy for chronic myelogenous leukemia: Computational target analysis and therapeutic validation

Developing an efficient and safe therapy necessitates a mechanistic understanding of the complex underlying pathology and manipulation of the multiple pathways at the molecular and genetic level. Network-based simulation of chronic myeloid leukemia (CML), a relatively well-understood cancer model, revealed the dynamics of simultaneously expressing pro-apoptotic BIM and silencing pro-survival MCL-1 in combination with the BCR-ABL-targeted tyrosine kinase inhibitor dasatinib. Viral/nonviral chimeric nanoparticles (ChNPs) composed of a BIM-expressing adeno-associated virus (AAV) core and a degradable polymeric shell that encapsulates MCL-1 siRNA (BIM/MCL-1 ChNPs) synergistically and selectively killed BCR-ABL+ CML cells in combination with dasatinib. In a mouse CML model, the BIM/MCL-1 ChNPs and dasatinib combination therapy suppressed proliferation of BCR-ABL+ hematopoietic cells and prevented leukemic infiltration of organs. The synergistic anti-leukemic effect was further pronounced in an acute phase model of the disease. This study investigated a strategy of developing a versatile and tunable multimodal therapy assisted by a computational toolset that analyzes the molecular foundation of a disease and predicts therapeutic response. The interdisciplinary approach developed and validated in this study can be used in discovering new therapies for cancer and other diseases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/656072v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@1e3632borg.highwire.dtl.DTLVardef@1839f12org.highwire.dtl.DTLVardef@4060c0org.highwire.dtl.DTLVardef@8ff0de_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO summarizing the study design and approach. A schematic representation of the integrated in silico and in vivo pipeline utilized in the study. The workflow begins with in silico simulations, including Boolean network modeling and protein-protein interaction (PPI) network analyses, leading to target discovery, optimized nanoparticle design, and validation in pathological contexts. This was followed by therapeutic efficacy assessments of BIM/MCL-1 ChNPs and their combination with dasatinib in BCR-ABL+ leukemia models in vitro and in vivo. C_FIG

cancer biology↗

bioRxiv2021

High-throughput analysis of B3GLCT regulation predicts phenotype of Peters' Plus Syndrome in line with the miRNA Proxy Hypothesis

MicroRNAs (miRNAs, miRs) finely tune protein expression and target networks of 100s-1000s of genes that control specific biological processes. They are critical regulators of glycosylation, one of the most diverse and abundant posttranslational modifications. In recent work, miRs have been shown to predict the biological functions of glycosylation enzymes, leading to the "miRNA proxy hypothesis" which states, "if a miR drives a specific biological phenotype..., the targets of that miR will drive the same biological phenotype." Testing of this powerful hypothesis is hampered by our lack of knowledge about miR targets. Target prediction suffers from low accuracy and a high false prediction rate. Herein, we develop a high-throughput experimental platform to analyze miR:target interactions, miRFluR. We utilize this system to analyze the interactions of the entire human miRome with beta-3-glucosyltransferase (B3GLCT), a glycosylation enzyme whose loss underpins the congenital disorder Peters Plus Syndrome. Although this enzyme is predicted by multiple algorithms to be highly targeted by miRs, we identify only 27 miRs that downregulate B3GLCT, a >96% false positive rate for prediction. Functional enrichment analysis of these validated miRs predict phenotypes associated with Peters Plus Syndrome, although B3GLCT is not in their known target network. Thus, biological phenotypes driven by B3GLCT may be driven by the target networks of miRs that regulate this enzyme, providing additional evidence for the miRNA Proxy Hypothesis.

biochemistry↗