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Phua, R.

Publications and source records attributed to Phua, R..

2 recordsLinked to original sources

Human stem cell models for group 3 medulloblastoma uncover JARID1B as a regulator of the chromatin landscape.

Medulloblastoma (MB) is one of the most prevalent malignant brain tumors in children, with tremendous cognitive and neuroendocrine disability among survivors. Group 3 MB (G3MB) has poor overall survival at <50%, high frequencies of metastases, and no targeted therapies. Amplification of MYC and activation of TGF{beta} signaling occur frequently in G3MB. Many tumors have no reported mutations, suggesting epigenetic drivers. We here describe novel humanized models for G3MB from human induced pluripotent stem cells (hiPSC). By transducing hiPSC-derived neuroepithelial stem cells (NESC), we determined that: 1) both MYC and TGF{beta} effectors drove tumors in vivo; 2) MYC/TGF{beta}R1 in combination led to more aggressive tumors and resistance to clinical inhibitors of TGF{beta}, and 3) NESC-derived tumors clustered with human G3MB. To decipher mechanisms, we integrated RNA-sequencing with CUT&RUN (for MYC genomic localization and post-translational modification of histones). MYC-bound neural developmental genes were repressed in MYC/TGF{beta}R1 co-driven lines. Gene signatures associated with the Polycomb Repressive Complex (PRC) demarcated with H3K27me3; the histone mark directly regulated by PRC. We identified JARID1B, a MYC binding partner and H3K4me3 demethylase, as a regulator of repressed neural genes. Primary G3MB also showed increased levels of H3K27me3 concurrent with higher expression of JARID1B. Knockdown of JARID1B in human G3MB cell lines reduced growth, supporting potential as a therapeutic target. We conclude that a MYC-TGF{beta}-JARID1B axis represses target genes to drive G3MB and present new humanized models for G3MB to understand epigenetic dysregulation in G3MB.

cancer biology↗

NOVA: a novel R-package enabling multi-parameter analysis and visualization of neural activity in MEA recordings

Multielectrode array (MEA) technology enables simultaneous recording of electrical signals from neuronal networks, producing complex datasets. Current analytical approaches typically examine a limited number of metrics such as mean firing rate and synchronicity, leaving much of the data underutilized. To address this gap, we created NOVA (Neural Output Visualization and Analysis), an accessible R-based computational tool for comprehensive MEA data interpretation and visualization. NOVA integrates dimensionality reduction through principal component analysis, hierarchical clustering with heatmap generation, and temporal trajectory mapping of network activity patterns. Our code offers both a userfriendly pipeline requiring minimal coding background as well as customizable advanced plotting modules for experienced users. Validation experiments using primary cortical neurons during development and pharmacological manipulation demonstrated NOVAs capacity to detect subtle activity shifts overlooked by conventional methods. Notably, our unbiased approach identified network burst duration as a stronger contributor to activity variance than commonly reported firing rate metrics, exemplifying NOVAs utility for discovering meaningful patterns and generating data-driven hypotheses.

neuroscience↗