Biology subjects

Mu, X. J.

Publications and source records attributed to Mu, X. J..

3 recordsLinked to original sources

bioRxiv2025

Photoproximity labeling of c-Myc reveals SLK as a cancer specific co-regulator

Transcription factors (TFs) have long been aspirational therapeutic targets for the treatment of diseases, as their dysregulation is a common mechanism for altered cell states. Despite this, many TFs implicated in disease have disordered structures and lack canonical binding pockets, rendering them non-trivial targets for small molecule-based therapies. Directly inhibiting TF function has proven difficult, but indirect inhibition by targeting the effector molecules that modulate TF function is a promising, yet underexplored, alternative approach. Here we report a strategy for capturing cancer-specific protein-protein interactions using context-dependent {micro}Map photoproximity labeling. Using an intein-based method for catalyst conjugation in biochemically intact nuclei, we demonstrate that we can capture unique protein interactomes of c-Myc in healthy and cancerous prostate cell lines, and that these unique interactors can be mined to identify druggable vulnerabilities. We find that a cancer specific Myc interactor, SLK, selectively promotes c-Myc stabilization at the protein level, drives epithelial morphology, and is essential for tumorigenesis, validating it as a viable therapeutic target. Importantly, this stabilization is driven by a change in SLK splicing rather than expression at the protein or RNA levels. Furthermore, analysis of cancer patient data shows a strong correlation between this splice isoform and expression of c-Myc targets, suggesting this novel regulatory axis is operative across human cancer.

cancer biology↗

bioRxiv2024

Catalytic inhibition of KAT6/KAT7 enhances the efficacy and overcomes primary and acquired resistance to Menin inhibitors in MLL leukaemia

Understanding the molecular pathogenesis of MLL fusion oncoprotein (MLL-FP) leukaemia has spawned epigenetic therapies that have improved clinical outcomes in this often-incurable disease. Using genetic and pharmacological approaches, we define the individual and combined contribution of KAT6A, KAT6B and KAT7, in MLL-FP leukaemia. Whilst inhibition of KAT6A/B is efficacious in some pre-clinical models, simultaneous targeting of KAT7, with the novel inhibitor PF-9363, increases the therapeutic efficacy. KAT7 interacts with Menin and the MLL complex and is co-localised at chromatin to co-regulate the MLL-FP transcriptional program. Inhibition of KAT6/KAT7 provides an orthogonal route to targeting Menin to disable the transcriptional activity of MLL-FP. Consequently, combined inhibition rapidly evicts the MLL-FP from chromatin, potently represses oncogenic transcription and overcomes primary resistance to Menin inhibitors. Moreover, PF-9363 or genetic depletion of KAT7 can also overcome acquired genetic/non-genetic resistance to Menin inhibition. These data provide the molecular rationale for rapid clinical translation of combination therapy in MLL-FP leukaemia.

cancer biology↗

bioRxiv2019

Deep Learning of Representations for Transcriptomics-based Phenotype Prediction

The ability to predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. This task is complicated because expression data are high dimensional whereas each experiment is usually small (e.g.,[~] 20,000 genes may be measured for[~] 100 subjects). However, thousands of transcriptomics experiments with hundreds of thousands of samples are available in public repositories. Can representation learning techniques leverage these public data to improve predictive performance on other tasks? Here, we report a comprehensive analysis using different gene sets, normalization schemes, and machine learning methods on a set of 24 binary and multiclass prediction problems and 26 survival analysis tasks. Methods that combine large numbers of genes outperformed single gene methods, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses.

bioinformatics↗