Biology subjects

Luo, N.

Publications and source records attributed to Luo, N..

3 recordsLinked to original sources

bioRxiv2019

Mutant p53 Drives Clonal Hematopoiesis through Modulating Epigenetic Pathway

Clonal hematopoiesis of indeterminate potential (CHIP) increases with age and is associated with increased risks of hematological malignancies. While TP53 mutations have been identified in CHIP, the molecular mechanisms by which mutant p53 promotes hematopoietic stem and progenitor cell (HSPC) expansion are largely unknown. We discovered that mutant p53 confers a competitive advantage to HSPCs following transplantation and promotes HSPC expansion after radiation-induced stress. Mechanistically, mutant p53 interacts with EZH2 and enhances its association with the chromatin, thereby increasing the levels of H3K27me3 in genes regulating HSPC self-renewal and differentiation. Further, genetic and pharmacological inhibition of EZH2 decrease the repopulating potential of p53 mutant HSPCs. Thus, we have uncovered an epigenetic mechanism by which mutant p53 drives clonal hematopoiesis. Our work will likely establish epigenetic regulator EZH2 as a novel therapeutic target for preventing CHIP progression and treating hematological malignancies with TP53 mutations.

cancer biology↗

bioRxiv2019

Targeted mRNA demethylation using an engineered dCas13b-ALKBH5 fusion protein

Studies on biological functions of N6-methyladenosine (m6A) modification in mRNA have sprung up in recent years. Here we construct and characterize a CRISPR-Cas13b-based tool for the first time that targeted m6A methylation of mRNA by fusing the catalytically dead Type VI-B Cas13 enzyme from Prevotella sp.P5-125 (dPspCas13b) with the m6A demethylase ALKBH5, which is named as dm6ACRISPR. Subsequently, such system is shown to specific demethylase the m6A of target mRNA such as CYB5A to increase its mRNA stability. In addition, the dm6ACRISPR system appeared to afford efficient demethylation of the target genes with tenuous off-target effects. Together, we provide a programmable and in vivo manipulation tool to study mRNA modification and its potential biological functions of specific gene.

cell biology↗

bioRxiv2019

Massive computational acceleration by using neural networks to emulate mechanism-based biological models

Mechanism-based mathematical models are the foundation for diverse applications. It is often critical to explore the massive parametric space for each model. However, for many applications, such as agent-based models, partial differential equations, and stochastic differential equations, this practice can impose a prohibitive computational demand. To overcome this limitation, we present a fundamentally new framework to improve computational efficiency by orders of magnitude. The key concept is to train an artificial neural network using a limited number of simulations generated by a mechanistic model. This number is small enough such that the simulations can be completed in a short time frame but large enough to enable reliable training of the neural network. The trained neural network can then be used to explore the system dynamics of a much larger parametric space. We demonstrate this notion by training neural networks to predict self-organized pattern formation and stochastic gene expression. With this framework, we can predict not only the 1-D distribution in space (for partial differential equation models) and probability density function (for stochastic differential equation models) of variables of interest with high accuracy, but also novel system dynamics not present in the training sets. We further demonstrate that using an ensemble of neural networks enables the self-contained evaluation of the quality of each prediction. Our work can potentially be a platform for faster parametric space screening of biological models with user defined objectives.

systems biology↗