Pluralistic and stochastic gene regulation: examples, models and consistent theory

Neurog3-Independent Methylation Is the Earliest Detectable Mark Distinguishing Pancreatic Progenitor Identity

In the developing pancreas, transient Neurog3-expressing progenitors give rise to four major islet cell types: α, β, δ, and γ; when and how the Neurog3+ cells choose cell fate is unknown. Using single-cell RNA-seq, trajectory analysis, and combinatorial lineage tracing, we showed here that the Neurog3+ cells co-expressing Myt1 (i.e., Myt1+Neurog3+) were biased toward β cell fate, while those not simultaneously expressing Myt1 (Myt1-Neurog3+) favored α fate. Myt1 manipulation only marginally affected α versus β cell specification, suggesting Myt1 as a marker but not determinant for islet-cell-type specification. The Myt1+Neurog3+ cells displayed higher Dnmt1 expression and enhancer methylation at Arx, an α-fate-promoting gene. Inhibiting Dnmts in pancreatic progenitors promoted α cell specification, while Dnmt1 overexpression or Arx enhancer hypermethylation favored β cell production. Moreover, the pancreatic progenitors contained distinct Arx enhancer methylation states without transcriptionally definable sub-populations, a phenotype independent of Neurog3 activity. These data suggest that Neurog3-independent methylation on fate-determining gene enhancers specifies distinct endocrine-cell programs.

Dynamic and Modularized MicroRNA Regulation and Its Implication in Human Cancers

MicroRNA is responsible for the fine-tuning of fundamental cellular activities and human disease development. The altered availability of microRNAs, target mRNAs, and other types of endogenous RNAs competing for microRNA interactions reflects the dynamic and conditional property of microRNA-mediated gene regulation that remains under-investigated. Here we propose a new integrative method to study this dynamic process by considering both competing and cooperative mechanisms and identifying functional modules where different microRNAs co-regulate the same functional process. Specifically, a new pipeline was built based on a meta-Lasso regression model and the proof-of-concept study was performed using a large-scale genomic dataset from ~4,200 patients with 9 cancer types. In the analysis, 10,726 microRNA-mRNA interactions were identified to be associated with a specific stage and/or type of cancer, which demonstrated the dynamic and conditional miRNA regulation during cancer progression. On the other hands, we detected 4,134 regulatory modules that exhibit high fidelity of microRNA function through selective microRNA-mRNA binding and modulation. For example, miR-18a-3p, -320a, -193b-3p, and -92b-3p co-regulate the glycolysis/gluconeogenesis and focal adhesion in cancers of kidney, liver, lung, and uterus. Furthermore, several new insights into dynamic microRNA regulation in cancers have been discovered in this study.