Local chromatin dynamics of transcription factors imply cell-lineage specific functions during cellular differentiation

TNF-α-mediated microRNA-136 induces differentiation of myeloid cells by targeting NFIA

Immune cell-lineage specification and function are influenced by progenitor origin and environmental factors. The mechanism of differentiation of immune cells, such as neutrophils, monocytes, and myeloid-derived suppressor cells, in inflammatory environments has not been elucidated completely. In this study, we have identified human microRNA-136 as a positive regulator of the differentiation of granulocytes and monocytes. Ectopic microRNA-136 induced cells to express higher levels of CD11b, CD14, and C/EBPε, secrete more cytokines, and synthesize higher levels of reactive oxygen species and H(2)O(2). microRNA-136 was shown to target and degrade multiple differentiation-associated molecules, such as the transcription factor NFIA, which induced the release of another microRNA, microRNA-223, with the ability to promote CD11b expression. Furthermore, microRNA-136 expression was remarkably increased by TNF-α, which activated NF-κB to bind to the DNA-promoter region controlling microRNA-136 expression. Additionally, TNF-α may alter NFIA expression through its modulation of microRNA-136 expression. Thus, TNF-α-mediated microRNA-136 may play a critical role in the generation and differentiation of inflammatory immune cells.

Quantitative epigenetic co-variation in CpG islands and co-regulation of developmental genes

The genome-wide variation of multiple epigenetic modifications in CpG islands (CGIs) and the interactions between them are of great interest. Here, we optimized an entropy-based strategy to quantify variation of epigenetic modifications and explored their interaction across mouse embryonic stem cells, neural precursor cells and brain. Our results showed that four epigenetic modifications (DNA methylation, H3K4me2, H3K4me3 and H3K27me3) of CGIs in the mouse genome undergo combinatorial variation during neuron differentiation. DNA methylation variation was positively correlated with H3K27me3 variation, and negatively correlated with H3K4me2/3 variation. We identified 5,194 CGIs differentially modified by epigenetic modifications (DEM-CGIs). Among them, the differentially DNA methylated CGIs overlapped significantly with the CGIs differentially modified by H3K27me3. Moreover, DEM-CGIs may contribute to co-regulation of related developmental genes including core transcription factors. Our entropy-based strategy provides an effective way of investigating dynamic cross-talk among epigenetic modifications in various biological processes at the macro scale.

Computational inference of mRNA stability from histone modification and transcriptome profiles

Histone modifications play important roles in regulating eukaryotic gene expression and have been used to model expression levels. Here, we present a regression model to systematically infer mRNA stability by comparing transcriptome profiles with ChIP-seq of H3K4me3, H3K27me3 and H3K36me3. The results from multiple human and mouse cell lines show that the inferred unstable mRNAs have significantly longer 3′Untranslated Regions (UTRs) and more microRNA binding sites within 3′UTR than the inferred stable mRNAs. Regression residuals derived from RNA-seq, but not from GRO-seq, are highly correlated with the half-lives measured by pulse-labeling experiments, supporting the rationale of our inference. Whereas, the functions enriched in the inferred stable and unstable mRNAs are consistent with those from pulse-labeling experiments, we found the unstable mRNAs have higher cell-type specificity under functional constraint. We conclude that the systematical use of histone modifications can differentiate non-expressed mRNAs from unstable mRNAs, and distinguish stable mRNAs from highly expressed ones. In summary, we represent the first computational model of mRNA stability inference that compares transcriptome and epigenome profiles, and provides an alternative strategy for directing experimental measurements.