IL-4 orchestrates STAT6-mediated DNA demethylation leading to dendritic cell differentiation

BACKGROUND

The role of cytokines in establishing specific transcriptional programmes in innate immune cells has long been recognized. However, little is known about how these extracellular factors instruct innate immune cell epigenomes to engage specific differentiation states. Human monocytes differentiate under inflammatory conditions into effector cells with non-redundant functions, such as dendritic cells and macrophages. In this context, interleukin 4 (IL-4) and granulocyte macrophage colony-stimulating factor (GM-CSF) drive dendritic cell differentiation, whereas GM-CSF alone leads to macrophage differentiation.

RESULTS

Here, we investigate the role of IL-4 in directing functionally relevant dendritic-cell-specific DNA methylation changes. A comparison of DNA methylome dynamics during differentiation from human monocytes to dendritic cells and macrophages identified gene sets undergoing dendritic-cell-specific or macrophage-specific demethylation. Demethylation is TET2-dependent and is essential for acquiring proper dendritic cell and macrophage identity. Most importantly, activation of the JAK3-STAT6 pathway, downstream of IL-4, is required for the acquisition of the dendritic-cell-specific demethylation and expression signature, following STAT6 binding. A constitutively activated form of STAT6 is able to bypass IL-4 upstream signalling and instruct dendritic-cell-specific functional DNA methylation changes.

CONCLUSIONS

Our study is the first description of a cytokine-mediated sequence of events leading to direct gene-specific demethylation in innate immune cell differentiation.

Identification of novel markers in rheumatoid arthritis through integrated analysis of DNA methylation and microRNA expression

Autoimmune rheumatic diseases are complex disorders, whose etiopathology is attributed to a crosstalk between genetic predisposition and environmental factors. Both variants of autoimmune susceptibility genes and environment are involved in the generation of aberrant epigenetic profiles in a cell-specific manner, which ultimately result in dysregulation of expression. Furthermore, changes in miRNA expression profiles also cause gene dysregulation associated with aberrant phenotypes. In rheumatoid arthritis, several cell types are involved in the destruction of the joints, synovial fibroblasts being among the most important. In this study we performed DNA methylation and miRNA expression screening of a set of rheumatoid arthritis synovial fibroblasts and compared the results with those obtained from osteoarthritis patients with a normal phenotype. DNA methylation screening allowed us to identify changes in novel key target genes like IL6R, CAPN8 and DPP4, as well as several HOX genes. A significant proportion of genes undergoing DNA methylation changes were inversely correlated with expression. miRNA screening revealed the existence of subsets of miRNAs that underwent changes in expression. Integrated analysis highlighted sets of miRNAs that are controlled by DNA methylation, and genes that are regulated by DNA methylation and are targeted by miRNAs with a potential use as clinical markers. Our study enabled the identification of novel dysregulated targets in rheumatoid arthritis synovial fibroblasts and generated a new workflow for the integrated analysis of miRNA and epigenetic control.

PU.1 target genes undergo Tet2-coupled demethylation and DNMT3b-mediated methylation in monocyte-to-osteoclast differentiation

BACKGROUND

DNA methylation is a key epigenetic mechanism for driving and stabilizing cell-fate decisions. Local deposition and removal of DNA methylation are tightly coupled with transcription factor binding, although the relationship varies with the specific differentiation process. Conversion of monocytes to osteoclasts is a unique terminal differentiation process within the hematopoietic system. This differentiation model is relevant to autoimmune disease and cancer, and there is abundant knowledge on the sets of transcription factors involved.

RESULTS

Here we focused on DNA methylation changes during osteoclastogenesis. Hypermethylation and hypomethylation changes took place in several thousand genes, including all relevant osteoclast differentiation and function categories. Hypomethylation occurred in association with changes in 5-hydroxymethylcytosine, a proposed intermediate toward demethylation. Transcription factor binding motif analysis revealed an over-representation of PU.1, NF-κB, and AP-1 (Jun/Fos) binding motifs in genes undergoing DNA methylation changes. Among these, only PU.1 motifs were significantly enriched in both hypermethylated and hypomethylated genes; ChIP-seq data analysis confirmed its association to both gene sets. Moreover, PU.1 interacts with both DNMT3b and TET2, suggesting its participation in driving hypermethylation and hydroxymethylation-mediated hypomethylation. Consistent with this, siRNA-mediated PU.1 knockdown in primary monocytes impaired the acquisition of DNA methylation and expression changes, and reduced the association of TET2 and DNMT3b at PU.1 targets during osteoclast differentiation.

CONCLUSIONS

The work described here identifies key changes in DNA methylation during monocyte-to-osteoclast differentiation and reveals novel roles for PU.1 in this process.

Using machine learning techniques and genomic/proteomic information from known databases for defining relevant features for PPI classification

In modern proteomics, prediction of protein-protein interactions (PPIs) is a key research line, as these interactions take part in most essential biological processes. In this paper, a new approach is proposed to PPI data classification based on the extraction of genomic and proteomic information from well-known databases and the incorporation of semantic measures. This approach is carried out through the application of data mining techniques and provides very accurate models with high levels of sensitivity and specificity in the classification of PPIs. The well-known support vector machine paradigm is used to learn the models, which will also return a new confidence score which may help expert researchers to filter out and validate new external PPIs. One of the most-widely analyzed organisms, yeast, will be studied. We processed a very high-confidence dataset by extracting up to 26 specific features obtained from the chosen databases, half of them calculated using two new similarity measures proposed in this paper. Then, by applying a filter-wrapper algorithm for feature selection, we obtained a final set composed of the eight most relevant features for predicting PPIs, which was validated by a ROC analysis. The prediction capability of the support vector machine model using these eight features was tested through the evaluation of the predictions obtained in a set of external experimental, computational, and literature-collected datasets.