Activation-induced cytidine deaminase targets SUV4-20-mediated histone H4K20 trimethylation to class-switch recombination sites.

Activation-induced cytidine deaminase (AID) triggers antibody diversification in B cells by catalysing deamination and subsequently mutating immunoglobulin (Ig) genes. Association of AID with RNA Pol II and occurrence of epigenetic changes during Ig gene diversification suggest participation of AID in epigenetic regulation. AID is mutated in hyper-IgM type 2 (HIGM2) syndrome. Here, we investigated the potential role of AID in the acquisition of epigenetic changes. We discovered that AID binding to the IgH locus promotes an increase in H4K20me3. In 293F cells, we demonstrate interaction between co-transfected AID and the three SUV4-20 histone H4K20 methyltransferases, and that SUV4-20H1.2, bound to the IgH switch (S) mu site, is replaced by SUV4-20H2 upon AID binding. Analysis of HIGM2 mutants shows that the AID truncated form W68X is impaired to interact with SUV4-20H1.2 and SUV4-20H2 and is unable to bind and target H4K20me3 to the Smu site. We finally show in mouse primary B cells undergoing class-switch recombination (CSR) that AID deficiency associates with decreased H4K20me3 levels at the Smu site. Our results provide a novel link between SUV4-20 enzymes and CSR and offer a new aspect of the interplay between AID and histone modifications in setting the epigenetic status of CSR sites.

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.

Monozygotic twins discordant for common variable immunodeficiency reveal impaired DNA demethylation during naïve-to-memory B-cell transition.

Common variable immunodeficiency (CVID), the most frequent primary immunodeficiency characterized by loss of B-cell function, depends partly on genetic defects, and epigenetic changes are thought to contribute to its aetiology. Here we perform a high-throughput DNA methylation analysis of this disorder using a pair of CVID-discordant MZ twins and show predominant gain of DNA methylation in CVID B cells with respect to those from the healthy sibling in critical B lymphocyte genes, such as PIK3CD, BCL2L1, RPS6KB2, TCF3 and KCNN4. Individual analysis confirms hypermethylation of these genes. Analysis in naive, unswitched and switched memory B cells in a CVID patient cohort shows impaired ability to demethylate and upregulate these genes in transitioning from naive to memory cells in CVID. Our results not only indicate a role for epigenetic alterations in CVID but also identify relevant DNA methylation changes in B cells that could explain the clinical manifestations of CVID individuals.

NF-κB directly mediates epigenetic deregulation of common microRNAs in Epstein-Barr virus-mediated transformation of B-cells and in lymphomas.

MicroRNAs (miRNAs) have negative effects on gene expression and are major players in cell function in normal and pathological conditions. Epstein-Barr virus (EBV) infection of resting B lymphocytes results in their growth transformation and associates with different B cell lymphomas. EBV-mediated B cell transformation involves large changes in gene expression, including cellular miRNAs. We performed miRNA expression analysis in growth transformation of EBV-infected B cells. We observed predominant downregulation of miRNAs and upregulation of a few miRNAs. We observed similar profiles of miRNA expression in B cells stimulated with CD40L/IL-4, and those infected with EBNA-2- and LMP-1-deficient EBV particles, suggesting the implication of the NF-kB pathway, common to all four situations. In fact, the NF-kB subunit p65 associates with the transcription start site (TSS) of both upregulated and downregulated miRNAs following EBV infection This occurs together with changes at histone H3K27me3 and histone H3K4me3. Inhibition of the NF-kB pathway impairs changes in miRNA expression, NF-kB binding and changes at the above histone modifications near the TSS of these miRNA genes. Changes in expression of these miRNAs also occurred in diffuse large B cell lymphomas (DLBCL), which are strongly NF-kB dependent. Our results highlight the relevance of the NF-kB pathway in epigenetically mediated miRNA control in B cell transformation and DLBCL.

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.

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns.

MOTIVATION: Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. RESULTS: The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. AVAILABILITY: The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

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.

Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques.

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased.