ADMIRE: analysis and visualization of differential methylation in genomic regions using the Infinium HumanMethylation450 Assay

A novel approach toward optimal workflow selection for DNA methylation biomarker discovery

DNA methylation is a major epigenetic modification involved in many physiological processes. Normal methylation patterns are disrupted in many diseases and methylation-based biomarkers have shown promise in several contexts. Marker discovery typically involves the analysis of publicly available DNA methylation data from high-throughput assays. Numerous methods for identification of differentially methylated biomarkers have been developed, making the need for best practices guidelines and context-specific analyses workflows exceedingly high. To this end, here we propose TASA, a novel method for simulating methylation array data in various scenarios. We then comprehensively assess different data analysis workflows using real and simulated data and suggest optimal start-to-finish analysis workflows. Our study demonstrates that the choice of analysis pipeline for DNA methylation-based marker discovery is crucial and different across different contexts.

Association of Clonal Hematopoiesis of Indeterminate Potential with Inflammatory Gene Expression in Patients with COPD

Chronic obstructive pulmonary disease (COPD) is a disease with an inflammatory phenotype with increasing prevalence in the elderly. Expanded population of mutant blood cells carrying somatic mutations is termed clonal hematopoiesis of indeterminate potential (CHIP). The association between CHIP and COPD and its relevant effects on DNA methylation in aging are mainly unknown. Analyzing the deep-targeted amplicon sequencing from 125 COPD patients, we found enhanced incidence of CHIP mutations (~20%) with a predominance of DNMT3A CHIP-mediated hypomethylation of Phospholipase D Family Member 5 (PLD5), which in turn is positively correlated with increased levels of glycerol phosphocholine, pro-inflammatory cytokines, and deteriorating lung function.

Single cell sequencing reveals endothelial plasticity with transient mesenchymal activation after myocardial infarction

Endothelial cells play a critical role in the adaptation of tissues to injury. Tissue ischemia induced by infarction leads to profound changes in endothelial cell functions and can induce transition to a mesenchymal state. Here we explore the kinetics and individual cellular responses of endothelial cells after myocardial infarction by using single cell RNA sequencing. This study demonstrates a time dependent switch in endothelial cell proliferation and inflammation associated with transient changes in metabolic gene signatures. Trajectory analysis reveals that the majority of endothelial cells 3 to 7 days after myocardial infarction acquire a transient state, characterized by mesenchymal gene expression, which returns to baseline 14 days after injury. Lineage tracing, using the Cdh5-CreERT2;mT/mG mice followed by single cell RNA sequencing, confirms the transient mesenchymal transition and reveals additional hypoxic and inflammatory signatures of endothelial cells during early and late states after injury. These data suggest that endothelial cells undergo a transient mes-enchymal activation concomitant with a metabolic adaptation within the first days after myocardial infarction but do not acquire a long-term mesenchymal fate. This mesenchymal activation may facilitate endothelial cell migration and clonal expansion to regenerate the vascular network.

Endothelial cells play a critical role in the adaptation of tissues to injury and show a remarkable plasticity. Here the authors show, using single cell sequencing, that endothelial cells acquire a transient mesenchymal state associated with metabolic adaptation after myocardial infarction.

MADA: a web service for analysing DNA methylation array data

Background

DNA methylation in the human genome is acknowledged to be widely associated with biological processes and complex diseases. The Illumina Infinium methylation arrays have been approved as one of the most efficient and universal technologies to investigate the whole genome changes of methylation patterns. As methylation arrays may still be the dominant method for detecting methylation in the anticipated future, it is crucial to develop a reliable workflow to analysis methylation array data.

Results

In this study, we develop a web service MADA for the whole process of methylation arrays data analysis, which includes the steps of a comprehensive differential methylation analysis pipeline: pre-processing (data loading, quality control, data filtering, and normalization), batch effect correction, differential methylation analysis, and downstream analysis. In addition, we provide the visualization of pre-processing, differentially methylated probes or regions, gene ontology, pathway and cluster analysis results. Moreover, a customization function for users to define their own workflow is also provided in MADA.

Conclusions

With the analysis of two case studies, we have shown that MADA can complete the whole procedure of methylation array data analysis. MADA provides a graphical user interface and enables users with no computational skills and limited bioinformatics background to carry on complicated methylation array data analysis. The web server is available at: http://120.24.94.89:8080/MADA

Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles

In silico models to predict which tumors will respond to a given drug are necessary for Precision Oncology. However, predictive models are only available for a handful of cases (each case being a given drug acting on tumors of a specific cancer type). A way to generate predictive models for the remaining cases is with suitable machine learning algorithms that are yet to be applied to existing in vitro pharmacogenomics datasets. Here, we apply XGBoost integrated with a stringent feature selection approach, which is an algorithm that is advantageous for these high-dimensional problems. Thus, we identified and validated 118 predictive models for 62 drugs across five cancer types by exploiting four molecular profiles (sequence mutations, copy-number alterations, gene expression, and DNA methylation). Predictive models were found in each cancer type and with every molecular profile. On average, no omics profile or cancer type obtained models with higher predictive accuracy than the rest. However, within a given cancer type, some molecular profiles were overrepresented among predictive models. For instance, CNA profiles were predictive in breast invasive carcinoma (BRCA) cell lines, but not in small cell lung cancer (SCLC) cell lines where gene expression (GEX) and DNA methylation profiles were the most predictive. Lastly, we identified the best XGBoost model per cancer type and analyzed their selected features. For each model, some of the genes in the selected list had already been found to be individually linked to the response to that drug, providing additional evidence of the usefulness of these models and the merits of the feature selection scheme.

WIlsON: Web-based Interactive Omics VisualizatioN

Motivation

High throughput (HT) screens in the omics field are typically analyzed by automated pipelines that generate static visualizations and comprehensive spreadsheet data for scientists. However, exploratory and hypothesis driven data analysis are key aspects of the understanding of biological systems, both generating extensive need for customized and dynamic visualization.

Results

Here we describe WIlsON, an interactive workbench for analysis and visualization of multi-omics data. It is primarily intended to empower screening platforms to offer access to pre-calculated HT screen results to the non-computational scientist. Facilitated by an open file format, WIlsON supports all types of omics screens, serves results via a web-based dashboard, and enables end users to perform analyses and generate publication-ready plots.

Availability and implementation

We implemented WIlsON in R with a focus on extensibility using the modular Shiny and Plotly frameworks. A demo of the interactive workbench without limitations may be accessed at http://loosolab.mpi-bn.mpg.de. A standalone Docker container as well as the source code of WIlsON are freely available from our Docker hub https://hub.docker. com/r/loosolab/wilson, CRAN https://cran.r-project.org/web/packages/wilson/, and GitHub repository https://github.molgen.mpg.de/loosolab/wilson-apps, respectively.

MeinteR: A framework to prioritize DNA methylation aberrations based on conformational and cis-regulatory element enrichment

DNA methylation studies have been reformed with the advent of single-base resolution arrays and bisulfite sequencing methods, enabling deeper investigation of methylation-mediated mechanisms. In addition to these advancements, numerous bioinformatics tools address important computational challenges, covering DNA methylation calling up to multi-modal interpretative analyses. However, contrary to the analytical frameworks that detect driver mutational signatures, the identification of putatively actionable epigenetic events remains an unmet need. The present work describes a novel computational framework, called MeinteR, that prioritizes critical DNA methylation events based on the following hypothesis: critical aberrations of DNA methylation more likely occur on a genomic substrate that is enriched in cis-acting regulatory elements with distinct structural characteristics, rather than in genomic “deserts”. In this context, the framework incorporates functional cis-elements, e.g. transcription factor binding sites, tentative splice sites, as well as conformational features, such as G-quadruplexes and palindromes, to identify critical epigenetic aberrations with potential implications on transcriptional regulation. The evaluation on multiple, public cancer datasets revealed significant associations between the highest-ranking loci with gene expression and known driver genes, enabling for the first time the computational identification of high impact epigenetic changes based on high-throughput DNA methylation data.

Pulmonary endothelial cell DNA methylation signature in pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a severe and incurable pulmonary vascular disease. One of the primary origins of PAH is pulmonary endothelial dysfunction leading to vasoconstriction, aberrant angiogenesis and smooth muscle cell proliferation, endothelial-to-mesenchymal transition, thrombosis and inflammation. Our objective was to study the epigenetic variations in pulmonary endothelial cells (PEC) through a specific pattern of DNA methylation. DNA was extracted from cultured PEC from idiopathic PAH (n = 11), heritable PAH (n = 10) and controls (n = 18). DNA methylation was assessed using the Illumina HumanMethylation450 Assay. After normalization, samples and probes were clustered according to their methylation profile. Differential clusters were functionally analyzed using bioinformatics tools. Unsupervised hierarchical clustering allowed the identification of two clusters of probes that discriminates controls and PAH patients. Among 147 differential methylated promoters, 46 promoters coding for proteins or miRNAs were related to lipid metabolism. Top 10 up and down-regulated genes were involved in lipid transport including ABCA1, ABCB4, ADIPOQ, miR-26A, BCL2L11. NextBio meta-analysis suggested a contribution of ABCA1 in PAH. We confirmed ABCA1 mRNA and protein downregulation specifically in PAH PEC by qPCR and immunohistochemistry and made the proof-of-concept in an experimental model of the disease that its targeting may offer novel therapeutic options. In conclusion, DNA methylation analysis identifies a set of genes mainly involved in lipid transport pathway which could be relevant to PAH pathophysiology.

Promoter hypermethylation as a mechanism for Lamin A/C silencing in a subset of neuroblastoma cells

Nuclear lamins support the nuclear envelope and provide anchorage sites for chromatin. They are involved in DNA synthesis, transcription, and replication. It has previously been reported that the lack of Lamin A/C expression in lymphoma and leukaemia is due to CpG island promoter hypermethylation. Here, we provide evidence that Lamin A/C is silenced via this mechanism in a subset of neuroblastoma cells. Moreover, Lamin A/C expression can be restored with a demethylating agent. Importantly, Lamin A/C reintroduction reduced cell growth kinetics and impaired migration, invasion, and anchorage-independent cell growth. Cytoskeletal restructuring was also induced. In addition, the introduction of lamin Δ50, known as Progerin, caused senescence in these neuroblastoma cells. These cells were stiffer and developed a cytoskeletal structure that differed from that observed upon Lamin A/C introduction. Of relevance, short hairpin RNA Lamin A/C depletion in unmethylated neuroblastoma cells enhanced the aforementioned tumour properties. A cytoskeletal structure similar to that observed in methylated cells was induced. Furthermore, atomic force microscopy revealed that Lamin A/C knockdown decreased cellular stiffness in the lamellar region. Finally, the bioinformatic analysis of a set of methylation arrays of neuroblastoma primary tumours showed that a group of patients (around 3%) gives a methylation signal in some of the CpG sites located within the Lamin A/C promoter region analysed by bisulphite sequencing PCR. These findings highlight the importance of Lamin A/C epigenetic inactivation for a subset of neuroblastomas, leading to enhanced tumour properties and cytoskeletal changes. Additionally, these findings may have treatment implications because tumour cells lacking Lamin A/C exhibit more aggressive behaviour.

Epigenetics and nutrition-related epidemics of metabolic diseases: Current perspectives and challenges

We live in a world fascinated by the relationship between disease and nutritional disequilibrium. The subtle and slow effects of chronic nutrient toxicity are a major public health concern. Since food is potentially important for the development of "metabolic memory", there is a need for more information on the type of nutrients causing adverse or toxic effects. We now know that metabolic alterations produced by excessive intake of some nutrients, drugs and chemicals directly impact epigenetic regulation. We envision that understanding how metabolic pathways are coordinated by environmental and genetic factors will provide novel insights for the treatment of metabolic diseases. New methods will enable the assembly and analysis of large sets of complex molecular and clinical data for understanding how inflammation and mitochondria affect bioenergetics, epigenetics and health. Collectively, the observations we highlight indicate that energy utilization and disease are intimately connected by epigenetics. The challenge is to incorporate metabolo-epigenetic data in better interpretations of disease, to expedite therapeutic targeting of key pathways linking nutritional toxicity and metabolism. An additional concern is that changes in the parental phenotype are detectable in the methylome of subsequent offspring. The effect might create a menace to future generations and preconceptional considerations.

Establishing an analytic pipeline for genome-wide DNA methylation

The need for research investigating DNA methylation (DNAm) in clinical studies has increased, leading to the evolution of new analytic methods to improve accuracy and reproducibility of the interpretation of results from these studies. The purpose of this article is to provide clinical researchers with a summary of the major data processing steps routinely applied in clinical studies investigating genome-wide DNAm using the Illumina HumanMethylation 450K BeadChip. In most studies, the primary goal of employing DNAm analysis is to identify differential methylation at CpG sites among phenotypic groups. Experimental design considerations are crucial at the onset to minimize bias from factors related to sample processing and avoid confounding experimental variables with non-biological batch effects. Although there are currently no de facto standard methods for analyzing these data, we review the major steps in processing DNAm data recommended by several research studies. We describe several variations available for clinical researchers to process, analyze, and interpret DNAm data. These insights are applicable to most types of genome-wide DNAm array platforms and will be applicable for the next generation of DNAm array technologies (e.g., the 850K array). Selection of the DNAm analytic pipeline followed by investigators should be guided by the research question and supported by recently published methods.