Epigenomic Profiling of Human CD4+ T Cells Supports a Linear Differentiation Model and Highlights Molecular Regulators of Memory Development.

The impact of epigenetics on the differentiation of memory T (Tmem) cells is poorly defined. We generated deep epigenomes comprising genome-wide profiles of DNA methylation, histone modifications, DNA accessibility, and coding and non-coding RNA expression in naive, central-, effector-, and terminally differentiated CD45RA+ CD4+ Tmem cells from blood and CD69+ Tmem cells from bone marrow (BM-Tmem). We observed a progressive and proliferation-associated global loss of DNA methylation in heterochromatic parts of the genome during Tmem cell differentiation. Furthermore, distinct gradually changing signatures in the epigenome and the transcriptome supported a linear model of memory development in circulating T cells, while tissue-resident BM-Tmem branched off with a unique epigenetic profile. Integrative analyses identified candidate master regulators of Tmem cell differentiation, including the transcription factor FOXP1. This study highlights the importance of epigenomic changes for Tmem cell biology and demonstrates the value of epigenetic data for the identification of lineage regulators.

The landscape of genomic alterations across childhood cancers.

Pan-cancer analyses that examine commonalities and differences among various cancer types have emerged as a powerful way to obtain novel insights into cancer biology. Here we present a comprehensive analysis of genetic alterations in a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer. Using a standardized workflow, we identified marked differences in terms of mutation frequency and significantly mutated genes in comparison to previously analysed adult cancers. Genetic alterations in 149 putative cancer driver genes separate the tumours into two classes: small mutation and structural/copy-number variant (correlating with germline variants). Structural variants, hyperdiploidy, and chromothripsis are linked to TP53 mutation status and mutational signatures. Our data suggest that 7-8% of the children in this cohort carry an unambiguous predisposing germline variant and that nearly 50% of paediatric neoplasms harbour a potentially druggable event, which is highly relevant for the design of future clinical trials.

Author Correction: The landscape of genomic alterations across childhood cancers.

In this Article, author Benedikt Brors was erroneously associated with affiliation number '8' (Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA); the author's two other affiliations (affiliations '3' and '7', both at the German Cancer Research Center (DKFZ)) were correct. This has been corrected online.

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases.

This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.

Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma.

Medulloblastoma is a highly malignant paediatric brain tumour currently treated with a combination of surgery, radiation and chemotherapy, posing a considerable burden of toxicity to the developing child. Genomics has illuminated the extensive intertumoral heterogeneity of medulloblastoma, identifying four distinct molecular subgroups. Group 3 and group 4 subgroup medulloblastomas account for most paediatric cases; yet, oncogenic drivers for these subtypes remain largely unidentified. Here we describe a series of prevalent, highly disparate genomic structural variants, restricted to groups 3 and 4, resulting in specific and mutually exclusive activation of the growth factor independent 1 family proto-oncogenes, GFI1 and GFI1B. Somatic structural variants juxtapose GFI1 or GFI1B coding sequences proximal to active enhancer elements, including super-enhancers, instigating oncogenic activity. Our results, supported by evidence from mouse models, identify GFI1 and GFI1B as prominent medulloblastoma oncogenes and implicate 'enhancer hijacking' as an efficient mechanism driving oncogene activation in a childhood cancer.

Decoding the regulatory landscape of medulloblastoma using DNA methylation sequencing.

Epigenetic alterations, that is, disruption of DNA methylation and chromatin architecture, are now acknowledged as a universal feature of tumorigenesis. Medulloblastoma, a clinically challenging, malignant childhood brain tumour, is no exception. Despite much progress from recent genomics studies, with recurrent changes identified in each of the four distinct tumour subgroups (WNT-pathway-activated, SHH-pathway-activated, and the less-well-characterized Group 3 and Group 4), many cases still lack an obvious genetic driver. Here we present whole-genome bisulphite-sequencing data from thirty-four human and five murine tumours plus eight human and three murine normal controls, augmented with matched whole-genome, RNA and chromatin immunoprecipitation sequencing data. This comprehensive data set allowed us to decipher several features underlying the interplay between the genome, epigenome and transcriptome, and its effects on medulloblastoma pathophysiology. Most notable were highly prevalent regions of hypomethylation correlating with increased gene expression, extending tens of kilobases downstream of transcription start sites. Focal regions of low methylation linked to transcription-factor-binding sites shed light on differential transcriptional networks between subgroups, whereas increased methylation due to re-normalization of repressed chromatin in DNA methylation valleys was positively correlated with gene expression. Large, partially methylated domains affecting up to one-third of the genome showed increased mutation rates and gene silencing in a subgroup-specific fashion. Epigenetic alterations also affected novel medulloblastoma candidate genes (for example, LIN28B), resulting in alternative promoter usage and/or differential messenger RNA/microRNA expression. Analysis of mouse medulloblastoma and precursor-cell methylation demonstrated a somatic origin for many alterations. Our data provide insights into the epigenetic regulation of transcription and genome organization in medulloblastoma pathogenesis, which are probably also of importance in a wider developmental and disease context.

BioModels: expanding horizons to include more modelling approaches and formats.

BioModels serves as a central repository of mathematical models representing biological processes. It offers a platform to make mathematical models easily shareable across the systems modelling community, thereby supporting model reuse. To facilitate hosting a broader range of model formats derived from diverse modelling approaches and tools, a new infrastructure for BioModels has been developed that is available at http://www.ebi.ac.uk/biomodels. This new system allows submitting and sharing of a wide range of models with improved support for formats other than SBML. It also offers a version-control backed environment in which authors and curators can work collaboratively to curate models. This article summarises the features available in the current system and discusses the potential benefit they offer to the users over the previous system. In summary, the new portal broadens the scope of models accepted in BioModels and supports collaborative model curation which is crucial for model reproducibility and sharing.

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

Dissecting the genomic complexity underlying medulloblastoma.

Medulloblastoma is an aggressively growing tumour, arising in the cerebellum or medulla/brain stem. It is the most common malignant brain tumour in children, and shows tremendous biological and clinical heterogeneity. Despite recent treatment advances, approximately 40% of children experience tumour recurrence, and 30% will die from their disease. Those who survive often have a significantly reduced quality of life. Four tumour subgroups with distinct clinical, biological and genetic profiles are currently identified. WNT tumours, showing activated wingless pathway signalling, carry a favourable prognosis under current treatment regimens. SHH tumours show hedgehog pathway activation, and have an intermediate prognosis. Group 3 and 4 tumours are molecularly less well characterized, and also present the greatest clinical challenges. The full repertoire of genetic events driving this distinction, however, remains unclear. Here we describe an integrative deep-sequencing analysis of 125 tumour-normal pairs, conducted as part of the International Cancer Genome Consortium (ICGC) PedBrain Tumor Project. Tetraploidy was identified as a frequent early event in Group 3 and 4 tumours, and a positive correlation between patient age and mutation rate was observed. Several recurrent mutations were identified, both in known medulloblastoma-related genes (CTNNB1, PTCH1, MLL2, SMARCA4) and in genes not previously linked to this tumour (DDX3X, CTDNEP1, KDM6A, TBR1), often in subgroup-specific patterns. RNA sequencing confirmed these alterations, and revealed the expression of what are, to our knowledge, the first medulloblastoma fusion genes identified. Chromatin modifiers were frequently altered across all subgroups. These findings enhance our understanding of the genomic complexity and heterogeneity underlying medulloblastoma, and provide several potential targets for new therapeutics, especially for Group 3 and 4 patients.

Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores.

Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.

The COVID-19 Data Exchange Platform of the German University Medicine.

COVID-19 has challenged the healthcare systems worldwide. To quickly identify successful diagnostic and therapeutic approaches large data sharing approaches are inevitable. Though organizational clinical data are abundant, many of them are available only in isolated silos and largely inaccessible to external researchers. To overcome and tackle this challenge the university medicine network (comprising all 36 German university hospitals) has been founded in April 2020 to coordinate COVID-19 action plans, diagnostic and therapeutic strategies and collaborative research activities. 13 projects were initiated from which the CODEX project, aiming at the development of a Germany-wide Covid-19 Data Exchange Platform, is presented in this publication. We illustrate the conceptual design, the stepwise development and deployment, first results and the current status.

Hypertension delays viral clearance and exacerbates airway hyperinflammation in patients with COVID-19.

In coronavirus disease 2019 (COVID-19), hypertension and cardiovascular diseases are major risk factors for critical disease progression. However, the underlying causes and the effects of the main anti-hypertensive therapies-angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs)-remain unclear. Combining clinical data (n = 144) and single-cell sequencing data of airway samples (n = 48) with in vitro experiments, we observed a distinct inflammatory predisposition of immune cells in patients with hypertension that correlated with critical COVID-19 progression. ACEI treatment was associated with dampened COVID-19-related hyperinflammation and with increased cell intrinsic antiviral responses, whereas ARB treatment related to enhanced epithelial-immune cell interactions. Macrophages and neutrophils of patients with hypertension, in particular under ARB treatment, exhibited higher expression of the pro-inflammatory cytokines CCL3 and CCL4 and the chemokine receptor CCR1. Although the limited size of our cohort does not allow us to establish clinical efficacy, our data suggest that the clinical benefits of ACEI treatment in patients with COVID-19 who have hypertension warrant further investigation.

COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis.

To investigate the immune response and mechanisms associated with severe coronavirus disease 2019 (COVID-19), we performed single-cell RNA sequencing on nasopharyngeal and bronchial samples from 19 clinically well-characterized patients with moderate or critical disease and from five healthy controls. We identified airway epithelial cell types and states vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In patients with COVID-19, epithelial cells showed an average three-fold increase in expression of the SARS-CoV-2 entry receptor ACE2, which correlated with interferon signals by immune cells. Compared to moderate cases, critical cases exhibited stronger interactions between epithelial and immune cells, as indicated by ligand-receptor expression profiles, and activated immune cells, including inflammatory macrophages expressing CCL2, CCL3, CCL20, CXCL1, CXCL3, CXCL10, IL8, IL1B and TNF. The transcriptional differences in critical cases compared to moderate cases likely contribute to clinical observations of heightened inflammatory tissue damage, lung injury and respiratory failure. Our data suggest that pharmacologic inhibition of the CCR1 and/or CCR5 pathways might suppress immune hyperactivation in critical COVID-19.

Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters.

BACKGROUND: Bidirectional promoters (BPs) are prevalent in eukaryotic genomes. However, it is poorly understood how the cell integrates different epigenomic information, such as transcription factor (TF) binding and chromatin marks, to drive gene expression at BPs. Single-cell sequencing technologies are revolutionizing the field of genome biology. Therefore, this study focuses on the integration of single-cell RNA-seq data with bulk ChIP-seq and other epigenetics data, for which single-cell technologies are not yet established, in the context of BPs. RESULTS: We performed integrative analyses of novel human single-cell RNA-seq (scRNA-seq) data with bulk ChIP-seq and other epigenetics data. scRNA-seq data revealed distinct transcription states of BPs that were previously not recognized. We find associations between these transcription states to distinct patterns in structural gene features, DNA accessibility, histone modification, DNA methylation and TF binding profiles. CONCLUSIONS: Our results suggest that a complex interplay of all of these elements is required to achieve BP-specific transcriptional output in this specialized promoter configuration. Further, our study implies that novel statistical methods can be developed to deconvolute masked subpopulations of cells measured with different bulk epigenomic assays using scRNA-seq data.

Whole genome sequencing puts forward hypotheses on metastasis evolution and therapy in colorectal cancer.

Incomplete understanding of the metastatic process hinders personalized therapy. Here we report the most comprehensive whole-genome study of colorectal metastases vs. matched primary tumors. 65% of somatic mutations originate from a common progenitor, with 15% being tumor- and 19% metastasis-specific, implicating a higher mutation rate in metastases. Tumor- and metastasis-specific mutations harbor elevated levels of BRCAness. We confirm multistage progression with new components ARHGEF7/ARHGEF33. Recurrently mutated non-coding elements include ncRNAs RP11-594N15.3, AC010091, SNHG14, 3' UTRs of FOXP2, DACH2, TRPM3, XKR4, ANO5, CBL, CBLB, the latter four potentially dual protagonists in metastasis and efferocytosis-/PD-L1 mediated immunosuppression. Actionable metastasis-specific lesions include FAT1, FGF1, BRCA2, KDR, and AKT2-, AKT3-, and PDGFRA-3' UTRs. Metastasis specific mutations are enriched in PI3K-Akt signaling, cell adhesion, ECM and hepatic stellate activation genes, suggesting genetic programs for site-specific colonization. Our results put forward hypotheses on tumor and metastasis evolution, and evidence for metastasis-specific events relevant for personalized therapy.

OTP: An automatized system for managing and processing NGS data.

The One Touch Pipeline (OTP) is an automation platform managing Next-Generation Sequencing (NGS) data and calling bioinformatic pipelines for processing these data. OTP handles the complete digital process from import of raw sequence data via alignment of sequencing reads to identify genomic events in an automated and scalable way. Three major goals are pursued: firstly, reduction of human resources required for data management by introducing automated processes. Secondly, reduction of time until the sequences can be analyzed by bioinformatic experts, by executing all operations more reliably and quickly. Thirdly, storing all information in one system with secure web access and search capabilities. From software architecture perspective, OTP is both information center and workflow management system. As a workflow management system, OTP call several NGS pipelines that can easily be adapted and extended according to new requirements. As an information center, it comprises a database for metadata information as well as a structured file system. Based on complete and consistent information, data management and bioinformatic pipelines within OTP are executed automatically with all steps book-kept in a database.

Prediction of clinical outcome and biological characterization of neuroblastoma by expression profiling.

Neuroblastoma is a common childhood tumor comprising cases with rapid disease progression as well as spontaneous regression. Although numerous prognostic factors have been identified, risk evaluation in individual patients remains difficult. To define a reliable prognostic predictor and gene signatures characteristic of biological subgroups, we performed mRNA expression profiling of 68 neuroblastomas of all stages. Expression data were analysed using support vector machines (SVM-rbf), prediction analysis of microarrays (PAM), k-nearest neighbors (k-NN) algorithms and multiple decision trees. SVM-rbf performed best of all methods, and predicted recurrence of neuroblastoma with an accuracy of 85% (sensitivity 77%, specificity 94%). PAM identified a classifier of 39 genes reliably predicting outcome with an accuracy of 80%. In comparison, conventional risk stratification based on stage, age and MYCN-status only reached a predictive accuracy of 64%. Kaplan-Meier analysis using the PAM classifier indicated a 5-year survival of 20 versus 78% for patients with unfavorably versus favorably predicted neuroblastomas, respectively (P = 0.0001). Significance analysis of microarrays (SAM) identified additional genes differentially expressed among subgroups. MYCN-amplification and high expression of NTRK1/TrkA demonstrated a strong association with specific gene expression patterns. Our data suggest that microarray-derived data in addition to traditional clinical factors will be useful for risk assessment and defining biological properties of neuroblastoma.

Microarray analysis reveals differential gene expression patterns and regulation of single target genes contributing to the opposing phenotype of TrkA- and TrkB-expressing neuroblastomas.

Expression of neurotrophin receptors of the tyrosine kinase receptor (Trk) family is an important prognostic factor in solid tumors including neuroblastoma. High expression of TrkA (NTRK1) is associated with a favorable biology and outcome of neuroblastoma, whereas TrkB (NTRK2) is expressed on aggressive neuroblastomas with unfavorable outcome. To gain new insights into the global gene expression program resulting in these divergent biological phenotypes, we stably expressed either TrkA or TrkB in the human SH-SY5Y neuroblastoma cell line. Gene expression profiles were obtained from parental cells and transfectants activated by their ligands in a time course over 24 h using oligonucleotide microarrays. Basal activation of Trk receptors in the absence of exogenous ligand was sufficient to induce broad and divergent genetic changes. Global gene regulation following external ligand stimulation was surprisingly similar in SY5Y-TrkA and SY5Y-TrkB cells except for the differential expression of distinct novel target genes. Consistent with their divergent biological phenotype, SY5Y-TrkA cells were characterized by upregulation of proapoptotic genes and angiogenesis inhibitors, whereas SY5Y-TrkB cells demonstrated upregulation of genes involved in invasion or therapy resistance. We suggest that the transcriptional program of neuroblastoma cells is modulated by Trk-receptor expression and basal activation rather than by ligand-induced activation. Fine-tuning of the malignant phenotype may be achieved by additional ligand stimulation with subsequent activation of a few specific genes.

Molecular profiling of temporal lobe epilepsy: comparison of data from human tissue samples and animal models.

The advent of gene chip technology and the era of functional genomics have initially been accompanied by huge anticipations to quickly unravel the molecular pathogenesis of multifactorial diseases. Expectations have, today, given way to some concerns about this non-hypothesis driven approach. However, the careful and controlled application of expression microarrays in concert with refined bioinformatic tools may provide novel insights in major disorders particularly of highly complex organs such as the central nervous system (CNS). Epilepsies are among the most frequent CNS disorders affecting approximately 1.5% of the population worldwide. In temporal lobe epilepsy (TLE), the seizure origin typically involves the hippocampal formation, a structure located in the mesial temporal lobe. Many TLE patients develop pharmacoresistance, i.e. seizures can no more be controlled by antiepileptic drugs. In order to achieve seizure control, surgical removal of the epileptogenic focus has been established as successful therapeutic strategy. Hippocampal biopsy tissue of pharmacoresistant TLE patients represents an excellent substrate to analyze molecular mechanisms related to structural and cellular reorganization in epilepsy. The complexity of alterations in TLE hippocampi suggests numerous genes and signaling cascades to be involved in the pathogenesis. By microarrays, genome wide expression profiles can be constituted from TLE tissues. However, hippocampi of pharmacoresistant TLE patients represent an advanced stage of the disease. Early stages of epilepsy development are not available for functional genome analysis in humans. Animal models of TLE appear particularly helpful to study molecular mechanisms of highly dynamic processes such as the development of hyperexcitability and pharmacoresistance. In this review, we summarize recent data of gene expression profiles in human and experimental TLE and discuss the relevance of novel tools for bioinformatic analysis and data mining.

Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer.

Early-onset prostate cancer (EO-PCA) represents the earliest clinical manifestation of prostate cancer. To compare the genomic alteration landscapes of EO-PCA with "classical" (elderly-onset) PCA, we performed deep sequencing-based genomics analyses in 11 tumors diagnosed at young age, and pursued comparative assessments with seven elderly-onset PCA genomes. Remarkable age-related differences in structural rearrangement (SR) formation became evident, suggesting distinct disease pathomechanisms. Whereas EO-PCAs harbored a prevalence of balanced SRs, with a specific abundance of androgen-regulated ETS gene fusions including TMPRSS2:ERG, elderly-onset PCAs displayed primarily non-androgen-associated SRs. Data from a validation cohort of > 10,000 patients showed age-dependent androgen receptor levels and a prevalence of SRs affecting androgen-regulated genes, further substantiating the activity of a characteristic "androgen-type" pathomechanism in EO-PCA.