Although genome sequencing has identified numerous noncoding alterations between primate species, which of those are regulatory and potentially relevant to the evolution of the human brain is unclear. Here we annotated cis-regulatory elements (CREs) in the human, rhesus macaque and chimpanzee genomes using chromatin immunoprecipitation followed by sequencing (ChIP-seq) in different anatomical regions of the adult brain. We found high similarity in the genomic positioning of rhesus macaque and human CREs, suggesting that the majority of these elements were already present in a common ancestor 25 million years ago. Most of the observed regulatory changes between humans and rhesus macaques occurred before the ancestral separation of humans and chimpanzees, leaving a modest set of regulatory elements with predicted human specificity. Our data refine previous predictions and hypotheses on the consequences of genomic changes between primate species and allow the identification of regulatory alterations relevant to the evolution of the brain.

In vertebrate embryos, anterior tissues are generated early, followed by the other axial structures that emerge sequentially from a posterior growth zone. The genetic network driving posterior axial elongation in mice, and its disturbance in mutants with posterior truncation, is not yet fully understood. Here, we show that the combined expression of Cdx2 and T Brachyury is essential to establish the core signature of posterior axial progenitors. Cdx2 and T Brachyury are required for extension of a similar trunk portion of the axis. Simultaneous loss of function of these two genes disrupts axial elongation to a much greater extent than each single mutation alone. We identify and validate common targets for Cdx2 and T Brachyury in vivo, including Wnt and Fgf pathway components active in the axial progenitor niche. Our data demonstrate that integration of the Cdx/Hox and T Brachyury transcriptional networks controls differential axial growth during vertebrate trunk elongation.

The underlying molecular mechanisms for many autoimmune diseases are poorly understood. Juvenile idiopathic arthritis (JIA) is an exceptionally well-suited model for studying autoimmune diseases due to its early onset and the possibility to analyze cells derived from the site of inflammation. Epigenetic profiling, utilizing primary JIA patient-derived cells, can contribute to the understanding of autoimmune diseases. With H3K27ac chromatin immunoprecipitation, we identified a disease-specific, inflammation-associated, typical enhancer and super-enhancer signature in JIA patient synovial-fluid-derived CD4(+) memory/effector T cells. RNA sequencing of autoinflammatory site-derived patient T cells revealed that BET inhibition, utilizing JQ1, inhibited immune-related super-enhancers and preferentially reduced disease-associated gene expression, including cytokine-related processes. Altogether, these results demonstrate the potential use of enhancer profiling to identify disease mediators and provide evidence for BET inhibition as a possible therapeutic approach for the treatment of autoimmune diseases.

Sequential 3'-to-5' activation of the Hox gene clusters in early embryos is a most fascinating issue in developmental biology. Neither the trigger nor the regulatory elements involved in the transcriptional initiation of the 3'-most Hox genes have been unraveled in any organism. We demonstrate that a series of enhancers, some of which are Wnt-dependent, is located within a HoxA 3' subtopologically associated domain (subTAD). This subTAD forms the structural basis for multiple layers of 3'-polarized features, including DNA accessibility and enhancer activation. Deletion of the cassette of Wnt-dependent enhancers proves its crucial role in initial transcription of HoxA at the 3' side of the cluster.

Background The new concept of difficult-to-treat rheumatoid arthritis (D2T RA) refers to RA patients who remain symptomatic after several lines of treatment, resulting in a high patient and economic burden. During a hackathon, we aimed to identify and predict D2T RA patients in structured and unstructured routine care data. Methods Routine care data of 1873 RA patients were extracted from the Utrecht Patient Oriented Database. Data from a previous cross-sectional study, in which 152 RA patients were clinically classified as either D2T or non-D2T, served as a validation set. Machine learning techniques, text mining, and feature importance analyses were performed to identify and predict D2T RA patients based on structured and unstructured routine care data. Results We identified 123 potentially new D2T RA patients by applying the D2T RA definition in structured and unstructured routine care data. Additionally, we developed a D2T RA identification model derived from a feature importance analysis of all available structured data (AUC-ROC 0.88 (95% CI 0.82–0.94)), and we demonstrated the potential of longitudinal hematological data to differentiate D2T from non-D2T RA patients using supervised dimension reduction. Lastly, using data up to the time of starting the first biological treatment, we predicted future development of D2TRA (AUC-ROC 0.73 (95% CI 0.71–0.75)). Conclusions During this hackathon, we have demonstrated the potential of different techniques for the identification and prediction of D2T RA patients in structured as well as unstructured routine care data. The results are promising and should be optimized and validated in future research.

Our findings suggest that variations in or near the HPN and LTBP4 genes do not play a role in the susceptibility to IA in the Dutch population.

PURPOSE Interpretation of genomic variants in tumor samples still presents a challenge in research and the clinical setting. A major issue is that information for variant interpretation is fragmented across disparate databases, and aggregation of information from these requires building extensive infrastructure. To this end, we have developed Genome Nexus, a one-stop shop for variant annotation with a user-friendly interface for cancer researchers and clinicians. METHODS Genome Nexus (1) aggregates variant information from sources that are relevant to cancer research and clinical applications, (2) allows high-performance programmatic access to the aggregated data via a unified application programming interface, (3) provides a reference page for individual cancer variants, (4) provides user-friendly tools for annotating variants in patients, and (5) is freely available under an open source license and can be installed in a private cloud or local environment and integrated with local institutional resources. RESULTS Genome Nexus is available at https://www.genomenexus.org . It displays annotations from more than a dozen resources including those that provide variant effect information (variant effect predictor), protein sequence annotation (Uniprot, Pfam, and dbPTM), functional consequence prediction (Polyphen-2, Mutation Assessor, and SIFT), population prevalences (gnomAD, dbSNP, and ExAC), cancer population prevalences (Cancer hotspots and SignalDB), and clinical actionability (OncoKB, CIViC, and ClinVar). We describe several use cases that demonstrate the utility of Genome Nexus to clinicians, researchers, and bioinformaticians. We cover single-variant annotation, cohort analysis, and programmatic use of the application programming interface. Genome Nexus is unique in providing a user-friendly interface specific to cancer that allows high-performance annotation of any variant including unknown ones. CONCLUSION Interpretation of cancer genomic variants is improved tremendously by having an integrated resource for annotations. Genome Nexus is freely available under an open source license.

When developing models for clinical information retrieval and decision support systems, the discrete outcomes required for training are often missing. These labels need to be extracted from free text in electronic health records. For this extraction process one of the most important contextual properties in clinical text is negation, which indicates the absence of findings. We aimed to improve large scale extraction of labels by comparing three methods for negation detection in Dutch clinical notes. We used the Erasmus Medical Center Dutch Clinical Corpus to compare a rule-based method based on ContextD, a biLSTM model using MedCAT and (finetuned) RoBERTa-based models. We found that both the biLSTM and RoBERTa models consistently outperform the rule-based model in terms of F1 score, precision and recall. In addition, we systematically categorized the classification errors for each model, which can be used to further improve model performance in particular applications. Combining the three models naively was not beneficial in terms of performance. We conclude that the biLSTM and RoBERTa-based models in particular are highly accurate accurate in detecting clinical negations, but that ultimately all three approaches can be viable depending on the use case at hand.