Pituitary adenylate cyclase-activating peptide induces long-lasting neuroprotection through the induction of activity-dependent signaling via the cyclic AMP response element-binding protein-regulated transcription co-activator 1

Pituitary adenylate cyclase-activating peptide (PACAP) is a neuroprotective peptide which exerts its effects mainly through the cAMP-protein kinase A (PKA) pathway. Here, we show that in cortical neurons, PACAP-induced PKA signaling exerts a major part of its neuroprotective effects indirectly, by triggering action potential (AP) firing. Treatment of cortical neurons with PACAP induces a rapid and sustained PKA-dependent increase in AP firing and associated intracellular Ca(2+) transients, which are essential for the anti-apoptotic actions of PACAP. Transient exposure to PACAP induces long-lasting neuroprotection in the face of apoptotic insults which is reliant on AP firing and the activation of cAMP response element (CRE) binding protein (CREB)-mediated gene expression. Although direct, activity-independent PKA signaling is sufficient to trigger phosphorylation on CREB’s activating serine-133 site, this is insufficient for activation of CREB-mediated gene expression. Full activation is dependent on CREB-regulated transcription co-activator 1 (CRTC1), whose PACAP-induced nuclear import is dependent on firing activity-dependent calcineurin signaling. Over-expression of CRTC1 is sufficient to rescue PACAP-induced CRE-mediated gene expression in the face of activity-blockade, while dominant negative CRTC1 interferes with PACAP-induced, CREB-mediated neuroprotection. Thus, the enhancement of AP firing may play a significant role in the neuroprotective actions of PACAP and other adenylate cyclase-coupled ligands

The Subtype of GluN2 C-terminal Domain Determines the Response to Excitotoxic Insults

It is currently unclear whether the GluN2 subtype influences NMDA receptor (NMDAR) excitotoxicity. We report that the toxicity of NMDAR-mediated Ca(2+) influx is differentially controlled by the cytoplasmic C-terminal domains of GluN2B (CTD(2B)) and GluN2A (CTD(2A)). Studying the effects of acute expression of GluN2A/2B-based chimeric subunits with reciprocal exchanges of their CTDs revealed that CTD(2B) enhances NMDAR toxicity, compared to CTD(2A). Furthermore, the vulnerability of forebrain neurons in vitro and in vivo to NMDAR-dependent Ca(2+) influx is lowered by replacing the CTD of GluN2B with that of GluN2A by targeted exon exchange in a mouse knockin model. Mechanistically, CTD(2B) exhibits stronger physical/functional coupling to the PSD-95-nNOS pathway, which suppresses protective CREB activation. Dependence of NMDAR excitotoxicity on the GluN2 CTD subtype can be overcome by inducing high levels of NMDAR activity. Thus, the identity (2A versus 2B) of the GluN2 CTD controls the toxicity dose-response to episodes of NMDAR activity

The Type 2 Diabetes Knowledge Portal: an Open access Genetic Resource Dedicated to Type 2 Diabetes and Related Traits

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP\u27s comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results

Scaling up data curation using deep learning: An application to literature triage in genomic variation resources.

Manually curating biomedical knowledge from publications is necessary to build a knowledge based service that provides highly precise and organized information to users. The process of retrieving relevant publications for curation, which is also known as document triage, is usually carried out by querying and reading articles in PubMed. However, this query-based method often obtains unsatisfactory precision and recall on the retrieved results, and it is difficult to manually generate optimal queries. To address this, we propose a machine-learning assisted triage method. We collect previously curated publications from two databases UniProtKB/Swiss-Prot and the NHGRI-EBI GWAS Catalog, and used them as a gold-standard dataset for training deep learning models based on convolutional neural networks. We then use the trained models to classify and rank new publications for curation. For evaluation, we apply our method to the real-world manual curation process of UniProtKB/Swiss-Prot and the GWAS Catalog. We demonstrate that our machine-assisted triage method outperforms the current query-based triage methods, improves efficiency, and enriches curated content. Our method achieves a precision 1.81 and 2.99 times higher than that obtained by the current query-based triage methods of UniProtKB/Swiss-Prot and the GWAS Catalog, respectively, without compromising recall. In fact, our method retrieves many additional relevant publications that the query-based method of UniProtKB/Swiss-Prot could not find. As these results show, our machine learning-based method can make the triage process more efficient and is being implemented in production so that human curators can focus on more challenging tasks to improve the quality of knowledge bases

Functional interdependence of BRD4 and DOT1L in MLL leukemia.

Targeted therapies against disruptor of telomeric silencing 1-like (DOT1L) and bromodomain-containing protein 4 (BRD4) are currently being evaluated in clinical trials. However, the mechanisms by which BRD4 and DOT1L regulate leukemogenic transcription programs remain unclear. Using quantitative proteomics, chemoproteomics and biochemical fractionation, we found that native BRD4 and DOT1L exist in separate protein complexes. Genetic disruption or small-molecule inhibition of BRD4 and DOT1L showed marked synergistic activity against MLL leukemia cell lines, primary human leukemia cells and mouse leukemia models. Mechanistically, we found a previously unrecognized functional collaboration between DOT1L and BRD4 that is especially important at highly transcribed genes in proximity to superenhancers. DOT1L, via dimethylated histone H3 K79, facilitates histone H4 acetylation, which in turn regulates the binding of BRD4 to chromatin. These data provide new insights into the regulation of transcription and specify a molecular framework for therapeutic intervention in this disease with poor prognosis

The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation.

Polygenic [risk] scores (PGS) can enhance prediction and understanding of common diseases and traits. However, the reproducibility of PGS and their subsequent applications in biological and clinical research have been hindered by several factors, including: inadequate and incomplete reporting of PGS development, heterogeneity in evaluation techniques, and inconsistent access to, and distribution of, the information necessary to calculate the scores themselves. To address this we present the PGS Catalog (www.PGSCatalog.org), an open resource for polygenic scores. The PGS Catalog currently contains 238 published PGS and data from 92 publications for 113 traits, including diabetes, cardiovascular diseases, neurological disorders, cancers, body mass index and blood lipids. Full scoring information for each PGS is available, along with consistently curated metadata required for reproducibility as well as accurate application in independent studies. Using the PGS Catalog, we demonstrate that multiple PGS can be systematically evaluated to generate comparable performance metrics. The PGS Catalog has capabilities for user deposition, expert curation and programmatic access, thus providing the community with an open platform for polygenic score dissemination, research and translation.This work was supported by core funding from: the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194;RG/18/13/33946) and the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] [*]. This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. MI was supported by the Munz Chair of Cardiovascular Prediction and Prevention. This study was supported by the Victorian Government’s Operational Infrastructure Support (OIS) program. Research reported in this publication was supported by the National Human Genome Research Institute of the National Institutes of Health under Award Number U41HG007823. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. In addition, we acknowledge funding from the European Molecular Biology Laboratory. JD holds a British Heart Foundation Chair and is funded by the National Institute for Health Research [Senior Investigator Award] [*]. MI and SR are supported by the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust]. SL is supported by a postdoctoral fellowship award from the Canadian Institutes of Health Research

A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog

Abstract The accurate description of ancestry is essential to interpret, access, and integrate human genomics data, and to ensure that these benefit individuals from all ancestral backgrounds. However, there are no established guidelines for the representation of ancestry information. Here we describe a framework for the accurate and standardized description of sample ancestry, and validate it by application to the NHGRI-EBI GWAS Catalog. We confirm known biases and gaps in diversity, and find that African and Hispanic or Latin American ancestry populations contribute a disproportionately high number of associations. It is our hope that widespread adoption of this framework will lead to improved analysis, interpretation, and integration of human genomics data