Sialic acid-binding immunoglobulin-like lectin (Sigelac)-15 is a rapidly internalised cell-surface antigen expressed by acute myeloid leukaemia cells.

Sialic acid-binding immunoglobulin-like lectin (Siglec)-15 has recently been identified as a critical tumour checkpoint, augmenting the expression and function of programmed death-ligand 1. We raised a monoclonal antibody, A9E8, specific for Siglec-15 using phage display. A9E8 stained myeloid leukaemia cell lines and peripheral cluster of differentiation (CD)33+ blasts and CD34+ leukaemia stem cells from patients with acute myeloid leukaemia (AML). By contrast, there was minimal expression on healthy donor leucocytes or CD34+ stem cells from non-AML donors, suggesting targeting Siglec-15 may have significant therapeutic advantages over its fellow Siglec CD33. After binding, A9E8 was rapidly internalised (half-life of 180 s) into K562 cells. Antibodies to Siglec-15 therefore hold therapeutic potential for AML treatment.

TARM1 Is a Novel Leukocyte Receptor Complex-Encoded ITAM Receptor That Costimulates Proinflammatory Cytokine Secretion by Macrophages and Neutrophils.

We identified a novel, evolutionarily conserved receptor encoded within the human leukocyte receptor complex and syntenic region of mouse chromosome 7, named T cell-interacting, activating receptor on myeloid cells-1 (TARM1). The transmembrane region of TARM1 contained a conserved arginine residue, consistent with association with a signaling adaptor. TARM1 associated with the ITAM adaptor FcRγ but not with DAP10 or DAP12. In healthy mice, TARM1 is constitutively expressed on the cell surface of mature and immature CD11b(+)Gr-1(+) neutrophils within the bone marrow. Following i.p. LPS treatment or systemic bacterial challenge, TARM1 expression was upregulated by neutrophils and inflammatory monocytes and TARM1(+) cells were rapidly recruited to sites of inflammation. TARM1 expression was also upregulated by bone marrow-derived macrophages and dendritic cells following stimulation with TLR agonists in vitro. Ligation of TARM1 receptor in the presence of TLR ligands, such as LPS, enhanced the secretion of proinflammatory cytokines by macrophages and primary mouse neutrophils, whereas TARM1 stimulation alone had no effect. Finally, an immobilized TARM1-Fc fusion protein suppressed CD4(+) T cell activation and proliferation in vitro. These results suggest that a putative T cell ligand can interact with TARM1 receptor, resulting in bidirectional signaling and raising the T cell activation threshold while costimulating the release of proinflammatory cytokines by macrophages and neutrophils.

Roadmap for cardiovascular circulation model.

Computational models of many aspects of the mammalian cardiovascular circulation have been developed. Indeed, along with orthopaedics, this area of physiology is one that has attracted much interest from engineers, presumably because the equations governing blood flow in the vascular system are well understood and can be solved with well-established numerical techniques. Unfortunately, there have been only a few attempts to create a comprehensive public domain resource for cardiovascular researchers. In this paper we propose a roadmap for developing an open source cardiovascular circulation model. The model should be registered to the musculo-skeletal system. The computational infrastructure for the cardiovascular model should provide for near real-time computation of blood flow and pressure in all parts of the body. The model should deal with vascular beds in all tissues, and the computational infrastructure for the model should provide links into CellML models of cell function and tissue function. In this work we review the literature associated with 1D blood flow modelling in the cardiovascular system, discuss model encoding standards, software and a model repository. We then describe the coordinate systems used to define the vascular geometry, derive the equations and discuss the implementation of these coupled equations in the open source computational software OpenCMISS. Finally, some preliminary results are presented and plans outlined for the next steps in the development of the model, the computational software and the graphical user interface for accessing the model.

Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders.

Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).

Eliciting candidate anatomical routes for protein interactions: a scenario from endocrine physiology.

BACKGROUND: In this paper, we use: i) formalised anatomical knowledge of connectivity between body structures and ii) a formal theory of physiological transport between fluid compartments in order to define and make explicit the routes followed by proteins to a site of interaction. The underlying processes are the objects of mathematical models of physiology and, therefore, the motivation for the approach can be understood as using knowledge representation and reasoning methods to propose concrete candidate routes corresponding to correlations between variables in mathematical models of physiology. In so doing, the approach projects physiology models onto a representation of the anatomical and physiological reality which underpins them. RESULTS: The paper presents a method based on knowledge representation and reasoning for eliciting physiological communication routes. In doing so, the paper presents the core knowledge representation and algorithms using it in the application of the method. These are illustrated through the description of a prototype implementation and the treatment of a simple endocrine scenario whereby a candidate route of communication between ANP and its receptors on the external membrane of smooth muscle cells in renal arterioles is elicited. The potential of further development of the approach is illustrated through the informal discussion of a more complex scenario. CONCLUSIONS: The work presented in this paper supports research in intercellular communication by enabling knowledge-based inference on physiologically-related biomedical data and models.

An infrastructure for ontology-based information systems in biomedicine: RICORDO case study.

SUMMARY: The article presents an infrastructure for supporting the semantic interoperability of biomedical resources based on the management (storing and inference-based querying) of their ontology-based annotations. This infrastructure consists of: (i) a repository to store and query ontology-based annotations; (ii) a knowledge base server with an inference engine to support the storage of and reasoning over ontologies used in the annotation of resources; (iii) a set of applications and services allowing interaction with the integrated repository and knowledge base. The infrastructure is being prototyped and developed and evaluated by the RICORDO project in support of the knowledge management of biomedical resources, including physiology and pharmacology models and associated clinical data. AVAILABILITY AND IMPLEMENTATION: The RICORDO toolkit and its source code are freely available from http://ricordo.eu/relevant-resources. CONTACT: sarala@ebi.ac.uk.

Toxicology knowledge graph for structural birth defects.

BACKGROUND: Birth defects are functional and structural abnormalities that impact about 1 in 33 births in the United States. They have been attributed to genetic and other factors such as drugs, cosmetics, food, and environmental pollutants during pregnancy, but for most birth defects there are no known causes. METHODS: To further characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with a focus on associations between birth defects, drugs, and genes. Specifically, we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression changes in cell lines, known drug targets, genetic burden scores for human genes, and placental crossing scores for small molecules. RESULTS: Using ReproTox-KG and semi-supervised learning (SSL), we scored >30,000 preclinical small molecules for their potential to cross the placenta and induce birth defects, and identified >500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG can be accessed via a web-based user interface available at https://maayanlab.cloud/reprotox-kg . This site enables users to explore the associations between birth defects, approved and preclinical drugs, and all human genes. CONCLUSIONS: ReproTox-KG provides a resource for exploring knowledge about the molecular mechanisms of birth defects with the potential of predicting the likelihood of genes and preclinical small molecules to induce birth defects.

While birth defects are common, for most birth defects there are no known causes. During pregnancy, developing babies are exposed to drugs, cosmetics, food, and environmental pollutants that may cause birth defects. However, exactly how these environmental factors are involved in producing birth defects is difficult to discern. Also, birth defects can be a consequence of the genes inherited from the parents. We combined general data about human genes and drugs with specific data previously implicating genes and drugs in inducing birth defects to create a knowledge graph representation that connects genes, drugs, and birth defects. This knowledge graph can be used to explore new links that may explain why birth defects occur, particularly those that result from a combination of inherited and environmental influences.

ApiNATOMY: a novel toolkit for visualizing multiscale anatomy schematics with phenotype-related information.

A significant proportion of biomedical resources carries information that cross references to anatomical structures across multiple scales. To improve the visualization of such resources in their anatomical context, we developed an automated methodology that produces anatomy schematics in a consistent manner,and provides for the overlay of anatomy-related resource information onto the same diagram. This methodology, called ApiNATOMY, draws upon the topology of ontology graphs to automatically lay out treemaps representing body parts as well as semantic metadata linking to such ontologies. More generally, ApiNATOMY treemaps provide an efficient and manageable way to visualize large biomedical ontologies in a meaningful and consistent manner. In the anatomy domain, such treemaps will allow epidemiologists, clinicians, and biomedical scientists to review, and interact with, anatomically aggregated heterogeneous data and model resources. Such an approach supports the visual identification of functional relations between anatomically colocalized resources that may not be immediately amenable to automation by ontology-based inferencing. We also describe the application of ApiNATOMY schematics to integrate, and add value to, human phenotype-related information­results are found at http://apinatomy.org. The long-term goal for the ApiNATOMY toolkit is to support clinical and scientific graphical user interfaces and dashboards for biomedical resource management and data analytics.

Transcription profiling in human platelets reveals LRRFIP1 as a novel protein regulating platelet function.

Within the healthy population, there is substantial, heritable, and interindividual variability in the platelet response. We explored whether a proportion of this variability could be accounted for by interindividual variation in gene expression. Through a correlative analysis of genome-wide platelet RNA expression data from 37 subjects representing the normal range of platelet responsiveness within a cohort of 500 subjects, we identified 63 genes in which transcript levels correlated with variation in the platelet response to adenosine diphosphate and/or the collagen-mimetic peptide, cross-linked collagen-related peptide. Many of these encode proteins with no reported function in platelets. An association study of 6 of the 63 genes in 4235 cases and 6379 controls showed a putative association with myocardial infarction for COMMD7 (COMM domain-containing protein 7) and a major deviation from the null hypo thesis for LRRFIP1 [leucine-rich repeat (in FLII) interacting protein 1]. Morpholino-based silencing in Danio rerio identified a modest role for commd7 and a significant effect for lrrfip1 as positive regulators of thrombus formation. Proteomic analysis of human platelet LRRFIP1-interacting proteins indicated that LRRFIP1 functions as a component of the platelet cytoskeleton, where it interacts with the actin-remodeling proteins Flightless-1 and Drebrin. Taken together, these data reveal novel proteins regulating the platelet response.

Representing Normal and Abnormal Physiology as Routes of Flow in ApiNATOMY.

We present (i) the ApiNATOMY workflow to build knowledge models of biological connectivity, as well as (ii) the ApiNATOMY TOO map, a topological scaffold to organize and visually inspect these connectivity models in the context of a canonical architecture of body compartments. In this work, we outline the implementation of ApiNATOMY's knowledge representation in the context of a large-scale effort, SPARC, to map the autonomic nervous system. Within SPARC, the ApiNATOMY modeling effort has generated the SCKAN knowledge graph that combines connectivity models and TOO map. This knowledge graph models flow routes for a number of normal and disease scenarios in physiology. Calculations over SCKAN to infer routes are being leveraged to classify, navigate and search for semantically-linked metadata of multimodal experimental datasets for a number of cross-scale, cross-disciplinary projects.

Extending and using anatomical vocabularies in the stimulating peripheral activity to relieve conditions project.

The stimulating peripheral activity to relieve conditions (SPARC) program is a US National Institutes of Health-funded effort to improve our understanding of the neural circuitry of the autonomic nervous system (ANS) in support of bioelectronic medicine. As part of this effort, the SPARC project is generating multi-species, multimodal data, models, simulations, and anatomical maps supported by a comprehensive knowledge base of autonomic circuitry. To facilitate the organization of and integration across multi-faceted SPARC data and models, SPARC is implementing the findable, accessible, interoperable, and reusable (FAIR) data principles to ensure that all SPARC products are findable, accessible, interoperable, and reusable. We are therefore annotating and describing all products with a common FAIR vocabulary. The SPARC Vocabulary is built from a set of community ontologies covering major domains relevant to SPARC, including anatomy, physiology, experimental techniques, and molecules. The SPARC Vocabulary is incorporated into tools researchers use to segment and annotate their data, facilitating the application of these ontologies for annotation of research data. However, since investigators perform deep annotations on experimental data, not all terms and relationships are available in community ontologies. We therefore implemented a term management and vocabulary extension pipeline where SPARC researchers may extend the SPARC Vocabulary using InterLex, an online vocabulary management system. To ensure the quality of contributed terms, we have set up a curated term request and review pipeline specifically for anatomical terms involving expert review. Accepted terms are added to the SPARC Vocabulary and, when appropriate, contributed back to community ontologies to enhance ANS coverage. Here, we provide an overview of the SPARC Vocabulary, the infrastructure and process for implementing the term management and review pipeline. In an analysis of >300 anatomical contributed terms, the majority represented composite terms that necessitated combining terms within and across existing ontologies. Although these terms are not good candidates for community ontologies, they can be linked to structures contained within these ontologies. We conclude that the term request pipeline serves as a useful adjunct to community ontologies for annotating experimental data and increases the FAIRness of SPARC data.

Functional genomics in zebrafish permits rapid characterization of novel platelet membrane proteins.

In this study, we demonstrate the suitability of the vertebrate Danio rerio (zebrafish) for functional screening of novel platelet genes in vivo by reverse genetics. Comparative transcript analysis of platelets and their precursor cell, the megakaryocyte, together with nucleated blood cell elements, endothelial cells, and erythroblasts, identified novel platelet membrane proteins with hitherto unknown roles in thrombus formation. We determined the phenotype induced by antisense morpholino oligonucleotide (MO)-based knockdown of 5 of these genes in a laser-induced arterial thrombosis model. To validate the model, the genes for platelet glycoprotein (GP) IIb and the coagulation protein factor VIII were targeted. MO-injected fish showed normal thrombus initiation but severely impaired thrombus growth, consistent with the mouse knockout phenotypes, and concomitant knockdown of both resulted in spontaneous bleeding. Knockdown of 4 of the 5 novel platelet proteins altered arterial thrombosis, as demonstrated by modified kinetics of thrombus initiation and/or development. We identified a putative role for BAMBI and LRRC32 in promotion and DCBLD2 and ESAM in inhibition of thrombus formation. We conclude that phenotypic analysis of MO-injected zebrafish is a fast and powerful method for initial screening of novel platelet proteins for function in thrombosis.

A HaemAtlas: characterizing gene expression in differentiated human blood cells.

Hematopoiesis is a carefully controlled process that is regulated by complex networks of transcription factors that are, in part, controlled by signals resulting from ligand binding to cell-surface receptors. To further understand hematopoiesis, we have compared gene expression profiles of human erythroblasts, megakaryocytes, B cells, cytotoxic and helper T cells, natural killer cells, granulocytes, and monocytes using whole genome microarrays. A bioinformatics analysis of these data was performed focusing on transcription factors, immunoglobulin superfamily members, and lineage-specific transcripts. We observed that the numbers of lineage-specific genes varies by 2 orders of magnitude, ranging from 5 for cytotoxic T cells to 878 for granulocytes. In addition, we have identified novel coexpression patterns for key transcription factors involved in hematopoiesis (eg, GATA3-GFI1 and GATA2-KLF1). This study represents the most comprehensive analysis of gene expression in hematopoietic cells to date and has identified genes that play key roles in lineage commitment and cell function. The data, which are freely accessible, will be invaluable for future studies on hematopoiesis and the role of specific genes and will also aid the understanding of the recent genome-wide association studies.

A functional genomics approach reveals novel quantitative trait loci associated with platelet signaling pathways.

Platelet response to activation varies widely between individuals but shows interindividual consistency and strong heritability. The genetic basis of this variation has not been properly explored. We therefore systematically measured the effect on function of sequence variation in 97 candidate genes in the collagen and adenosine-diphosphate (ADP) signaling pathways. Resequencing of the genes in 48 European DNA samples nearly doubled the number of known single nucleotide polymorphisms (SNPs) and informed the selection of 1327 SNPs for genotyping in 500 healthy Northern European subjects with known platelet responses to collagen-related peptide (CRP-XL) and ADP. This identified 17 novel associations with platelet function (P < .005) accounting for approximately 46% of the variation in response. Further investigations with platelets of known genotype explored the mechanisms behind some of the associations. SNPs in PEAR1 associated with increased platelet response to CRP-XL and increased PEAR1 protein expression after platelet degranulation. The minor allele of a 3' untranslated region (UTR) SNP (rs2769668) in VAV3 was associated with higher protein expression (P = .03) and increased P-selectin exposure after ADP activation (P = .004). Furthermore the minor allele of the intronic SNP rs17786144 in ITPR1 modified Ca(2+) levels after activation with ADP (P < .004). These data provide novel insights into key hubs within platelet signaling networks.

Reactome knowledgebase of human biological pathways and processes.

Reactome (http://www.reactome.org) is an expert-authored, peer-reviewed knowledgebase of human reactions and pathways that functions as a data mining resource and electronic textbook. Its current release includes 2975 human proteins, 2907 reactions and 4455 literature citations. A new entity-level pathway viewer and improved search and data mining tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. Reactome has increased its utility to the model organism communities with improved orthology prediction methods allowing pathway inference for 22 species and through collaborations to create manually curated Reactome pathway datasets for species including Arabidopsis, Oryza sativa (rice), Drosophila and Gallus gallus (chicken). Reactome's data content and software can all be freely used and redistributed under open source terms.

Knowledge Representation for Multi-Scale Physiology Route Modeling.

We present a framework for the topological and semantic assembly of multiscale physiology route maps. The framework, called ApiNATOMY, consists of a knowledge representation (KR) model and a set of knowledge management (KM) tools. Using examples of ApiNATOMY route maps, we present a KR format that is suitable for the analysis and visualization by KM tools. The conceptual KR model provides a simple method for physiology experts to capture process interactions among anatomical entities. In this paper, we outline the KR model, modeling format, and the KM procedures to translate concise abstraction-based specifications into fully instantiated models of physiology processes.

The SPARC DRC: Building a Resource for the Autonomic Nervous System Community.

The Data and Resource Center (DRC) of the NIH-funded SPARC program is developing databases, connectivity maps, and simulation tools for the mammalian autonomic nervous system. The experimental data and mathematical models supplied to the DRC by the SPARC consortium are curated, annotated and semantically linked via a single knowledgebase. A data portal has been developed that allows discovery of data and models both via semantic search and via an interface that includes Google Map-like 2D flatmaps for displaying connectivity, and 3D anatomical organ scaffolds that provide a common coordinate framework for cross-species comparisons. We discuss examples that illustrate the data pipeline, which includes data upload, curation, segmentation (for image data), registration against the flatmaps and scaffolds, and finally display via the web portal, including the link to freely available online computational facilities that will enable neuromodulation hypotheses to be investigated by the autonomic neuroscience community and device manufacturers.

Comparative genomics indicates the mammalian CD33rSiglec locus evolved by an ancient large-scale inverse duplication and suggests all Siglecs share a common ancestral region.

The CD33-related sialic acid binding Ig-like lectins (CD33rSiglecs) are predominantly inhibitory receptors expressed on leukocytes. They are distinguishable from conserved Siglecs, such as Sialoadhesin and MAG, by their rapid evolution. A comparison of the CD33rSiglec gene cluster in different mammalian species showed that it can be divided into subclusters, A and B. The two subclusters, inverted in relation to each other, each encode a set of CD33rSiglec genes arranged head-to-tail. Two regions of strong correspondence provided evidence for a large-scale inverse duplication, encompassing the framework CEACAM-18 (CE18) and ATPBD3 (ATB3) genes that seeded the mammalian CD33rSiglec cluster. Phylogenetic analysis was consistent with the predicted inversion. Rodents appear to have undergone wholesale loss of CD33rSiglec genes after the inverse duplication. In contrast, CD33rSiglecs expanded in primates and many are now pseudogenes with features consistent with activating receptors. In contrast to mammals, the fish CD33rSiglecs clusters show no evidence of an inverse duplication. They display greater variation in cluster size and structure than mammals. The close arrangement of other Siglecs and CD33rSiglecs in fish is consistent with a common ancestral region for Siglecs. Expansion of mammalian CD33rSiglecs appears to have followed a large inverse duplication of a smaller primordial cluster over 180 million years ago, prior to eutherian/marsupial divergence. Inverse duplications in general could potentially have a stabilizing effect in maintaining the size and structure of large gene clusters, facilitating the rapid evolution of immune gene families.

Automated PDF highlighting to support faster curation of literature for Parkinson's and Alzheimer's disease.

Neurodegenerative disorders such as Parkinson's and Alzheimer's disease are devastating and costly illnesses, a source of major global burden. In order to provide successful interventions for patients and reduce costs, both causes and pathological processes need to be understood. The ApiNATOMY project aims to contribute to our understanding of neurodegenerative disorders by manually curating and abstracting data from the vast body of literature amassed on these illnesses. As curation is labour-intensive, we aimed to speed up the process by automatically highlighting those parts of the PDF document of primary importance to the curator. Using techniques similar to those of summarisation, we developed an algorithm that relies on linguistic, semantic and spatial features. Employing this algorithm on a test set manually corrected for tool imprecision, we achieved a macro F 1 -measure of 0.51, which is an increase of 132% compared to the best bag-of-words baseline model. A user based evaluation was also conducted to assess the usefulness of the methodology on 40 unseen publications, which reveals that in 85% of cases all highlighted sentences are relevant to the curation task and in about 65% of the cases, the highlights are sufficient to support the knowledge curation task without needing to consult the full text. In conclusion, we believe that these are promising results for a step in automating the recognition of curation-relevant sentences. Refining our approach to pre-digest papers will lead to faster processing and cost reduction in the curation process. Database URL: https://github.com/KHP-Informatics/NapEasy.

Exegesis: a procedure to improve gene predictions and its use to find immunoglobulin superfamily proteins in the human and mouse genomes.

Exegesis is a procedure to refine the gene predictions that are produced for complex genomes, e.g. those of humans and mice. It uses the program Genewise, sequences determined by experiment, experimental maps of gene segment libraries and a new browser that allows the user to rapidly inspect and compare multiple gene maps to regions of genomic sequences. The procedure should be of general use. Here, we use the procedure to find members of the immunoglobulin superfamily in the human and mouse genomes. To do this, Exegesis was used to process the original gene predictions from the automated Ensembl annotation pipeline. Exegesis produced (i) many more complete genes and new transcripts and (ii) a mapping of the immunoglobulin and T cell receptor gene libraries to the genome, which are largely absent in the Ensembl set.