A transgenic zebrafish model expressing KIT-D816V recapitulates features of aggressive systemic mastocytosis

Summary: Systemic mastocytosis (SM) is a rare myeloproliferative disease without curative therapy. Despite clinical variability, the majority of patients harbour a KIT-D816V mutation, but efforts to inhibit mutant KIT with tyrosine kinase inhibitors have been unsatisfactory, indicating a need for new preclinical approaches to identify alternative targets and novel therapies in this disease. Murine models to date have been limited and do not fully recapitulate the most aggressive forms of SM. We describe the generation of a transgenic zebrafish model expressing the human KIT-D816V mutation. Adult fish demonstrate a myeloproliferative disease phenotype, including features of aggressive SM in haematopoeitic tissues and high expression levels of endopeptidases, consistent with SM patients. Transgenic embryos demonstrate a cell-cycle phenotype with corresponding expression changes in genes associated with DNA maintenance and repair, such as reduced dnmt1. In addition, epcam was consistently downregulated in both transgenic adults and embryos. Decreased embryonic epcam expression was associated with reduced neuromast numbers, providing a robust in vivo phenotypic readout for chemical screening in KIT-D816V-induced disease. This study represents the first zebrafish model of a mast cell disease with an aggressive adult phenotype and embryonic markers that could be exploited to screen for novel agents in SM

Relationship of age, gender, race, and body size to infrarenal aortic diameter

AbstractPurpose: To assess the effects of age, gender, race, and body size on infrarenal aortic diameter (IAD) and to determine expected values for IAD on the basis of these factors.Methods: Veterans aged 50 to 79 years at 15 Department of Veterans Affairs medical centers were invited to undergo ultrasound measurement of IAD and complete a prescreening questionnaire. We report here on 69,905 subjects who had no previous history of abdominal aortic aneurysm (AAA) and no ultrasound evidence of AAA (defined as IAD ≥ 3.0 cm).Results: Although age, gender, black race, height, weight, body mass index, and body surface area were associated with IAD by multivariate linear regression (all p < 0.001), the effects were small. Female sex was associated with a 0.14 cm reduction in IAD and black race with a 0.01 cm increase in IAD. A 0.1 cm change in IAD was associated with large changes in the independent variables: 29 years in age, 19 cm or 40 cm in height, 35 kg in weight, 11 kg/m2 in body mass index, and 0.35 m2 in body surface area. Nearly all height-weight groups were within 0.1 cm of the gender means, and the unadjusted gender means differed by only 0.23 cm. The variation among medical centers had more influence on IAD than did the combination of age, gender, race, and body size.Conclusions: Age, gender, race, and body size have statistically significant but small effects on IAD. Use of these parameters to define AAA may not offer sufficient advantage over simpler definitions (such as an IAD ≥3.0 cm) to be warranted. (J Vasc Surg 1997;26:595-601.

A Simple Standard for Sharing Ontological Mappings (SSSOM).

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

Development and Experimental Validation of a 20K Atlantic Cod (Gadus morhua) Oligonucleotide Microarray Based on a Collection of over 150,000 ESTs

The collapse of Atlantic cod (Gadus morhua) wild populations strongly impacted the Atlantic cod fishery and led to the development of cod aquaculture. In order to improve aquaculture and broodstock quality, we need to gain knowledge of genes and pathways involved in Atlantic cod responses to pathogens and other stressors. The Atlantic Cod Genomics and Broodstock Development Project has generated over 150,000 expressed sequence tags from 42 cDNA libraries representing various tissues, developmental stages, and stimuli. We used this resource to develop an Atlantic cod oligonucleotide microarray containing 20,000 unique probes. Selection of sequences from the full range of cDNA libraries enables application of the microarray for a broad spectrum of Atlantic cod functional genomics studies. We included sequences that were highly abundant in suppression subtractive hybridization (SSH) libraries, which were enriched for transcripts responsive to pathogens or other stressors. These sequences represent genes that potentially play an important role in stress and/or immune responses, making the microarray particularly useful for studies of Atlantic cod gene expression responses to immune stimuli and other stressors. To demonstrate its value, we used the microarray to analyze the Atlantic cod spleen response to stimulation with formalin-killed, atypical Aeromonas salmonicida, resulting in a gene expression profile that indicates a strong innate immune response. These results were further validated by quantitative PCR analysis and comparison to results from previous analysis of an SSH library. This study shows that the Atlantic cod 20K oligonucleotide microarray is a valuable new tool for Atlantic cod functional genomics research

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch\u27s APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch\u27s data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch\u27s analytic tools by developing a customized plugin for OpenAI\u27s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

New filovirus disease classification and nomenclature.

The recent large outbreak of Ebola virus disease (EVD) in Western Africa resulted in greatly increased accumulation of human genotypic, phenotypic and clinical data, and improved our understanding of the spectrum of clinical manifestations. As a result, the WHO disease classification of EVD underwent major revision

NMR metabolic analysis of samples using fuzzy K-means clustering

The global analysis of metabolites can be used to define the phenotypes of cells, tissues or organisms. Classifying groups of samples based on their metabolic profile is one of the main topics of metabolomics research. Crisp clustering methods assign each feature to one cluster, thereby omitting information about the multiplicity of sample subtypes. Here, we present the applicationof fuzzy K-means clusteringmethod for the classificationof samples basedonmetabolomics 1D1HNMRfingerprints. The sample classification was performed on NMR spectra of cancer cell line extracts and of urine samples of type 2 diabetes patients and animalmodels. The cell line dataset includedNMRspectra of lipophilic cell extracts for twonormal and three cancer cell lines with cancer cell lines including two invasive and one non-invasive cancers. The second dataset included previously published NMR spectra of urine samples of human type 2 diabetics and healthy controls, mouse wild type and diabetes model and rat obese and lean phenotypes. The fuzzy K-means clusteringmethod allowedmore accurate sample classification in both datasets relative to the other tested methods including principal component analysis (PCA), hierarchical clustering (HCL) and K-means clustering. In the cell line samples, fuzzy clustering provided a clear separation of individual cell lines, groups of cancer and normal cell lines as well as non-invasive and invasive tumour cell lines. In the diabetes dataset, clear separation of healthy controls and diabetics in all three models was possible only by using the fuzzy clusteringmethod.Peer reviewed: YesNRC publication: Ye

A Conserved Target Site in HIV-1 Gag RNA is Accessible to Inhibition by Both an HDV Ribozyme and a Short Hairpin RNA

Antisense-based molecules targeting HIV-1 RNA have the potential to be used as part of gene or drug therapy to treat HIV-1 infection. In this study, HIV-1 RNA was screened to identify more conserved and accessible target sites for ribozymes based on the hepatitis delta virus motif. Using a quantitative screen for effects on HIV-1 production, we identified a ribozyme targeting a highly conserved site in the Gag coding sequence with improved inhibitory potential compared to our previously described candidates targeting the overlapping Tat/Rev coding sequence. We also demonstrate that this target site is highly accessible to short hairpin directed RNA interference, suggesting that it may be available for the binding of antisense RNAs with different modes of action. We provide evidence that this target site is structurally conserved in diverse viral strains and that it is sufficiently different from the human transcriptome to limit off-target effects from antisense therapies. We also show that the modified hepatitis delta virus ribozyme is more sensitive to a mismatch in its target site compared to the short hairpin RNA. Overall, our results validate the potential of a new target site in HIV-1 RNA to be used for the development of antisense therapies

Proteome profiling of extracellular vesicles captured with the affinity peptide Vn96: comparison of Laemmli and TRIzol© protein-extraction methods

Sample amount is often a limiting factor for multi-parametric analyses that encompass at least three areas of ‘-omics’ research: genomics, transcriptomics and proteomics. Limited sample amounts are also an important consideration when these multi-parametric analyses are performed on extracellular vesicles (EVs), as the amount of EVs (and EV cargo) that can be isolated is often very low. It is well understood that a monophasic solution of phenol and guanidine isothiocyanate (i.e. TRIzol©) can simultaneously isolate DNA, RNA and proteins from biological samples; however, it is most commonly used for the extraction of RNA. Validation of this reagent for the isolation of multiple classes of biological molecules from EVs would provide a widely applicable method for performing multi-parametric analyses of EV material. In this report, we describe a comparison of proteins identified from EVs processed with either TRIzol© or the conventional Laemmli buffer protein-extraction reagents. EVs were isolated from 3 mL of cell-culture supernatant derived from MCF-10A, MCF-7 and MDA-MB-231 cells using the Vn96 EV capture technology. For the TRIzol© extraction protocol, proteins were precipitated with acetone from the organic phase and then re-solubilized in a mixture of 8M urea, 0.2% SDS and 1 M Tris-HCl pH 6.8, followed by dilution in 5× loading buffer prior to fractionation with 1D SDS-PAGE. NanoLC-MS/MS of the trypsin-digested proteins was used to generate proteomic profiles from EV protein samples extracted with each method. Of the identified proteins, 57.7%, 69.2% and 57.0% were common to both extraction methods for EVs from MCF-10A, MCF-7 and MDA-MB-231, respectively. Our results suggest that TRIzol© extraction of proteins from EVs has significant equivalence to the traditional Laemmli method. The advantage of using TRIzol© reagent is the ability to accumulate multi-parametric data (e.g., RNA and protein profiles) on the same limited EV sample while minimizing sample preparation and processing time