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      Twisting the theory on the origin of human umbilical cord coiling featuring monozygotic twins

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          Abstract

          This study provides a novel framework to understand the molecular origins of human umbilical cord helices using state-of-the-art imaging and omics approaches.

          Abstract

          The human umbilical cord (hUC) is the lifeline that connects the fetus to the mother. Hypercoiling of the hUC is associated with pre- and perinatal morbidity and mortality. We investigated the origin of hUC hypercoiling using state-of-the-art imaging and omics approaches. Macroscopic inspection of the hUC revealed the helices to originate from the arteries rather than other components of the hUC. Digital reconstruction of the hUC arteries showed the dynamic alignment of two layers of muscle fibers in the tunica media aligning in opposing directions. We observed that genetically identical twins can be discordant for hUC coiling, excluding genetic, many environmental, and parental origins of hUC coiling. Comparing the transcriptomic and DNA methylation profile of the hUC arteries of four twin pairs with discordant cord coiling, we detected 28 differentially expressed genes, but no differentially methylated CpGs. These genes play a role in vascular development, cell–cell interaction, and axis formation and may account for the increased number of hUC helices. When combined, our results provide a novel framework to understand the origin of hUC helices in fetal development.

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          The Molecular Signatures Database (MSigDB) hallmark gene set collection.

          The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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            The Gene Ontology resource: enriching a GOld mine

            Abstract The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.
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              The ENCODE Blacklist: Identification of Problematic Regions of the Genome

              Functional genomics assays based on high-throughput sequencing greatly expand our ability to understand the genome. Here, we define the ENCODE blacklist- a comprehensive set of regions in the human, mouse, worm, and fly genomes that have anomalous, unstructured, or high signal in next-generation sequencing experiments independent of cell line or experiment. The removal of the ENCODE blacklist is an essential quality measure when analyzing functional genomics data.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing—original draftRole: Writing—review and editing
                Role: Formal analysis
                Role: Data curation
                Role: Formal analysis
                Role: Data curation
                Role: Data curation
                Role: Formal analysis
                Role: Data curation
                Role: ConceptualizationRole: Writing—review and editing
                Role: ConceptualizationRole: Writing—review and editing
                Role: Data curation
                Role: ConceptualizationRole: Writing—review and editing
                Role: ConceptualizationRole: Data curation
                Role: ConceptualizationRole: Funding acquisitionRole: Writing—review and editing
                Role: ConceptualizationRole: Data curationRole: Writing—original draftRole: Writing—review and editing
                Journal
                Life Sci Alliance
                Life Sci Alliance
                lsa
                lsa
                Life Science Alliance
                Life Science Alliance LLC
                2575-1077
                3 June 2024
                August 2024
                3 June 2024
                : 7
                : 8
                : e202302543
                Affiliations
                [1 ] Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center ( https://ror.org/05xvt9f17) , Leiden, Netherlands;
                [2 ] Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center ( https://ror.org/05xvt9f17) , Leiden, Netherlands;
                [3 ] Department of Pathology, Leiden University Medical Center ( https://ror.org/05xvt9f17) , Leiden, Netherlands;
                [4 ] Department of Cell and Chemical Biology, Leiden University Medical Center ( https://ror.org/05xvt9f17) , Leiden, Netherlands;
                [5 ] Neonatology, Willem-Alexander Children’s Hospital, Department of Pediatrics, Leiden University Medical Center ( https://ror.org/05xvt9f17) , Leiden, Netherlands;
                [6 ] Pediatric Cardiology, Willem-Alexander Children’s Hospital, Department of Pediatrics, Leiden University Medical Center ( https://ror.org/05xvt9f17) , Leiden, Netherlands;
                [7 ] Department of Obstetrics, Division of Fetal Therapy, Leiden University Medical Center ( https://ror.org/05xvt9f17) , Leiden, Netherlands;
                [8 ] NecstGen, Leiden, Netherlands;
                [9 ] Department of Internal Medicine, Leiden University Medical Center ( https://ror.org/05xvt9f17) , Leiden, Netherlands;
                [10 ] Department of Pathology, Erasmus Medical Center, Rotterdam, Netherlands;
                Author notes
                Author information
                https://orcid.org/0000-0002-0865-5883
                https://orcid.org/0000-0001-5373-6765
                https://orcid.org/0000-0001-9794-067X
                https://orcid.org/0009-0005-8715-4671
                https://orcid.org/0000-0002-8832-045X
                https://orcid.org/0000-0002-0153-5934
                https://orcid.org/0000-0002-0443-1997
                https://orcid.org/0000-0001-5918-0534
                https://orcid.org/0000-0002-4537-5645
                Article
                LSA-2023-02543
                10.26508/lsa.202302543
                11147950
                38830769
                207c5da7-2d29-4d4e-8f65-18663a0c16be
                © 2024 Todtenhaupt et al.

                This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/).

                History
                : 19 December 2023
                : 21 May 2024
                : 22 May 2024
                Funding
                Funded by: Hartstichting (Heart Foundation), DOI http://dx.doi.org/10.13039/501100002996;
                Award ID: 2017T075
                Award Recipient :
                Funded by: Merck | Merck Healthcare KGaA (Merck Healthcare), DOI http://dx.doi.org/10.13039/100020956;
                Award ID: NaN
                Award Recipient :
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