9,996 research outputs found

    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    Biomarkers in acute ischemic stroke

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    The Type 2 Diabetes Knowledge Portal: an open access genetic resource dedicated to type 2 diabetes and related traits

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    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's 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

    Return of the Tbx5; lineage-tracing reveals ventricular cardiomyocyte-like precursors in the injured adult mammalian heart

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    The single curative measure for heart failure patients is a heart transplantation, which is limited due to a shortage of donors, the need for immunosuppression and economic costs. Therefore, there is an urgent unmet need for identifying cell populations capable of cardiac regeneration that we will be able to trace and monitor. Injury to the adult mammalian cardiac muscle, often leads to a heart attack through the irreversible loss of a large number of cardiomyocytes, due to an idle regenerative capability. Recent reports in zebrafish indicate that Tbx5a is a vital transcription factor for cardiomyocyte regeneration. Preclinical data underscore the cardioprotective role of Tbx5 upon heart failure. Data from our earlier murine developmental studies have identified a prominent unipotent Tbx5-expressing embryonic cardiac precursor cell population able to form cardiomyocytes, in vivo, in vitro and ex vivo. Using a developmental approach to an adult heart injury model and by employing a lineage-tracing mouse model as well as the use of single-cell RNA-seq technology, we identify a Tbx5-expressing ventricular cardiomyocyte-like precursor population, in the injured adult mammalian heart. The transcriptional profile of that precursor cell population is closer to that of neonatal than embryonic cardiomyocyte precursors. Tbx5, a cardinal cardiac development transcription factor, lies in the center of a ventricular adult precursor cell population, which seems to be affected by neurohormonal spatiotemporal cues. The identification of a Tbx5-specific cardiomyocyte precursor-like cell population, which is capable of dedifferentiating and potentially deploying a cardiomyocyte regenerative program, provides a clear target cell population for translationally-relevant heart interventional studies

    Distinct genomic routes underlie transitions to specialised symbiotic lifestyles in deep-sea annelid worms.

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    Bacterial symbioses allow annelids to colonise extreme ecological niches, such as hydrothermal vents and whale falls. Yet, the genetic principles sustaining these symbioses remain unclear. Here, we show that different genomic adaptations underpin the symbioses of phylogenetically related annelids with distinct nutritional strategies. Genome compaction and extensive gene losses distinguish the heterotrophic symbiosis of the bone-eating worm Osedax frankpressi from the chemoautotrophic symbiosis of deep-sea Vestimentifera. Osedax's endosymbionts complement many of the host's metabolic deficiencies, including the loss of pathways to recycle nitrogen and synthesise some amino acids. Osedax's endosymbionts possess the glyoxylate cycle, which could allow more efficient catabolism of bone-derived nutrients and the production of carbohydrates from fatty acids. Unlike in most Vestimentifera, innate immunity genes are reduced in O. frankpressi, which, however, has an expansion of matrix metalloproteases to digest collagen. Our study supports that distinct nutritional interactions influence host genome evolution differently in highly specialised symbioses

    Investigating neural differentiation capacity in Alzheimer’s disease iPSC-derived neural stem cells

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    Neurodegeneration in Alzheimer’s disease (AD) may be exacerbated by dysregulated hippocampal neurogenesis. Neural stem cells (NSC) maintain adult neurogenesis and depletion of the NSC niche has been associated with age-related cognitive decline and dementia. We hypothesise that familial AD (FAD) mutations bias NSC toward premature neural specification, reducing the stem cell niche over time and accelerating disease progression. Somatic cells derived from patients with FAD (PSEN1 A246E and PSEN1 M146L heterozygous mutations) and healthy controls were reprogrammed to generate induced pluripotent stem cells (iPSC). Pluripotency for patient and control iPSC lines was confirmed, then cells were amplified and cryopreserved as stores. iPSC were subjected to neural specification to rosette-forming SOX2+/nestin+ NSCs for comparative evaluations between FAD and age-matched controls. FAD patient and control NSC were passaged under defined steady state culture conditions to assess stem cell maintenance using quantitative molecular markers (SOX2, nestin, NeuN, MAP2 and βIII-tubulin). We observed trends towards downregulated expression of the nestin coding gene NES (p=0.051) and upregulated expression of MAP2 (p=0.16) in PSEN1 NSC compared with control NSC, indicative of a premature differentiation phenotype induced by presence of the PSEN1 mutation. Cell cycle analysis of PSEN1 NSC showed that compared with controls, a greater number of PSEN1 NSC were retained in G0/G1 phase of the cell cycle (p=0.39), fewer progressed to S-phase (p=0.11) and fewer still reached G2 phase (p=0.23), suggesting cell cycle progression may be impaired in PSEN1 NSC. Nuclear DNA fragmentation was increased (p=0.10) in FAD NSC compared with controls, indicative of elevated cell death/apoptosis. Flow cytometry-based analysis of live, nestin+ NSC and NPC indicated increased apoptosis (p=0.14) in FAD NSC compared with controls, as well as increasing levels of apoptosis (p=0.33) in FAD NSC as they specified to neural progenitor cells. Global RNA sequencing was used to identify transcriptomic changes occurring during both disease and control neural specification. GO analysis of DEGs between PSEN1 and control NSC at P3 revealed significant upregulation (FDR<0.0000259) of 5 biological processes related to transcription and gene expression as well as significant upregulation (FDR<0.000000725) of 12 molecular functions related to DNA binding and transcription factor activity. These data suggest significant changes in gene expression were occurring in PSEN1 NSC at P3 compared with control NSC at the same stage in neural specification. The number of DEGs (p<0.05) between PSEN1 and control NSC at P3 was 9.92-fold higher than the number of DEGs between PSEN1 and control NSC at P2, suggesting transcriptomic differences between PSEN1 and control NSC become more pronounced as cells specify further down the neural lineage. Gene ontology (GO) analysis of differentially expressed genes (DEGs) specific to AD neural differentiation revealed significant dysregulation (FDR p<0.05) of genes related to neurogenesis, apoptosis, cell cycle, transcriptional control, and cell growth/maintenance as PSEN1 NSC matured from P2 to P3. The number of DEGs (p<0.05) in PSEN1 neural differentiation was 4.7-fold higher than the number of DEGs seen in control neural differentiation, indicating more transcriptional changes occurred in PSEN1 NSC than in controls at the same time point in neural specification. Dysregulation of Notch signalling was specific to PSEN1 neural differentiation and Notch related DEGs significantly upregulated (p<0.05) in PSEN1 NSC at P3 compared with P2 included NCOR2, JAG2, CHAC1 and RFNG. qPCR based validation displayed significant upregulation of RFNG (p=0.04) in PSEN1 NSC at P3 compared with PSEN1 NSC at P2, and indicated a trend towards upregulation of JAG2 expression, correlating with RNA sequencing data. Data generated in this study indicate that presence of the PSEN1 mutation significantly increases the number of transcriptional changes occurring during neural differentiation. It is plausible that transcriptional changes to Notch signalling cause dysregulated neural specification and increased apoptosis in PSEN1 NSC, ultimately resulting in depletion of the NSC niche

    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

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    Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

    Multi-dimensional omics approaches to dissect natural immune control mechanisms associated with RNA virus infections

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    In recent decades, global health has been challenged by emerging and re-emerging viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), human immunodeficiency viruses (HIV-1), and Crimean–Congo hemorrhagic fever virus (CCHFV). Studies have shown dysregulations in the host metabolic processes against SARS-CoV2 and HIV-1 infections, and the research on CCHFV infection is still in the infant stage. Hence, understanding the host metabolic re-programming on the reaction level in infectious disease has therapeutic importance. The thesis uses systems biology methods to investigate the host metabolic alterations in response to SARS-CoV2, HIV-1, and CCHFV infections. The three distinct viruses induce distinct effects on human metabolism that, nevertheless, show some commonalities. We have identified alterations in various immune cell types in patients during the infections of the three viruses. Further, differential expression analysis identified that COVID-19 causes disruptions in pathways related to antiviral response and metabolism (fructose mannose metabolism, oxidative phosphorylation (OXPHOS), and pentose phosphate pathway). Up-regulation of OXPHOS and ROS pathways with most changes in OXPHOS complexes I, III, and IV were identified in people living with HIV on treatment (PLWHART). The acute phase of CCHFV infection is found to be linked with OXPHOS, glycolysis, N-glycan biosynthesis, and NOD-like receptor signaling pathways. The dynamic nature of the metabolic process and adaptive immune response in CCHFV-pathogenesis are also observed. Further, we have identified different metabolic flux in reactions transporting TCA cycle intermediates from the cytosol to mitochondria in COVID-19 patients. Genes such as monocarboxylate transporter (SLC16A6) and nucleoside transporter (SLC29A1) and metabolites such as α-ketoglutarate, succinate, and malate were found to be linked with COVID-19 disease response. Metabolic reactions associated with amino acid, carbohydrate, and energy metabolism pathways and various transporter reactions were observed to be uniquely disrupted in PLWHART along with increased production of αketoglutarate (αKG) and ATP molecules. Changes in essential (leucine and threonine) and non-essential (arginine, alanine, and glutamine) amino acid transport were found to be caused by acute CCHFV infection. The altered flux of reactions involving TCA cycle compounds such as pyruvate, isocitrate, and alpha-ketoglutarate was also observed in CCHFV infection. The research described in the thesis displayed dysregulations in similar metabolic processes against the three viral Infections. But further downstream analysis unveiled unique alterations in several metabolic reactions specific to each virus in the same metabolic pathways showing the importance of increasing the resolution of knowledge about host metabolism in infectious diseases
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