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Learning from conect4children: A Collaborative Approach towards Standardisation of Disease-Specific Paediatric Research Data
Get PDFThe conect4children (c4c) initiative was established to facilitate the development of new drugs and other therapies for paediatric patients. It is widely recognised that there are not enough medicines tested for all relevant ages of the paediatric population. To overcome this, it is imperative that clinical data from different sources are interoperable and can be pooled for larger post hoc studies. c4c has collaborated with the Clinical Data Interchange Standards Consortium (CDISC) to develop cross-cutting data resources that build on existing CDISC standards in an effort to standardise paediatric data. The natural next step was an extension to disease-specific data items. c4c brought together several existing initiatives and resources relevant to disease-specific data and analysed their use for standardising disease-specific data in clinical trials. Several case studies that combined disease-specific data from multiple trials have demonstrated the need for disease- specific data standardisation. We identified three relevant initiatives. These include European Reference Networks, European Joint Programme on Rare Diseases, and Pistoia Alliance. Other resources reviewed were National Cancer Institute Enterprise Vocabulary Services, CDISC standards, pharmaceutical company-specific data dictionaries, Human Phenotype Ontology, Phenopackets, Unified Registry for Inherited Metabolic Disorders, Orphacodes, Rare Disease Cures Accelerator- Data and Analytics Platform (RDCA-DAP), and Observational Medical Outcomes Partnership. The collaborative partners associated with these resources were also reviewed briefly. A plan of action focussed on collaboration was generated for standardising disease-specific paediatric clinical trial data. A paediatric data standards multistakeholder and multi-project user group was established to guide the remaining actions—FAIRification of metadata, a Phenopackets pilot with RDCA-DAP, applying Orphacodes to case report forms of clinical trials, introducing CDISC standards into European Reference Networks, testing of the CDISC Pediatric User Guide using data from the mentioned resources and organisation of further workshops and educational materials
Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML.
Get PDFAcute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression
Inhibitory G proteins play multiple roles to polarize sensory hair cell morphogenesis.
Get PDFInhibitory G alpha (GNAI or Gαi) proteins are critical for the polarized morphogenesis of sensory hair cells and for hearing. The extent and nature of their actual contributions remains unclear, however, as previous studies did not investigate all GNAI proteins and included non-physiological approaches. Pertussis toxin can downregulate functionally redundant GNAI1, GNAI2, GNAI3, and GNAO proteins, but may also induce unrelated defects. Here, we directly and systematically determine the role(s) of each individual GNAI protein in mouse auditory hair cells. GNAI2 and GNAI3 are similarly polarized at the hair cell apex with their binding partner G protein signaling modulator 2 (GPSM2), whereas GNAI1 and GNAO are not detected. I
Improving prenatal diagnosis through standards and aggregation.
Get PDFAdvances in sequencing and imaging technologies enable enhanced assessment in the prenatal space, with a goal to diagnose and predict the natural history of disease, to direct targeted therapies, and to implement clinical management, including transfer of care, election of supportive care, and selection of surgical interventions. The current lack of standardization and aggregation stymies variant interpretation and gene discovery, which hinders the provision of prenatal precision medicine, leaving clinicians and patients without an accurate diagnosis. With large amounts of data generated, it is imperative to establish standards for data collection, processing, and aggregation. Aggregated and homogeneously processed genetic and phenotypic data permits dissection of the genomic architecture of prenatal presentations of disease and provides a dataset on which data analysis algorithms can be tuned to the prenatal space. Here we discuss the importance of generating aggregate data sets and how the prenatal space is driving the development of interoperable standards and phenotype-driven tools
Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.
Get PDFOBJECTIVE: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids).
MATERIALS AND METHODS: We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison.
RESULTS: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures.
DISCUSSION: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation.
CONCLUSION: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs
Gene replacement-Alzheimer\u27s disease (GR-AD): Modeling the genetics of human dementias in mice.
Get PDFINTRODUCTION: Genetic studies conducted over the past four decades have provided us with a detailed catalog of genes that play critical roles in the etiology of Alzheimer\u27s disease (AD) and related dementias (ADRDs). Despite this progress, as a field we have had only limited success in incorporating this rich complexity of human AD/ADRD genetics findings into our animal models of these diseases. Our primary goal for the gene replacement (GR)-AD project is to develop mouse lines that model the genetics of AD/ADRD as closely as possible.
METHODS: To do this, we are generating mouse lines in which the genes of interest are precisely and completely replaced in the mouse genome by their full human orthologs.
RESULTS: Each model set consists of a control line with a wild-type human allele and variant lines that precisely match the human genomic sequence in the control line except for a high-impact pathogenic mutation or risk variant
Discovery of immunotherapy targets for pediatric solid and brain tumors by exon-level expression.
Get PDFImmunotherapy with chimeric antigen receptor T cells for pediatric solid and brain tumors is constrained by available targetable antigens. Cancer-specific exons present a promising reservoir of targets; however, these have not been explored and validated systematically in a pan-cancer fashion. To identify cancer specific exon targets, here we analyze 1532 RNA-seq datasets from 16 types of pediatric solid and brain tumors for comparison with normal tissues using a newly developed workflow. We find 2933 exons in 157 genes encoding proteins of the surfaceome or matrisome with high cancer specificity either at the gene (n = 148) or the alternatively spliced isoform (n = 9) level. Expression of selected alternatively spliced targets, including the EDB domain of fibronectin 1, and gene targets, such as COL11A1, are validated in pediatric patient derived xenograft tumors. We generate T cells expressing chimeric antigen receptors specific for the EDB domain or COL11A1 and demonstrate that these have antitumor activity. The full target list, explorable via an interactive web portal ( https://cseminer.stjude.org/ ), provides a rich resource for developing immunotherapy of pediatric solid and brain tumors using gene or AS targets with high expression specificity in cancer
Strategies to dissect microglia-synaptic interactions during aging and in Alzheimer\u27s disease.
No full textAge is the largest risk factor for developing Alzheimer\u27s disease (AD), a neurodegenerative disorder that causes a progressive and severe dementia. The underlying cause of cognitive deficits seen in AD is thought to be the disconnection of neural circuits that control memory and executive functions. Insight into the mechanisms by which AD diverges from normal aging will require identifying precisely which cellular events are driven by aging and which are impacted by AD-related pathologies. Since microglia, the brain-resident macrophages, are known to have critical roles in the formation and maintenance of neural circuits through synaptic pruning, they are well-positioned to modulate synaptic connectivity in circuits sensitive to aging or AD. In this review, we provide an overview of the current state of the field and on emerging technologies being employed to elucidate microglia-synaptic interactions in aging and AD. We also discuss the importance of leveraging genetic diversity to study how these interactions are shaped across more realistic contexts. We propose that these approaches will be essential to define specific aging- and disease-relevant trajectories for more personalized therapeutics aimed at reducing the effects of age or AD pathologies on the brain. This article is part of the Special Issue on Microglia
The RNA-binding protein IGF2BP1 regulates stability of mRNA transcribed from FOXM1 target genes in hypermitotic meningiomas.
Get PDFPediatric Tumors as Disorders of Development: The Case for In Vitro Modeling Based on Human Stem Cells.
Get PDFDespite improvements in patient outcomes, pediatric cancer remains a leading cause of non-accidental death in children. Recent genetic analysis of patients with pediatric cancers indicates an important role for both germline genetic predisposition and cancer-specific somatic driver mutations. Increasingly, evidence demonstrates that the developmental timepoint at which the cancer cell-of-origin transforms is critical to tumor identity and therapeutic response. Therefore, future therapeutic development would be bolstered by the use of disease models that faithfully recapitulate the genetic context, cell-of-origin, and developmental window of vulnerability in pediatric cancers. Human stem cells have the potential to incorporate all of these characteristics into a pediatric cancer model, while serving as a platform for rapid genetic and pharmacological testing. In this review, we describe how human stem cells have been used to model pediatric cancers and how these models compare to other pediatric cancer model modalities