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Project description:Background. Patients with congenital heart disease are identified in 1% of live births, and with improved surgical intervention many patients now survive to adulthood, the corollary of which is increased mortality in the over-65-year-old CHD population. In the clinic, genetic sequencing increasingly identifies novel genetic variants in genes related to congenital heart disease (CHD). Traditional assays for interpreting novel genetic variants are often limited by gene-specificity, whereas animal models are cumbersome and may not accurately reflect human disease. This study investigates CRISPR gene editing in induced pluripotent stem cells and cardiomyocyte-directed differentiation as a human disease model to investigate novel genetic variants identified in association with CHD. Methods and Results. The GATA4 p.Arg284His genetic variant was introduced into iPSCs using high efficiency CRISPR gene editing with homology-directed repair. GATA4 genetic variant and isogenic control cell pairs were selected and differentiated into cardiomyocytes. Expression of the GATA4 p.Arg284His variant resulted in altered calcium transient’s indicative of CHD and consistent with the patient’s clinical phenotype. Transcriptomics revealed molecular and cellular pathway changes in cardiac development, calcium handling, and energy metabolism that contribute to disease aetiology and mechanism, and identified potential treatments that could restore normal cell function. Conclusion. Directed differentiation of iPSCs harbouring the GATA4 p.Arg284His genetic variant recapitulated the CHD phenotype, indicated disease mechanisms, and pointed to potential treatments. The study highlights the utility of transcriptomics for the functional interpretation of cardiac genetic variants and is an exemplar for precision medicine approaches for the investigation of CHD.
2026-02-11 | GSE305687 | GEO
Project description:Background. Patients with congenital heart disease are identified in 1% of live births, and with improved surgical intervention many patients now survive to adulthood, the corollary of which is increased mortality in the over-65-year-old CHD population. In the clinic, genetic sequencing increasingly identifies novel genetic variants in genes related to congenital heart disease (CHD). Traditional assays for interpreting novel genetic variants are often limited by gene-specificity, whereas animal models are cumbersome and may not accurately reflect human disease. This study investigates CRISPR gene editing in induced pluripotent stem cells and cardiomyocyte-directed differentiation as a human disease model to investigate novel genetic variants identified in association with CHD. Methods and Results. The GATA4 p.Arg284His genetic variant was introduced into iPSCs using high efficiency CRISPR gene editing with homology-directed repair. GATA4 genetic variant and isogenic control cell pairs were selected and differentiated into cardiomyocytes. Expression of the GATA4 p.Arg284His variant resulted in altered calcium transient’s indicative of CHD and consistent with the patient’s clinical phenotype. Transcriptomics revealed molecular and cellular pathway changes in cardiac development, calcium handling, and energy metabolism that contribute to disease aetiology and mechanism, and identified potential treatments that could restore normal cell function. Conclusion. Directed differentiation of iPSCs harbouring the GATA4 p.Arg284His genetic variant recapitulated the CHD phenotype, indicated disease mechanisms, and pointed to potential treatments. The study highlights the utility of transcriptomics for the functional interpretation of cardiac genetic variants and is an exemplar for precision medicine approaches for the investigation of CHD.
2026-02-11 | GSE305730 | GEO
Project description:A precision medicine approach to interpret a GATA4 genetic variant in a paediatric patient with congenital heart disease
| PRJNA1310636 | ENA
Project description:A precision medicine approach to interpret a GATA4 genetic variant in a paediatric patient with congenital heart disease [AmpSeq]
| PRJNA1307469 | ENA
Project description:A precision medicine approach to interpret a GATA4 genetic variant in a paediatric patient with congenital heart disease [RNA-seq]
| PRJNA1307511 | ENA
Project description:Precision medicine, defined as tailoring medical care individually based upon relevant factors, is primarily implemented currently through the use of genetic variation. Over the past thirty years, the possibility of determining specific genetic variants underlying congenital heart disease has increased dramatically. This has created the potential for using precision genetic approaches to improve care and outcomes for patients and families with congenital heart disease. In this review, recent advances in understanding the roles of genetic variants in various outcomes, in developing novel therapeutic approaches, and in refining clinical trials for congenital heart disease are discussed.
| S-EPMC9729438 | biostudies-literature
Project description:Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that modulate cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches in a concurrent, adjuvant, or recurrent setting.
2022-07-01 | GSE189650 | GEO
Project description:More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one-variant one-phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein-protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the "wiring" of the interactome to better capture heterogeneity in genotype-phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine-learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype-phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods.
| S-EPMC6450710 | biostudies-literature
Project description:Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single-cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that modulate cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches to an individual patient in a concurrent, adjuvant, or recurrent setting.
| S-EPMC9360428 | biostudies-literature
Project description:Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.
| S-EPMC9237320 | biostudies-literature