Single cell multiomics guided mechanistic understanding of Fontan-associated liver disease
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE223843 | GEO | 2024/01/24
REPOSITORIES: GEO
Similar Datasets
Project description:Single cell multiomics guided mechanistic understanding of Fontan-associated liver disease
| PRJNA928662 | ENA
Project description:There are no curative treatment options for patients with single ventricle disease. Rather, the standard of care includes a series of palliative procedures that culminate in the Fontan operation, which, while life-saving, nevertheless reduces longevity and quality of life. The cause of late term death in these patients is typically ascribed to insidious end organ sequelae associated with the Fontan circulation. The etiology underlying these pathological processes remains unknown, but is thought to be diverse and multifactorial, arising from progressive deterioration of end-organ function due to the non-physiologic Fontan hemodynamics and its consequences on tissue perfusion and remodeling.Our overall goal is to evaluate the impact of Fontan physiology on cardiopulmonary remodeling over long periods in an ovine Fontan survival model.
2022-12-30 | GSE201288 | GEO
Project description:Protein losing enteropathy (PLE) is a severe complication of Fontan circulation with increased morbidity and mortality. The underlying pathophysiologic mechanism leading to the development of PLE in Fontan patients remain largely unknown. We performed microarray-based microRNA (miRNA) expression profiling from whole blood to generate hypotheses about relevant pathways involved in PLE pathogenesis controlled by miRNA signature.
2020-05-01 | GSE130647 | GEO
Project description:Single-ventricle defects are palliatively corrected using the Glenn and Fontan procedures that provide lifesaving relief but lead to early mortality rates and poor prognosis post-operatively. The pathophysiology of these complications is diverse, and there are no clear predictors for these outcomes. As such, we aimed to identify novel biomarkers for single-ventricle associated outcomes through the characterization of the serum transcriptome and small extracellular vesicles (sEVs). We report on molecular responses in serum and sEVs pre-operatively and post-operatively in patients undergoing either Glenn or Fontan operations. We found that transcriptomes of serum RNA and sEV RNA are distinctly different, whereas differences between the Fontan and Glenn transcriptomes did not show any significant changes. An in vitro wound healing assay further confirmed that post-operative sEVs have higher angiogenic potential. Moreover, we used a partial-least square regression model to identify 100 RNAs and 50 microRNAs that are involved in angiogenesis-related pathways and accurately predict our wound healing results and compared them with our previously published models of ovine Fontan surgery. Lastly, an EV-origin analysis showed decreased secretion of sEV from cardiac tissue in post-operative samples and increased secretion of sEVs from stomach and nervous tissue. Overall, this study provided evidence that the circulating sEVs transcriptome are potent biomarkers and demonstrated the role of sEVs as a possible mediator for formation of veno-venous collaterals.
2024-12-20 | GSE269879 | GEO
Project description:Background: Fontan-associated liver disease (FALD) is a common sequelae of single-ventricle patients palliated with the Fontan operation. FALD severity can impact clinical decisions; however, the pathophysiology of FALD progression is unknown. Methods: Single-cell spatial transcriptomics (ST) was performed on liver explant tissue sections from FALD patients with early and advanced fibrosis using CosMxTM Spatial Molecular Imaging with in-situ hybridization of 6000 genes (n=2). Immunofluorescence for hepatic zonation and cellular stress markers was used to confirm protein expression based on ST analysis in biopsy and explant FALD tissues (n=17). Results: Unbiased clustering yielded 12 liver cell types, comprising five subtypes of hepatocytes. FALD with advanced fibrosis demonstrated the expansion of mid-zonal hepatocytes, accompanied by the loss of metabolic markers associated with canonical pericentral and periportal hepatocytes. Advanced FALD hepatocytes uniquely demonstrated increased cellular stress and a redundant metabolic phenotype. Advanced FALD hepatocytes changed into a senescent-like state, which we have termed hepatocyte metabolic exhaustion. Protein expression analysis confirmed metabolic exhaustion within hepatocytes and upregulation of cellular stress markers in advanced FALD tissue specimens. Immunofluorescence staining of FALD samples demonstrated a disruption of zonation and a significant increase in heat shock protein 70 (HSP70). HSP70 expression strongly correlated with the congestive hepatic fibrosis (CHF) score. Conclusions: Single-cell ST has identified a population of hepatocytes with features of metabolic exhaustion and cellular stress unique to advanced FALD.
2026-02-04 | GSE310443 | GEO
Project description:The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple facets. However, integrating these large-scale multiomics data and discovering functional insights are, nevertheless, challenging tasks. To address these challenges, machine learning has been broadly applied to analyze multiomics. This review introduces multiview learning-an emerging machine learning field-and envisions its potentially powerful applications to multiomics. In particular, multiview learning is more effective than previous integrative methods for learning data's heterogeneity and revealing cross-talk patterns. Although it has been applied to various contexts, such as computer vision and speech recognition, multiview learning has not yet been widely applied to biological data-specifically, multiomics data. Therefore, this paper firstly reviews recent multiview learning methods and unifies them in a framework called multiview empirical risk minimization (MV-ERM). We further discuss the potential applications of each method to multiomics, including genomics, transcriptomics, and epigenomics, in an aim to discover the functional and mechanistic interpretations across omics. Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits and caveats of using multiview learning to discover the molecular mechanisms and functions of these systems.
| S-EPMC7117667 | biostudies-literature
Project description:<p>BACKGROUND: Single ventricle congenital heart disease (SVCHD) is a severe form of cardiac malformation in which there is only one functional ventricle. The Fontan operation is the current standard of care for SVCHD. Almost all patients who have undergone the Fontan operation develop liver fibrosis at a young age, resulting in a condition known as Fontan-associated liver disease (FALD). The pathogenesis and mechanisms underlying FALD remain little understood, hindering development of effective therapies.</p><p>OBJECTIVES: We aimed to present a comprehensive multiomic analysis of human FALD, thereby revealing the fundamental biology and pathogenesis of FALD. </p><p>METHODS: We recently generated a single-cell transcriptomic and epigenomic atlas of human FALD using snRNA-ATAC-seq, which revealed profound metabolic reprogramming in FALD. Here we applied liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics to unveil the metabolomic landscape of human FALD, using liver samples/biopsies from age and gender-matched healthy donors and FALD patients (n=12 per group). Extracted liver metabolites were analyzed by C18 high performance liquid chromatography (HPLC) and hydrophilic interaction chromatography (HILIC) followed by high resolution MS on Orbitrap. Statistical and bioinformatics analyses were performed to identify altered metabolites and metabolic pathways in FALD. These results were integrated with recently published snRNA-ATAC-seq and serum metabolomics datasets to present a comprehensive multiomic atlas of FALD. </p><p>RESULTS: We discovered profound metabolic abnormalities in livers of patients with early-stage FALD, particularly amino acid metabolism, peroxisomal fatty acid oxidation, cytochrome P450 system, glycolysis, TCA cycle, ketone body metabolism and bile acids metabolism. Integrated analyses with liver snRNA-ATAC-seq and serum metabolomics data unveiled the transcriptional mechanisms driving this metabolic reprogramming and the crosstalk between liver and the rest of the body. Comparison with human metabolic dysfunction-associated fatty liver disease (MAFLD) and metabolic dysfunction-associated steatohepatitis (MASH) revealed dysregulated amino acid metabolism as a common metabolic abnormality. </p><p>CONCLUSIONS: Our comprehensive multiomic atlas of human FALD reveals the fundamental biology and pathogenesis mechanisms of FALD.</p>
2024-12-13 | MTBLS10782 | MetaboLights
Project description:Single-cell Spatial Transcriptomics Reveals Hepatocyte Metabolic Reprogramming in Fontan Associated Liver Disease
| PRJNA1366348 | ENA
Project description:We utilize a finite element model of implantation mechanics to guide the in vitro micropatterning human embryonic stem cells to simulate in vivo stress gradients that form during development. Micropatterned stem cells showed early formation of GATA4+/SOX17+ primitive endoderm due to migration toward regions of high stress, while NANOG+/OCT4+ epiblast was retained in regions of low stress under spontaneous differentiation.
2018-11-29 | GSE123074 | GEO
Project description:Schlafen 11 (SLFN11) is an interferon-inducible antiviral restriction factor with tRNA endoribonuclease and DNA binding functions. It is recruited to stalled replication forks in response to replication stress and inhibits replication of certain viruses such as the human immunodeficiency virus 1 (HIV-1) by modulating the tRNA pool. SLFN11 has been identified as a predictive biomarker in cancer, as its expression correlates with a beneficial response to DNA damage inducing anticancer drugs. However, the mechanism and interdependence of these two functions are largely unknown. Here, we present cryo-electron microscopy (cryo-EM) structures of human SLFN11 in its dimeric apoenzyme state, bound to tRNA and in complex with single-strand DNA. Full-length SLFN11 neither hydrolyses nor binds ATP and the helicase domain appears in an autoinhibited state. Together with biochemical and structure guided mutagenesis studies, our data give detailed insights into the mechanism of endoribonuclease activity as well as suggestions on how SLFN11 may block stressed replication forks.
| S-EPMC9482658 | biostudies-literature