RESUMO
Asthma and obesity are two of the most common chronic conditions in children and adolescents. There is increasing evidence that sphingolipid metabolism is altered in childhood asthma and is linked to airway hyperreactivity. Dysregulated sphingolipid metabolism is also reported in obesity. However, the functional link between sphingolipid metabolism, asthma, and obesity is not completely understood. This paper describes the protocol of an ongoing study on sphingolipids that aims to examine the pathophysiology of sphingolipids in childhood asthma and obesity. In addition, this study aims to explore the novel biomarkers through a comprehensive multi-omics approach including genomics, genome-wide DNA methylation, RNA-Seq, microRNA (miRNA) profiling, lipidomics, metabolomics, and cytokine profiling. This is a cross-sectional study aiming to recruit 440 children from different groups: children with asthma and normal weight (n = 100), asthma with overweight or obesity (n = 100), overweight or obesity (n = 100), normal weight (n = 70), and siblings of asthmatic children with normal weight, overweight, or obesity (n = 70). These participants will be recruited from the pediatric pulmonology, pediatric endocrinology, and general pediatric outpatient clinics at Sidra Medicine, Doha, Qatar. Information will be obtained from self-reported questionnaires on asthma, quality of life, food frequency (FFQ), and a 3-day food diary that are completed by the children and their parents. Clinical measurements will include anthropometry, blood pressure, biochemistry, bioelectrical impedance, and pulmonary function tests. Blood samples will be obtained for sphingolipid analysis, serine palmitoyltransferase (SPT) assay, whole-genome sequencing (WGS), genome-wide DNA methylation study, RNA-Seq, miRNA profiling, metabolomics, lipidomics, and cytokine analysis. Group comparisons of continuous outcome variables will be carried out by a one-way analysis of variance or the Kruskal-Wallis test using an appropriate pairwise multiple comparison test. The chi-squared test or a Fisher's exact test will be used to test the associations between categorical variables. Finally, multivariate analysis will be carried out to integrate the clinical data with multi-omics data. This study will help us to understand the role of dysregulated sphingolipid metabolism in obesity and asthma. In addition, the multi-omics data from the study will help to identify novel genetic and epigenetic signatures, inflammatory markers, and mechanistic pathways that link asthma and obesity in children. Furthermore, the integration of clinical and multi-omics data will help us to uncover the potential interactions between these diseases and to offer a new paradigm for the treatment of pediatric obesity-associated asthma.
RESUMO
The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.
Assuntos
Biomarcadores Tumorais , Neoplasias do Colo , Humanos , Estudos de Coortes , Biomarcadores Tumorais/genética , Neoplasias do Colo/genética , Neoplasias do Colo/patologia , Transcriptoma , Microambiente TumoralRESUMO
Transcriptome profiling of human whole blood is used to discover biomarkers of diseases and to assess phenotypic traits. Recently, finger-stick blood collection systems have allowed a less invasive and quicker collection of peripheral blood. Such non-invasive sampling of small volumes of blood offers practical advantages. The quality of gene expression data is strictly dependent on the steps used for the sample collection, extraction, preparation and sequencing. Here we have: (i) compared the manual and automated RNA extraction of small volumes of blood using the Tempus Spin RNA isolation kit and the MagMAX for Stabilized Blood RNA Isolation kit , respectively; and (ii) assessed the effect of TURBO DNA Free treatment on the transcriptomic data of RNA isolated from small volumes of blood. We have used the QuantSeq 3' FWD mRNA-Seq Library Prep kit to prepare RNA-seq libraries, which were sequenced on the Illumina NextSeq 500 system. The samples isolated manually displayed a higher variability in the transcriptomic data as compared to the other samples. The TURBO DNA Free treatment affected the RNA samples negatively, decreasing the RNA yield and reducing the quality and reproducibility of the transcriptomic data. We conclude that automated extraction systems should be preferred over manual extraction systems for data consistency, and that the TURBO DNA Free treatment should be avoided when working on RNA samples isolated manually from small volumes of blood.
Assuntos
Gastrópodes , Perfilação da Expressão Gênica , Humanos , Animais , Reprodutibilidade dos Testes , Transcriptoma , Coleta de Amostras Sanguíneas , RNARESUMO
Variants in cardiac myosin-binding protein C (cMyBP-C) are the leading cause of inherited hypertrophic cardiomyopathy (HCM), demonstrating the key role that cMyBP-C plays in the heart's contractile machinery. To investigate the c-MYBPC3 HCM-related cardiac impairment, we generated a zebrafish mypbc3-knockout model. These knockout zebrafish displayed significant morphological heart alterations related to a significant decrease in ventricular and atrial diameters at systolic and diastolic states at the larval stages. Immunofluorescence staining revealed significant hyperplasia in the mutant's total cardiac and ventricular cardiomyocytes. Although cardiac contractility was similar to the wild-type control, the ejection fraction was significantly increased in the mypbc3 mutants. At later stages of larval development, the mutants demonstrated an early cardiac phenotype of myocardium remodeling, concurrent cardiomyocyte hyperplasia, and increased ejection fraction as critical processes in HCM initiation to counteract the increased ventricular myocardial wall stress. The examination of zebrafish adults showed a thickened ventricular cardiac wall with reduced heart rate, swimming speed, and endurance ability in both the mypbc3 heterozygous and homozygous groups. Furthermore, heart transcriptome profiling showed a significant downregulation of the actin-filament-based process, indicating an impaired actin cytoskeleton organization as the main dysregulating factor associated with the early ventricular cardiac hypertrophy in the zebrafish mypbc3 HCM model.
Assuntos
Cardiomiopatia Hipertrófica , Peixe-Zebra , Actinas/genética , Actinas/metabolismo , Animais , Miosinas Cardíacas/genética , Cardiomiopatia Hipertrófica/genética , Proteínas de Transporte/genética , Proteínas de Transporte/metabolismo , Hiperplasia/metabolismo , Mutação , Miócitos Cardíacos/metabolismo , Transcriptoma , Peixe-Zebra/genética , Peixe-Zebra/metabolismoRESUMO
Life-threatening pulmonary influenza can be caused by inborn errors of type I and III IFN immunity. We report a 5-yr-old child with severe pulmonary influenza at 2 yr. She is homozygous for a loss-of-function IRF9 allele. Her cells activate gamma-activated factor (GAF) STAT1 homodimers but not IFN-stimulated gene factor 3 (ISGF3) trimers (STAT1/STAT2/IRF9) in response to IFN-α2b. The transcriptome induced by IFN-α2b in the patient's cells is much narrower than that of control cells; however, induction of a subset of IFN-stimulated gene transcripts remains detectable. In vitro, the patient's cells do not control three respiratory viruses, influenza A virus (IAV), parainfluenza virus (PIV), and respiratory syncytial virus (RSV). These phenotypes are rescued by wild-type IRF9, whereas silencing IRF9 expression in control cells increases viral replication. However, the child has controlled various common viruses in vivo, including respiratory viruses other than IAV. Our findings show that human IRF9- and ISGF3-dependent type I and III IFN responsive pathways are essential for controlling IAV.
Assuntos
Alelos , Homozigoto , Influenza Humana , Fator Gênico 3 Estimulado por Interferon, Subunidade gama/deficiência , Orthomyxoviridae/imunologia , Pneumonia Viral , Feminino , Humanos , Lactente , Influenza Humana/genética , Influenza Humana/imunologia , Influenza Humana/patologia , Interferon alfa-2/genética , Interferon alfa-2/imunologia , Fator Gênico 3 Estimulado por Interferon, Subunidade gama/imunologia , Pneumonia Viral/genética , Pneumonia Viral/imunologia , Pneumonia Viral/patologiaRESUMO
This article provides detailed information on the phenotypes and the metabolic profiles of 196 date fruits from 123 unique date fruit varieties. These date fruits are extensively diverse in their country of origin, variety and post harvesting conditions. We used a non-targeted mass-spectrometry based metabolomics approach to metabolically characterize date fruits, and measured 427 metabolites from a wide range of metabolic pathways. The metabolomics data for all the date fruit samples are available at the NIH Common Fund's Data Repository and Coordinating Center (supported by NIH grant, U01-DK097430) website, http://www.metabolomicsworkbench.org), under Metabolomics Workbench StudyID: ST000867. The data are directly accessible at http://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST000867&StudyType=MS&ResultType=1.