Denver Asthma Panel Study-CHEAR Ancillary Study
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens
TISSUE(S): Blood
DISEASE(S): Asthma
PROVIDER: ST001004 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| mwtab | Other |
Items per page: 5 1 - 1 of 1 |
Similar Datasets
Project description: Not available
| ST001039 | MetabolomicsWorkbench
Project description:Asthma is a complex syndrome associated with episodic decompensations provoked by aeroaller-gen exposures. The underlying pathophysiological states driving exacerbations are latent in the resting state and do not adequately inform biomarker-driven therapy. A better understanding of the pathophysiological pathways driving allergic exacerbations is needed. We hypothesized that disease-associated pathways could be identified in humans by unbiased metabolomics of bron-choalveolar fluid (BALF) during the peak inflammatory response provoked by a bronchial aller-gen challenge. We analyzed BALF metabolites in samples from 12 volunteers who underwent segmental bronchial antigen provocation (SBP-Ag). Metabolites were quantified using liquid chromatography-tandem mass spectrometry (LC–MS/MS) followed by pathway analysis and cor-relation with airway inflammation. SBP-Ag induced statistically significant changes in 549 fea-tures that mapped to 72 uniquely identified metabolites. From these features, two distinct induci-ble metabolic phenotypes were identified by the principal component analysis, partitioning around medoids (PAM) and k-means clustering. Ten index metabolites were identified that in-formed the presence of asthma-relevant pathways, including unsaturated fatty acid produc-tion/metabolism, mitochondrial beta oxidation of unsaturated fatty acid, and bile acid metabolism. Pathways were validated using proteomics in eosinophils. A segmental bronchial allergen chal-lenge induces distinct metabolic responses in humans, providing insight into pathogenic and pro-tective endotypes in allergic asthma.
2022-06-09 | PXD033172 | Pride
Project description:We used ATLAS-seq to comprehensively map the genomic location of LINE-1 elements belonging to the youngest and potentially polymorphic subfamily (L1HS-Ta). This was performed in a panel of 12 human primary or transformed cell lines (BJ, IMR90, MRC5, H1, K562, HCT116, HeLa S3, HepG2, MCF7, HEK-293, HEK-293T, 2102Ep). In brief, ATLAS-seq relies on the random mechanical fragmentation of the genomic DNA to ensure high-coverage, ligation of adapter sequences, suppression PCR-amplification of L1HS-Ta element junctions, and Ion Torrent sequencing using single-end 400 bp read chemistry. A notable aspect of ATLAS-seq is that we can obtain both L1 downstream and upstream junctions (3'- and 5'-ATLAS-seq libraries, respectively), for full-length L1 elements. Note that a 10-nt sample-specific barcode has been removed at the 5' end of the reads in the .fastq files upon demultiplexing. This was achieved using cutadapt v1.9.2.dev0 (with the following parameters: -e 0.1 -q 10 -m 25 -g <barcode_name>=^<barcode_sequence>)
2016-05-24 | E-MTAB-4676 | biostudies-arrayexpress
Project description:MicroRNAs are important negative regulators of protein coding gene expression, and have been studied intensively over the last few years. To this purpose, different measurement platforms to determine their RNA abundance levels in biological samples have been developed. In this study, we have systematically compared 12 commercially available microRNA expression platforms by measuring an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples, and synthetic spikes from homologous microRNA family members. We developed novel quality metrics in order to objectively assess platform performance of very different technologies such as small RNA sequencing, RT-qPCR and (microarray) hybridization. We assessed reproducibility, sensitivity, quantitative performance, and specificity. The results indicate that each method has its strengths and weaknesses, which helps guiding informed selection of a quantitative microRNA gene expression platform in function of particular study goals.
2013-09-01 | E-MTAB-1815 | biostudies-arrayexpress
Project description:With advances in technologies that facilitate metabolome-wide analyses, the incorporation of metabolomics in the pursuit of biomarkers of exposure and effect is rapidly evolving in population health studies. However, many analytic approaches are limited in their capacity to address high-dimensional metabolomics data within an epidemiologic framework, including the highly collinear nature of the metabolites and consideration of confounding variables. In this Children's Health Exposure Analysis Resource (CHEAR) network study, we showcase various analytic approaches that are established as well as novel in the field of metabolomics, including univariate single metabolite models, least absolute shrinkage and selection operator (LASSO), random forest, weighted quantile sum (WQSRS) regression, exploratory factor analysis (EFA), and latent class analysis (LCA). Here, in a Bangladeshi birth cohort (n = 199), we illustrate research questions that can be addressed by each analytic method in the assessment of associations between cord blood metabolites (1H NMR measurements) and birth anthropometric measurements (birth weight and head circumference).
| S-EPMC8041023 | biostudies-literature
Project description:BackgroundAsthma is a chronic inflammatory disease of the airways that is heterogeneous and multifactorial, making its accurate characterization a complex process. Therefore, identifying the genetic variations associated with asthma and discovering the molecular interactions between the omics that confer risk of developing this disease will help us to unravel the biological pathways involved in its pathogenesis.ObjectiveWe sought to develop a predictive genetic panel for asthma using machine learning methods.MethodsWe tested 3 variable selection methods: Boruta's algorithm, the top 200 genome-wide association study markers according to their respective P values, and an elastic net regression. Ten different algorithms were chosen for the classification tests. A predictive panel was built on the basis of joint scores between the classification algorithms.ResultsTwo variable selection methods, Boruta and genome-wide association studies, were statistically similar in terms of the average accuracies generated, whereas elastic net had the worst overall performance. The predictive genetic panel was completed with 155 single-nucleotide variants, with 91.18% accuracy, 92.75% sensitivity, and 89.55% specificity using the support vector machine algorithm. The markers used range from known single-nucleotide variants to those not previously described in the literature. Our study shows potential in creating genetic prediction panels with tailored penalties per marker, aiding in the identification of optimal machine learning methods for intricate results.ConclusionsThis method is able to classify asthma and nonasthma effectively, proving its potential utility in clinical prediction and diagnosis.
| S-EPMC11215340 | biostudies-literature
Project description:This study consists of 10 whole genome RNA-seq profiles which have been generated from blood samples collected from ten different volunteers in the Personal Genome Project UK
2018-03-04 | E-MTAB-6523 | biostudies-arrayexpress
Project description:Asthma is a major cause of morbidity and mortality and is associated with significant economic burden worldwide. The objectives of this study were to map current resource use associated with the disease management and to estimate the annual direct and indirect costs per adult patient with asthma.A Delphi panel with seven leading pulmonologists was conducted. A semistructured questionnaire was developed to elicit data on resource use and treatment patterns. Unit costs from official, published sources were subsequently assigned to resource use to estimate direct medical costs. Indirect costs were estimated as number of work loss days. Cost base year was 2015, and the perspective adopted was that of the National Organization of Health Care Services Provision, as well as the societal.Patients with asthma are mainly managed by pulmonologists (71.4%) and secondarily by general practitioners and internists (28.6%). The annual cost of managing exacerbations was estimated at €273.1, while maintenance costs were estimated at €1,100.2 per year. Total costs of managing asthma per patient per year were estimated at €2,281.8, 64.4% of which represented direct medical costs. Of the direct costs, pharmaceutical treatment was the key driver, accounting for 63.9 and 41.2% of direct and total costs, respectively. Direct non-medical costs (patient travel and waiting time) were estimated at €152.3. Indirect costs accounted for 28.9% of total costs.Asthma is a chronic condition, the management of which constrains the already limited Greek health care resources. The increasing prevalence of the disease raises concerns as it could translate per patient costs into a significant burden for the Greek health care system. Thus, the prevention, self-management, and improved quality of care for asthma should find a place in the health policy agenda in Greece.
| S-EPMC5380663 | biostudies-other
Project description:Genome-wide identification of transcription factor (TF) binding sites in the genome of the fission yeast Schizosaccharomyces pombe. The ChIP-nexus method was used. TFs included were: Cbf11-TAP and Cbf12-TAP (and their DBM mutants with impaired DNA binding), TAP-Mga2, and Fkh2-TAP (as an irrelevant control TF). IPs from an untagged WT strain were also analyzed. Cbf11-related IPs were performed from exponential cultures, while Cbf12-related IPs were performed from stationary cultures. YES complex medium was used for all cultivations.
2024-04-05 | E-MTAB-13258 | biostudies-arrayexpress
Project description:C2C12 myoblast is a model that has been used extensively to study the process of skeletal muscle differentiation. Proteomics has advanced our understanding of skeletal muscle biology and the process of myogenesis. However, there is still no deep coverage of C2C12 myoblast proteome, which is important for the understanding of key drivers for the differentiation of skeletal muscle cells. Here, we conducted a multi-dimensional proteome profiling with TiSH strategy to get a comprehensive analysis of proteome, phosphoproteome and N-linked sialylated glycoproteome of C2C12 myoblasts. A total of 8313 protein groups were identified in C2C12 myoblasts, including 7827 protein groups from non-modified peptides, 3803 phosphoproteins and 977 formerly N-linked sialylated glycoproteins.
2020-11-27 | PXD011334 | Pride