Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA151045 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The rates of obesity and sedentary lifestyle are on a dramatic incline, with associated detrimental health effects among women in particular. Although exercise prescriptions are useful for overcoming these problems, success can be hampered by differential responsiveness among individuals in cardiovascular fitness indices (i.e., improvements in strength, lipids, VO2max). Genetic factors appear to play an important role in determining this inter-individual variation in responsiveness. We performed microarray analyses on mRNA in whole blood from 60 sedentary women from a multi-ethnic cohort who underwent 12 weeks of exercise, to identify gene subsets that were differentially expressed between individuals who experienced the greatest and least improvements in fitness based upon a composite fitness score index. We identified 43 transcripts in 39 unique genes (FDR<10%; FC>1.5) whose expression increased the most in “high” versus “low” premenopausal female responders. Several (TIGD7, UQCRH, PSMA6, WDR12, TFB2M, USP15) have reported associations with fitness-related phenotypes. Bioinformatic analysis of the 39 genes identified 4 miRNAs whose expression has been linked to cardiovascular diseases (ANKRD22: miR-637, LRRFIP1: miR-132, PRKAR2B: miR-92a, RSAD2:miR-192). These 39 genes were enriched in 6 biological pathways, including the oxidative phosphorylation pathway (p=8.08 x 10-3). Two genes, LRRFIP1 and SNORD30, were also identified with lower expression in high responding postmenopausal women. In summary, we identified gene signatures based on mRNA analysis that define responsiveness to exercise in a largely minority-based female cohort. Importantly, this study validates several genes/pathways previously associated with exercise responsiveness and extends these findings with additional novel genes. We performed microarray analyses on mRNA in whole blood from 60 sedentary women from a multi-ethnic cohort who underwent 12 weeks of exercise, to identify gene subsets that were differentially expressed between individuals who experienced the greatest and least improvements in fitness based upon a composite fitness score index.
2012-05-31 | E-GEOD-34788 | biostudies-arrayexpress
Project description:The rates of obesity and sedentary lifestyle are on a dramatic incline, with associated detrimental health effects among women in particular. Although exercise prescriptions are useful for overcoming these problems, success can be hampered by differential responsiveness among individuals in cardiovascular fitness indices (i.e., improvements in strength, lipids, VO2max). Genetic factors appear to play an important role in determining this inter-individual variation in responsiveness. We performed microarray analyses on mRNA in whole blood from 60 sedentary women from a multi-ethnic cohort who underwent 12 weeks of exercise, to identify gene subsets that were differentially expressed between individuals who experienced the greatest and least improvements in fitness based upon a composite fitness score index. We identified 43 transcripts in 39 unique genes (FDR<10%; FC>1.5) whose expression increased the most in “high” versus “low” premenopausal female responders. Several (TIGD7, UQCRH, PSMA6, WDR12, TFB2M, USP15) have reported associations with fitness-related phenotypes. Bioinformatic analysis of the 39 genes identified 4 miRNAs whose expression has been linked to cardiovascular diseases (ANKRD22: miR-637, LRRFIP1: miR-132, PRKAR2B: miR-92a, RSAD2:miR-192). These 39 genes were enriched in 6 biological pathways, including the oxidative phosphorylation pathway (p=8.08 x 10-3). Two genes, LRRFIP1 and SNORD30, were also identified with lower expression in high responding postmenopausal women. In summary, we identified gene signatures based on mRNA analysis that define responsiveness to exercise in a largely minority-based female cohort. Importantly, this study validates several genes/pathways previously associated with exercise responsiveness and extends these findings with additional novel genes.
2012-06-01 | GSE34788 | GEO
Project description:Genomic Variations in a Multi-Ethnic Population in Malaysia
| PRJNA221113 | ENA
Project description:Genomic Variations in a Multi-Ethnic Population in Malaysia
| PRJNA221114 | ENA
Project description:Cervical cancer is a global public health subject as it affects women in the reproductive ages, and accounts for the second largest burden among cancer patients worldwide with an unforgiving 50% mortality rate. Poor awareness and access to effective diagnosis have led to this enormous disease burden, calling for point-of-care, minimally invasive diagnosis methods. Here, an end-to-end quantitative approach for a new kind of diagnosis has been developed, comprising identification of optimal biomarkers, design of the sensor, and simulation of the diagnostic circuit. Using miRNA expression data in the public domain, we identified circulating miRNA biomarkers specific to cervical cancer using multi-tier screening. Synthetic riboregulators called toehold switches specific for the biomarker panel were then designed. To predict the dynamic range of toehold switches for use in genetic circuits as biosensors, we developed a generic grammar of these switches, and built a multivariate linear regression model using thermodynamic features derived from RNA secondary structure and interaction. The model yielded predictions of toehold efficacy with an adjusted R2 = 0.59. Reaction kinetics modelling was performed to predict the sensitivity of the second-generation toehold switches to the miRNA biomarkers. Simulations showed a linear response between 10nM and 100nM before saturation. Our study demonstrates an end-to-end workflow for the efficient design of genetic circuits geared towards the effective detection of unique genomic signatures that would be increasingly important in today’s world. The approach has the potential to direct experimental efforts and minimise costs. All resources are provided open-source (https://github.com/igem2019) under GNU GPLv3 licence.
2022-05-05 | MODEL2107150001 | BioModels
Project description:First 20 principal components of 4988 genotyped GCAT Cohort individuals with Infinium Multi-Ethnic Global (MEGAEX2) array, with data for Cr1-22. Plink files with QC and imputed (SHAPEIT+IMPUTE).
| EGAD00001007730 | EGA
Project description:Cohort: Raw genotype files for BelCovid2 cohort. Genotype Chip: Illumina Global Screening Array-24 v3.0 + Multi-Disease beadchip genomic build: b37
| EGAD00010002179 | EGA
Project description:Cohort: Raw genotype files for GEN_COVID cohort. Genotype Chip: Illumina Global Screening Array-24 v3.0 + Multi-Disease beadchip genomic build: b37
| EGAD00010002177 | EGA