Similar Datasets
Project description:Atrial fibrillation (AF) is the most common sustained arrhythmia in humans, yet the molecular basis of AF remains incompletely understood. To determine the cell type-specific transcriptional changes underlying AF, we performed single-nucleus RNA-seq (snRNA-seq) on age- and sex-matched left atrial (LA) samples from 18 patients with AF and 16 controls without AF. From more than 175,000 nuclei we identified 15 cell types but found that only cardiomyocytes (CMs) and macrophages (MΦs) had a significant number of differentially expressed genes in patients with AF. Attractin Like 1 (ATRNL1) was strongly overexpressed in CMs among patients with AF and localized to the intercalated discs. Further, in both knockdown and overexpression experiments in atrial human embryonic stem cell-derived CMs (hESC-aCMs) we identified a potent role for ATRNL1 in cell stress response, cardiac conduction, cell junction organization and in modulation of the cardiac action potential. Finally, we prioritized genes at the known genetic loci for AF and identified an unexpected expression for a leading AF candidate gene, KCNN3. In sum, we have identified a role for ATRNL1 and other differentially regulated genes that may serve as potential therapeutic targets for this common arrhythmia.
2024-09-24 | GSE255612 | GEO
Project description:Atrial fibrillation (AF) is the most common sustained arrhythmia in humans, yet the molecular basis of AF remains incompletely understood. To determine the cell type-specific transcriptional changes underlying AF, we performed single-nucleus RNA-seq (snRNA-seq) on age- and sex-matched left atrial (LA) samples from 18 patients with AF and 16 controls without AF. From more than 175,000 nuclei we identified 15 cell types but found that only cardiomyocytes (CMs) and macrophages (MΦs) had a significant number of differentially expressed genes in patients with AF. Attractin Like 1 (ATRNL1) was strongly overexpressed in CMs among patients with AF and localized to the intercalated discs. Further, in both knockdown and overexpression experiments in atrial human embryonic stem cell-derived CMs (hESC-aCMs) we identified a potent role for ATRNL1 in cell stress response, cardiac conduction, cell junction organization and in modulation of the cardiac action potential. Finally, we prioritized genes at the known genetic loci for AF and identified an unexpected expression for a leading AF candidate gene, KCNN3. In sum, we have identified a role for ATRNL1 and other differentially regulated genes that may serve as potential therapeutic targets for this common arrhythmia.
2024-09-24 | GSE255991 | GEO
Project description:Large-scale Single-nuclei Profiling Identifies Role for ATRNL1 in Atrial Fibrillation
| PRJNA1077359 | ENA
Project description:Large-scale Single-nuclei Profiling Identifies Role for ATRNL1 in Atrial Fibrillation [snRNA-seq]
| PRJNA1075773 | ENA
Project description:Large-scale Single-nuclei Profiling Identifies Role for ATRNL1 in Atrial Fibrillation [bulk RNA-seq]
| PRJNA1077364 | ENA
Project description:BackgroundOptical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown.MethodsParticipants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10.ResultsWe recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events.ConclusionsThe probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).
| S-EPMC8112605 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2020-07-10 | GSE148507 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2021-08-05 | GSE181445 | GEO
Project description:Aging is accompanied by a loss of muscle mass and function, termed sarcopenia, which causes numerous morbidities and economic burdens in human populations. Mechanisms implicated in age-related sarcopenia or frailty include inflammation, muscle stem cell depletion, mitochondrial dysfunction, and loss of motor neurons, but whether there are key drivers of sarcopenia are not yet known. To gain deeper insights into age-related muscle loss, we performed transcriptome profiling on lower limb muscle biopsies from 72 young, elderly, and frail human subjects using bulk RNA-seq (N = 72) and single-nuclei RNA-seq (N = 17). This combined approach revealed changes in gene expression that occur with age and frailty in multiple cell types comprising mature skeletal muscle. Notably, we found increased expression of the genes MYH8 and PDK4, and decreased expression of the gene IGFN1, in aged muscle. We validated several key genes changes in fixed human muscle tissue using digital spatial profiling. We also identified a small population of nuclei that express CDKN1A, present only in aged samples, consistent with p21cip1-driven senescence in this subpopulation. Overall, our findings identify unique cellular subpopulations in aged and sarcopenic skeletal muscle, which will facilitate the development of new therapeutic strategies to combat age-related frailty.
| S-EPMC9792217 | biostudies-literature
Project description:Recent metabolomics studies of Rheumatoid Arthritis (RA) reported few metabolites that were associated with the disease, either due to small cohort sizes or limited coverage of metabolic pathways. Our objective is to identify metabolites associated with RA and its cofounders using a new untargeted metabolomics platform. Moreover, to investigate the pathomechanism of RA by identifying correlations between RA-associated metabolites. 132 RA patients and 104 controls were analyzed for 927 metabolites. Metabolites were tested for association with RA using linear regression. OPLS-DA was used to discriminate RA patients from controls. Gaussian Graphical Models (GGMs) were used to identify correlated metabolites. 32 metabolites are identified as significantly (Bonferroni) associated with RA, including the previously reported metabolites as DHEAS, cortisol and androstenedione and extending that to a larger set of metabolites in the steroid pathway. RA classification using metabolic profiles shows a sensitivity of 91% and specificity of 88%. Steroid levels show variation among the RA patients according to the corticosteroid treatment; lowest in those taking the treatment at the time of the study, higher in those who never took the treatment, and highest in those who took it in the past. Finally, the GGM reflects metabolite relations from the steroidogenesis pathway.
| S-EPMC5567269 | biostudies-literature