Dataset Information

RNAAgeCalc: A multi-tissue transcriptional age calculator.

ABSTRACT: Biological aging reflects decline in physiological functions and is an effective indicator of morbidity and mortality. Numerous epigenetic age calculators are available, however biological aging calculators based on transcription remain scarce. Here, we introduce RNAAgeCalc, a versatile across-tissue and tissue-specific transcriptional age calculator. By performing a meta-analysis of transcriptional age signature across multi-tissues using the GTEx database, we identify 1,616 common age-related genes, as well as tissue-specific age-related genes. Based on these genes, we develop new across-tissue and tissue-specific age predictors. We show that our transcriptional age calculator outperforms other prior age related gene signatures as indicated by the higher correlation with chronological age as well as lower median and median error. Our results also indicate that both racial and tissue differences are associated with transcriptional age. Furthermore, we demonstrate that the transcriptional age acceleration computed from our within-tissue predictor is significantly correlated with mutation burden, mortality risk and cancer stage in several types of cancer from the TCGA database, and offers complementary information to DNA methylation age. RNAAgeCalc is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#RNAAgeCalc, both as Bioconductor and Python packages, accompanied by a user-friendly interactive Shiny app.

SUBMITTER: Ren X

PROVIDER: S-EPMC7402472 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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RNAAgeCalc: A multi-tissue transcriptional age calculator.

Ren Xu X Kuan Pei Fen PF

PloS one 20200804 8

Biological aging reflects decline in physiological functions and is an effective indicator of morbidity and mortality. Numerous epigenetic age calculators are available, however biological aging calculators based on transcription remain scarce. Here, we introduce RNAAgeCalc, a versatile across-tissue and tissue-specific transcriptional age calculator. By performing a meta-analysis of transcriptional age signature across multi-tissues using the GTEx database, we identify 1,616 common age-related ...[more]

PMID: 32750074

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Project description:Objective: Both critical illness and current care have been hypothesized to upset daily rhythms and impair molecular circadian function. However, the influence of critical illness on clock function in different tissues and on circadian output genes are unknown. Here we evaluate the effect of critical care and illness on transcription, focusing on the functional organization of the core circadian oscillator. Methods: We downloaded RNAseq count data from the Genotype-Tissue Expression (GTEx) project. Treating mechanical ventilation as a marker for intensive care, we stratified samples into acute death (AD) and intensive care (IC) groups based on the documented Hardy Death Scale. We restricted our analysis to the 25 tissues with >50 samples in each group. Using the edgeR package and controlling for collection center, gender, and age, we identified transcripts differentially expressed between the AD and IC groups. Overrepresentation and enrichment methods were used to identify gene sets modulated by intensive care across tissues. For each tissue, we then calculated the delta clock correlation distance (ΔCCD), a comparative measure of the functional organization of the core circadian oscillator, in the both the AD and IC groups. The statistical significance of the ΔCCD was assessed by permutation, modifying a pre-existing R package to control for confounding variables. Results: Intensive care, as marked by ventilation, significantly modulated the expression of thousands of genes. Transcripts that were modulated in ≥75% of tissues were enriched for genes involved in mitochondrial energetics, cellular stress, metabolism, and notably circadian regulation. Transcripts that were more markedly affected, in ≥10 tissues, were enriched for inflammation, complement and immune pathways. Oscillator organization, as assessed by ΔCCD, was significantly reduced in the intensive care group in 11/25 tissues. Conclusion: Our findings support the hypothesis that patients in intensive care have impaired molecular circadian rhythms. Tissues involved in metabolism and energetics demonstrated the most marked changes in oscillator organization. In adipose tissue, there was a significant overlap between transcripts previously established to be modulated by sleep deprivation and fasting with those modulated by critical care. This work suggests that intensive care protocols that restore sleep/wake and nutritional rhythms may be of benefit.

Project description:BackgroundHeart age calculators are used worldwide to engage the public in cardiovascular disease (CVD) prevention. Experimental studies with small samples have found mixed effects of these tools, and previous reports of population samples that used web-based heart age tools have not evaluated psychological and behavioral outcomes.ObjectiveThis study aims to report on national users of the Australian heart age calculator and the follow-up of a sample of users.MethodsThe heart age calculator was launched in 2019 by the National Heart Foundation of Australia. Heart age results were calculated for all users and recorded for those who signed up for a heart age report and an email follow-up over 10 weeks, after which a survey was conducted. CVD risk factors, heart age results, and psychological and behavioral questions were analyzed using descriptive statistics and chi-square tests. Open responses were thematically coded.ResultsThere were 361,044 anonymous users over 5 months, of which 30,279 signed up to receive a heart age report and 1303 completed the survey. There were more women (19,840/30,279, 65.52%), with an average age of 55.67 (SD 11.43) years, and most users knew blood pressure levels (20,279/30,279, 66.97%) but not cholesterol levels (12,267/30,279, 40.51%). The average heart age result was 4.61 (SD 4.71) years older than the current age, including (23,840/30,279, 78.73%) with an older heart age. For the survey, most users recalled their heart age category (892/1303, 68.46%), and many reported lifestyle improvements (diet 821/1303, 63.01% and physical activity 809/1303, 62.09%). People with an older heart age result were more likely to report a doctor visit (538/1055, 51.00%). Participants indicated strong emotional responses to heart age, both positive and negative.ConclusionsMost Australian users received an older heart age as per international and UK heart age tools. Heart age reports with follow-up over 10 weeks prompted strong emotional responses, high recall rates, and self-reported lifestyle changes and clinical checks for more than half of the survey respondents. These findings are based on a more engaged user sample than previous research, who were more likely to know blood pressure and cholesterol values. Further research is needed to determine which aspects are most effective in initiating and maintaining lifestyle changes. The results confirm high public interest in heart age tools, but additional support is needed to help users understand the results and take appropriate action.

Dataset Information

RNAAgeCalc: A multi-tissue transcriptional age calculator.

Publications

RNAAgeCalc: A multi-tissue transcriptional age calculator.

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