Major aging-associated RNA expressions change at two distinct age-positions

Selecting mRNA markers in blood for age estimation of the donor of a biological stain

RNA has gained a substantial amount of attention within the forensic field over the last decade. There is evidence that RNAs are differentially expressed with biological age. Since RNA can be co-extracted with DNA from the same piece of evidence, RNA-based analysis appears as a promising molecular alternative for predicting the biological age and hence inferring the chronological age of a person. Using RNA-Seq data we searched for markers in blood potentially associated with age. We used our own RNA-Seq data from dried blood stains as well as publicly available RNA-Seq data from whole blood, and compared two different approaches to select candidate markers. The first approach focused on individual gene analysis with DESeq2 to select the genes most correlated with age, while the second approach employed lasso regression to select a set of genes for optimal prediction of age. We present two lists with 270 candidate markers, one for each approach.

Biomarkers selection and mathematical modeling in biological age estimation

Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

Gene Expression Analysis Reveals Age and Ethnicity Signatures Between Young and Old Adults in Human PBMC

Human immune system functions over an entire lifetime, yet how and why the immune system becomes less effective with age are not well understood. Here, we characterize peripheral blood mononuclear cell transcriptome from 132 healthy adults with 21–90 years of age using the weighted gene correlation network analyses. In our study, 113 Caucasian from the 10KIP database and RNA-seq data of 19 Asian (Chinese) are used to explore the differential co-expression genes in PBMC aging. These two dataset reveal a set of insightful gene expression modules and representative gene biomarkers for human immune system aging from Asian and Caucasian ancestry, respectively. Among them, the aging-specific modules may show an age-related gene expression variation spike around early-seventies. In addition, we find the top hub genes including NUDT7, CLPB, OXNAD1, and MLLT3 are shared between Asian and Caucasian aging related modules and further validated in human PBMCs from different age groups. Overall, the impact of age and race on transcriptional variation elucidated from this study may provide insights into the transcriptional driver of immune aging.

The Immunity Clock

The immune system has been for long considered a marker of health. The age-related decline in its function results in a greater incidence of infections, autoimmune diseases and cancer. Nevertheless, it is still not known if immune function can be used to accurately estimate the rate of aging of an individual. A set of 14 immune function variables were measured in 214 healthy individuals ranging from 19 to 88 years old. All immune variables were selected as independent variables for the prediction of age by multiple linear regression (MLR). The Immunity Clock was constructed including the following 5 immune variables: natural killer activity, phagocytosis and chemotaxis of neutrophils and chemotaxis and proliferative capacity of lymphocytes reaching an adjusted R 2 of 80.3% and a standard error of the estimate of 4.74 years. The Immunity Clock was validated in a different group of healthy individuals (N=106) obtaining a Pearson´s correlation coefficient of 0.898 (p < 0.001) between chronological age and the age estimated by the Immunity Clock, the ImmunolAge. Moreover, we demonstrate that women with anxiety (N=10) show a higher ImmunolAge than their chronological age whereas healthy centenarians (N=8) show a lower one. In addition, the Immunity Clock provided here proves to be useful for monitoring the effectiveness of a nutritional intervention lasting one month, by detecting a diminished ImmunolAge in the same individuals. Further research will be needed to ascertain if the Immunity Clock is a passive marker of the aging process or it plays an active role in it.

Calcium Channels in Adult Brain Neural Stem Cells and in Glioblastoma Stem Cells

The brain of adult mammals, including humans, contains neural stem cells (NSCs) located within specific niches of which the ventricular-subventricular zone (V-SVZ) is the largest one. Under physiological conditions, NSCs proliferate, self-renew and produce new neurons and glial cells. Several recent studies established that oncogenic mutations in adult NSCs of the V-SVZ are responsible for the emergence of malignant primary brain tumors called glioblastoma. These aggressive tumors contain a small subpopulation of cells, the glioblastoma stem cells (GSCs), that are endowed with proliferative and self-renewal abilities like NSCs from which they may arise. GSCs are thus considered as the cells that initiate and sustain tumor growth and, because of their resistance to current treatments, provoke tumor relapse. A growing body of studies supports that Ca²⁺ signaling controls a variety of processes in NSCs and GSCs. Ca²⁺ is a ubiquitous second messenger whose fluctuations of its intracellular concentrations are handled by channels, pumps, exchangers, and Ca²⁺ binding proteins. The concerted action of the Ca²⁺ toolkit components encodes specific Ca²⁺ signals with defined spatio-temporal characteristics that determine the cellular responses. In this review, after a general overview of the adult brain NSCs and GSCs, we focus on the multiple roles of the Ca²⁺ toolkit in NSCs and discuss how GSCs hijack these mechanisms to promote tumor growth. Extensive knowledge of the role of the Ca²⁺ toolkit in the management of essential functions in healthy and pathological stem cells of the adult brain should help to identify promising targets for clinical applications.

Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification

For the past several decades, research in understanding the molecular basis of human muscle aging has progressed significantly. However, the development of accessible tissue-specific biomarkers of human muscle aging that may be measured to evaluate the effectiveness of therapeutic interventions is still a major challenge. Here we present a method for tracking age-related changes of human skeletal muscle. We analyzed publicly available gene expression profiles of young and old tissue from healthy donors. Differential gene expression and pathway analysis were performed to compare signatures of young and old muscle tissue and to preprocess the resulting data for a set of machine learning algorithms. Our study confirms the established mechanisms of human skeletal muscle aging, including dysregulation of cytosolic Ca²⁺ homeostasis, PPAR signaling and neurotransmitter recycling along with IGFR and PI3K-Akt-mTOR signaling. Applying several supervised machine learning techniques, including neural networks, we built a panel of tissue-specific biomarkers of aging. Our predictive model achieved 0.91 Pearson correlation with respect to the actual age values of the muscle tissue samples, and a mean absolute error of 6.19 years on the test set. The performance of models was also evaluated on gene expression samples of the skeletal muscles from the Gene expression Genotype-Tissue Expression (GTEx) project. The best model achieved the accuracy of 0.80 with respect to the actual age bin prediction on the external validation set. Furthermore, we demonstrated that aging biomarkers can be used to identify new molecular targets for tissue-specific anti-aging therapies.

Gene expression hallmarks of cellular ageing

Ageing leads to dramatic changes in the physiology of many different tissues resulting in a spectrum of pathology. Nonetheless, many lines of evidence suggest that ageing is driven by highly conserved cell intrinsic processes, and a set of unifying hallmarks of ageing has been defined. Here, we survey reports of age-linked changes in basal gene expression across eukaryotes from yeast to human and identify six gene expression hallmarks of cellular ageing: downregulation of genes encoding mitochondrial proteins; downregulation of the protein synthesis machinery; dysregulation of immune system genes; reduced growth factor signalling; constitutive responses to stress and DNA damage; dysregulation of gene expression and mRNA processing. These encompass widely reported features of ageing such as increased senescence and inflammation, reduced electron transport chain activity and reduced ribosome synthesis, but also reveal a surprising lack of gene expression responses to known age-linked cellular stresses. We discuss how the existence of conserved transcriptomic hallmarks relates to genome-wide epigenetic differences underlying ageing clocks, and how the changing transcriptome results in proteomic alterations where data is available and to variations in cell physiology characteristic of ageing. Identification of gene expression events that occur during ageing across distant organisms should be informative as to conserved underlying mechanisms of ageing, and provide additional biomarkers to assess the effects of diet and other environmental factors on the rate of ageing.

Blood RNA expression profiles undergo major changes during the seventh decade

Genome-wide alterations in RNA expression profiles are age-associated. Yet the rate and temporal pattern of those alterations are poorly understood. We investigated temporal changes in RNA expression profiles in blood from population-based studies using a quadratic regression model. Comparative analysis between two independent studies was carried out after sample-weighting that downsized differences in sample distribution over age between the datasets. We show that age-associated expression profiles are clustered into two major inclinations and transcriptional alternations occur predominantly from the seventh decade onwards. The age-associated genes in blood are enriched in functional groups of the translational machinery and the immune system. The results are highly consistent between the two population-based studies indicating that our analysis overcomes potential confounders in population-based studies. We suggest that the critical age when major transcriptional alterations occur could help understanding aging and disease risk during adulthood.

Biochemical isolation of myonuclei employed to define changes to the myonuclear proteome that occur with aging

Skeletal muscle aging is accompanied by loss of muscle mass and strength. Examining changes in myonuclear proteins with age would provide insight into molecular processes which regulate these profound changes in muscle physiology. However, muscle tissue is highly adapted for contraction and thus comprised largely of contractile proteins making the nuclear proteins difficult to identify from whole muscle samples. By developing a method to purify myonuclei from whole skeletal muscle, we were able to collect myonuclei for analysis by flow cytometry, biochemistry, and mass spectrometry. Nuclear purification dramatically increased the number and intensity of nuclear proteins detected by mass spectrometry compared to whole tissue. We exploited this increased proteomic depth to investigate age-related changes to the myonuclear proteome. Nuclear levels of 54 of 779 identified proteins (7%) changed significantly with age; these proteins were primarily involved in chromatin maintenance and RNA processing. To determine whether the changes we detected were specific to myonuclei or were common to nuclei of excitatory tissues, we compared aging in myonuclei to aging in brain nuclei. Although several of the same processes were affected by aging in both brain and muscle nuclei, the specific proteins involved in these alterations differed between the two tissues. Isolating myonuclei allowed a deeper view into the myonuclear proteome than previously possible facilitating identification of novel age-related changes in skeletal muscle. Our technique will enable future studies into a heretofore underrepresented compartment of skeletal muscle.

The transcriptional landscape of age in human peripheral blood

Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the ‘transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.

Ageing increases the risk of many diseases. Here the authors compare blood cell transcriptomes of over 14,000 individuals and identify a set of about 1,500 genes that are differently expressed with age, shedding light on transcriptional programs linked to the ageing process and age-associated diseases.

A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status

BACKGROUND

Diagnostics of the human ageing process may help predict future healthcare needs or guide preventative measures for tackling diseases of older age. We take a transcriptomics approach to build the first reproducible multi-tissue RNA expression signature by gene-chip profiling tissue from sedentary normal subjects who reached 65 years of age in good health.

RESULTS

One hundred and fifty probe-sets form an accurate classifier of young versus older muscle tissue and this healthy ageing RNA classifier performed consistently in independent cohorts of human muscle, skin and brain tissue (n = 594, AUC = 0.83-0.96) and thus represents a biomarker for biological age. Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity. The gene score is 'up-regulated' in healthy human hippocampus with age, and when applied to blood RNA profiles from two large independent age-matched dementia case-control data sets (n = 717) the healthy controls have significantly greater gene scores than those with cognitive impairment. Alone, or when combined with our previously described prototype Alzheimer disease (AD) RNA 'disease signature', the healthy ageing RNA classifier is diagnostic for AD.

CONCLUSIONS

We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample. This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.

Transcriptomic profiles of aging in purified human immune cells

Background

Transcriptomic studies hold great potential towards understanding the human aging process. Previous transcriptomic studies have identified many genes with age-associated expression levels; however, small samples sizes and mixed cell types often make these results difficult to interpret.

Results

Using transcriptomic profiles in CD14+ monocytes from 1,264 participants of the Multi-Ethnic Study of Atherosclerosis (aged 55–94 years), we identified 2,704 genes differentially expressed with chronological age (false discovery rate, FDR ≤ 0.001). We further identified six networks of co-expressed genes that included prominent genes from three pathways: protein synthesis (particularly mitochondrial ribosomal genes), oxidative phosphorylation, and autophagy, with expression patterns suggesting these pathways decline with age. Expression of several chromatin remodeler and transcriptional modifier genes strongly correlated with expression of oxidative phosphorylation and ribosomal protein synthesis genes. 17% of genes with age-associated expression harbored CpG sites whose degree of methylation significantly mediated the relationship between age and gene expression (p < 0.05). Lastly, 15 genes with age-associated expression were also associated (FDR ≤ 0.01) with pulse pressure independent of chronological age.

Comparing transcriptomic profiles of CD14+ monocytes to CD4+ T cells from a subset (n = 423) of the population, we identified 30 age-associated (FDR < 0.01) genes in common, while larger sets of differentially expressed genes were unique to either T cells (188 genes) or monocytes (383 genes). At the pathway level, a decline in ribosomal protein synthesis machinery gene expression with age was detectable in both cell types.

Conclusions

An overall decline in expression of ribosomal protein synthesis genes with age was detected in CD14+ monocytes and CD4+ T cells, demonstrating that some patterns of aging are likely shared between different cell types. Our findings also support cell-specific effects of age on gene expression, illustrating the importance of using purified cell samples for future transcriptomic studies. Longitudinal work is required to establish the relationship between identified age-associated genes/pathways and aging-related diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1522-4) contains supplementary material, which is available to authorized users.