Aging Atlas: a multi-omics database for aging biology

Abstract

Organismal aging is driven by interconnected molecular changes encompassing internal and extracellular factors. Combinational analysis of high-throughput ‘multi-omics’ datasets (gathering information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and pharmacogenomics), at either populational or single-cell levels, can provide a multi-dimensional, integrated profile of the heterogeneous aging process with unprecedented throughput and detail. These new strategies allow for the exploration of the molecular profile and regulatory status of gene expression during aging, and in turn, facilitate the development of new aging interventions. With a continually growing volume of valuable aging-related data, it is necessary to establish an open and integrated database to support a wide spectrum of aging research. The Aging Atlas database aims to provide a wide range of life science researchers with valuable resources that allow access to a large-scale of gene expression and regulation datasets created by various high-throughput omics technologies. The current implementation includes five modules: transcriptomics (RNA-seq), single-cell transcriptomics (scRNA-seq), epigenomics (ChIP-seq), proteomics (protein–protein interaction), and pharmacogenomics (geroprotective compounds). Aging Atlas provides user-friendly functionalities to explore age-related changes in gene expression, as well as raw data download services. Aging Atlas is freely available at https://bigd.big.ac.cn/aging/index.

A single-cell transcriptomic landscape of the lungs of patients with COVID-19

“…Notably, in most cell types, we found ageing-associated senescence-associated secretory phenotype (SASP)-gene expression to be further elevated by COVID-19 (ref. 21 ), indicating that SARS-CoV-2 infection amplifies the pro-inflammatory microenvironment of the aged lung (Fig. 4e and Extended Data Figs.…”

Single-nucleus transcriptomic landscape of primate hippocampal aging

“…4 A and 4 B), which was in agreement with the increased transcriptional noise observed in other aged tissues (Li et al, 2020 ; Wang et al, 2020a ; Zhang et al, 2020c ). In addition, to annotate the hotspot genes involved in aging and age-related diseases, we performed an integrative comparative analysis between cell-type-specific marker genes and genes from the Aging Atlas (AA) gene set and gene sets implicated in learning and memory disorders (LD, MD) as well as neurodegenerative diseases including Alzheimer’s disease (AD) and Parkinson’s disease (PD) (Aging Atlas, 2021 ). The marker genes of microglia and EC were the most frequently annotated as hotspot genes (Fig.…”

“…Next, we performed an integrative comparative analysis between newly identified age-associated DEGs of the NHP hippocampus and annotated hotspot genes from various human neurodegenerative disease-associated gene sets or from the Aging Atlas gene set (Fig. 4 G) (Aging Atlas, 2021 ). Through the construction of a high-risk DEG network linking hippocampal aging and neurodegenerative diseases, we found that most of the high-risk DEGs were enriched in TAPC and microglia.…”

“…All the sequencing data are deposited in Genome Sequence Archive (GSA) ( https://bigd.big.ac.cn/gsa/ ) with the accession number of CRA004080 and the snRNA-seq atlas can be accessed via an interactive user-friendly webtool at Aging Atlas ( https://bigd.big.ac.cn/aging/landscape?project=CM_hippocampus ) (Aging Atlas, 2021 ).…”

Cytosolic Self-DNA—A Potential Source of Chronic Inflammation in Aging

“…Although these results clearly identify various mechanisms for the release of mtDNA, more research is needed to document mtDNA release under physiologically relevant conditions. Moreover, bioinformatics analysis of the genes that have been experimentally demonstrated to promote mtDNA release in various omics datasets [ 143 ] may provide some support for increased age-related cytosolic mtDNA release.…”