Integration of ‘omics’ data in aging research: from biomarkers to systems biology

Abstract

SummaryAge is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age‐related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high‐throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so‐called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age‐related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

Proteomics and metabolomics in ageing research: from biomarkers to systems biology

“…Each ‘omic’ domain encodes unique information about genetic and biochemical processes and pathways that affect ageing. The complete understanding of the biology of ageing and ageing-related diseases thus requires a framework that integrates multiple omics approaches [109–113]. …”

The failure of “engineered rejuvenation”: laboratory genomics do not translate into precision anti-aging therapies

“…This is also sometimes known as dual inheritance theory [50] . An extension of this concept describes the effects from our increasingly technological culture upon our own biology [51] , and at the same time, it describes how humanity can influence the further evolution of the environment itself [52] . In this way it may be possible to appreciate the complex and mutually influencing interactions of each patient with the environment, and begin to form a view which is at odds with the simplistic reductionist view that "one drug fits all" paradigm [53] .…”

Perspectives of aging study on stem cell

“…Increased number of manually curated databases of aging in the public domain may accelerate biological data-centered studies (Craig et al 2015;Tacutu et al 2013). However, most OMICS studies have focused on one technology, limiting biological meaningful interpretations of the complex property, although the different OMICS data are strongly inter-correlated (Zierer et al 2015). Moreover, many aging OMICS studies are restricted to blood analysis due to its easier accessibility, although the aging process exerts its detrimental effects across multiple tissues (Valdes et al 2013).…”

“…Nonetheless, systems biology has provided a rich collection of integrative methods to overcome the obstacles, and one of the efficient methods is the integration of OMICS profiling with network approaches where molecular components such as genes or gene products are represented as nodes and mutual dependencies between them as edges ( Fig. 1) (Zierer et al 2015). Research on neurodegenerative diseases successfully adopted the integrative strategy while leveraging the availability of high-throughput neurobiology data to gain a better understanding of the complex nature of these diseases (Parikshak et al 2015).…”