Literature DB >> 28698311

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

Jessica M Hoffman1, Yang Lyu2, Scott D Pletcher2, Daniel E L Promislow3,4.   

Abstract

Age is the single greatest risk factor for a wide range of diseases, and as the mean age of human populations grows steadily older, the impact of this risk factor grows as well. Laboratory studies on the basic biology of ageing have shed light on numerous genetic pathways that have strong effects on lifespan. However, we still do not know the degree to which the pathways that affect ageing in the lab also influence variation in rates of ageing and age-related disease in human populations. Similarly, despite considerable effort, we have yet to identify reliable and reproducible 'biomarkers', which are predictors of one's biological as opposed to chronological age. One challenge lies in the enormous mechanistic distance between genotype and downstream ageing phenotypes. Here, we consider the power of studying 'endophenotypes' in the context of ageing. Endophenotypes are the various molecular domains that exist at intermediate levels of organization between the genotype and phenotype. We focus our attention specifically on proteins and metabolites. Proteomic and metabolomic profiling has the potential to help identify the underlying causal mechanisms that link genotype to phenotype. We present a brief review of proteomics and metabolomics in ageing research with a focus on the potential of a systems biology and network-centric perspective in geroscience. While network analyses to study ageing utilizing proteomics and metabolomics are in their infancy, they may be the powerful model needed to discover underlying biological processes that influence natural variation in ageing, age-related disease, and longevity.
© 2017 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

Entities:  

Keywords:  ageing; biological networks; metabolomics; proteomics; systems biology

Mesh:

Substances:

Year:  2017        PMID: 28698311      PMCID: PMC5743054          DOI: 10.1042/EBC20160083

Source DB:  PubMed          Journal:  Essays Biochem        ISSN: 0071-1365            Impact factor:   7.258


  114 in total

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