Proteomics in aging research: A roadmap to clinical, translational research

Abstract

The identification of plasma proteins that systematically change with age and, independent of chronological age, predict accelerated decline of health is an expanding area of research. Circulating proteins are ideal translational "omics" since they are final effectors of physiological pathways and because physicians are accustomed to use information of plasma proteins as biomarkers for diagnosis, prognosis, and tracking the effectiveness of treatments. Recent technological advancements, including mass spectrometry (MS)-based proteomics, multiplexed proteomic assay using modified aptamers (SOMAscan), and Proximity Extension Assay (PEA, O-Link), have allowed for the assessment of thousands of proteins in plasma or other biological matrices, which are potentially translatable into new clinical biomarkers and provide new clues about the mechanisms by which aging is associated with health deterioration and functional decline. We carried out a detailed literature search for proteomic studies performed in different matrices (plasma, serum, urine, saliva, tissues) and species using multiple platforms. Herein, we identified 232 proteins that were age-associated across studies. Enrichment analysis of the 232 age-associated proteins revealed metabolic pathways previously connected with biological aging both in animal models and in humans, most remarkably insulin-like growth factor (IGF) signaling, mitogen-activated protein kinases (MAPK), hypoxia-inducible factor 1 (HIF1), cytokine signaling, Forkhead Box O (FOXO) metabolic pathways, folate metabolism, advance glycation end products (AGE), and receptor AGE (RAGE) metabolic pathway. Information on these age-relevant proteins, likely expanded and validated in longitudinal studies and examined in mechanistic studies, will be essential for patient stratification and the development of new treatments aimed at improving health expectancy.

Keywords: aging; geroscience; human; proteomics.

FIGURE 1

The cascade from transcription to translation is very complex and only a portion of mRNA transcripts are translated into proteins. This explains why measures of transcripts and proteins (and protein functions) are poorly correlated. Several mechanisms regulate gene expression from transcription to translation in order to determine the precise production and timing of a given protein in a cell. (1) DNA wraps around histone proteins in a very tight, non‐accessible structure (heterochromatin). The acetylation of the lysine at the end of histones loosens its organization (euchromatin), eventually allowing transcription factors and RNA polymerase to access DNA and initiate (2) transcription. The RNA polymerase copies a segment of DNA into and antiparallel RNA strand. If the segment of DNA transcribed includes a protein‐coding gene, a pre‐mRNA is produced. (3) Introns are then removed, and exons are assembled into splicing variants; mRNA maturation includes addition of a methyl‐guanosine nucleotide at the 5′ end (“5′ capping”) and addition of multiple adenosines [a poly(A) tail] at the 3′ end, which are necessary for nuclear export, stability, and translation of the mature mRNA. In the cytoplasm, mRNA can be stored in granules (e.g., stress granules or polar granules) and subsequently removed from storage (5) or undergo modifications that substantially reduce or increase turnover (6). Finally, mRNA can be transported to ribosomes and translated into proteins (7). Nascent proteins in turn undergo a number of post‐translational chemical modifications (PTMs), folding and assemblage that strongly affect their biological and properties (8)

FIGURE 2

Protein‐level changes are proximal to biological activity biological activity. After translation (1) proteins are folded by chaperones (2), in some cases assembled with other proteins into functional complexes (3), transported to the right site (4), and chemically modified by various additions such as phosphorylation, glycosylation, or acetylation (5). All these modifications can profoundly affect the protein biological activity. Proteins are then recycled after degradation by autophagy and/or the ubiquitin proteasome system (6). Of note, proteins can be quantified at various stages of maturation differently by different methods. For example, aptamers and proximity extension assays require appropriate folding while LC‐MS‐based methods are based on AA sequence

FIGURE 3

Pathway enrichment analysis of aging proteome. Visualization of 21 pathway clusters from 232 proteins that change systematically with aging (present in 12 human plasma proteome studies and other matrices). Each node (circle) is a pathway, and similar functional groups are clustered together. The representative (most significant) pathways from the pathway clusters are labeled. Pathways discussed here are shown in highlighted colors (IGF, FOXO, AGE/RAGE, MAPKinase, Chemokine/Cytokine, Folate metabolism), and all other pathway groups are shown as gray outlined. All pathways are Bonferroni‐corrected p < 0. 05. Node size shows pathway term significance; bigger nodes are most significant. Node color shows the proportions of genes from each cluster that are associated with the pathway