4.3. Transcriptomics

The transcriptome is the entirety of the RNA transcripts, which can be divided into coding and noncoding RNAs, the latter acting as regulatory elements.[112] Similar to the epigenome, the tran- scriptome changes significantly during aging.[90] Due to the rise of technologies, such as NGS and microarrays (DNA chips), the field of transcriptomics significantly evolved, making it possible to study gene alterations with age on large sample sets. The tran- scriptomic studies have already been performed for many types of human cells and tissues, and age-related changes in transcrip- tome were shown to be highly tissue-specific.[90,113]

n = The first large-scale meta-analysis ( 14 983) of human whole-blood gene expression revealed a compendium of genes (1497) differentially expressed with chronological age.[114] Identi- fied genes were consistent with well-known aging mechanisms including dysregulation of transcription and translation, ribo- some and mitochondria dysfunction, DNA damage accumula- tion, immune senescence, and altered metabolism. A principal transcriptomic age predictor was established based on 11 908 sig- nificantly expressed genes with the average absolute difference between predicted age and chronological age of 7.8 (Table S1, Supporting Information).

4.2. Epigenomics

Epigenetic modification including DNA methylation as well as histone methylation and acetylation are highly dynamic pro- cesses that occur over the human life span and are influenced by environmental and genetic factors.[103] These modifications also change during aging, making them a potential biomarker. While the total DNA methylation level slowly decreases with age, cytosine methylation at specific loci containing CpG din- ucleotides (referred to as CpG sites) changes differently with age, becoming both hyper- or hypo-methylated in different ge- nomic locations.[104,105] A significant breakthrough in biological age measurement came with the development of a well-known “epigenetic clocks” based on a correlation between the chrono- logical age and methylation status of selected CpG sites. Methy-

Integrative metabolomics and transcriptomics were later ap- plied to show global metabolic adaptations occurring in epider- mal skin during aging.[115] Microarray analysis of the transcrip- tome was performed on epidermal tissue samples from 24 young and 24 old donors, of which 23 donors of each group were also in- cluded in the metabolomics analysis. The results of the study re-

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vealed age-dependent reduction of coenzyme Q10 levels, as well as altered activity in upper glycolysis and glycerolipid biosynthe- sis or decreased protein and polyamine biosynthesis.[115] Impor- tantly, the identified compendium of age-dependent genes (1053 transcripts with decreased and 932 transcripts with increased lev- els) had no significant overlap with reported genes predicted to be involved in aging in humans in other tissues. This indicates that each tissue of our body has its own age-dependent gene ex- pression pattern.

date.[122] The extremely wide concentration range of the plasma proteins (up to 12 orders of magnitude) as well as the pres- ence of highly abundant proteins, such as serum albumin, com- plement factors, and immunoglobulins, that constitute more than 99% of the total protein mass,[123] impede the discovery and validation of novel biomarkers in clinical samples. Mass spectrometry-based proteomics remains the gold standard for protein biomarker screening, yet it lacks throughput in the val- idation phase.[124] The dynamic range problem could be partly overcome due to the emergence of the affinity-based, or “tar- geted” proteomics technologies. Thus, SOMAscan assay, a Slow Off-rate Modified Aptamer (SOMAmer)-based capture array, cov- ers now more than 4000 unique plasma proteins, whose con- centrations vary by eight orders of magnitude.[125] This and similar high-throughput multiplexing platforms have already been applied to exam age-associated alterations at the human sub-proteomes.

Fleischer and co-workers analyzed large genome-wide RNA- seq data of human dermal fibroblasts from 133 people aged 1 to 94 years old to unravel a transcriptomic signature of biological age.[116] The ensemble machine learning method based on linear discriminant analysis classifiers (i.e., transcriptomic clock) was shown to predict chronological age to a median absolute error of 4 years, at the time outperforming previous epigenetic and transcriptomic age estimators, even though one to two orders of magnitude fewer samples were used (Table S1, Supporting Information).[116]

The proteomics study of plasma proteins using SOMAscan as- say discovered 13 proteins associated with chronological age and n = age-related phenotypes in a cohort of female twins ( 202) and of these proteins, ten were also replicated in the independent sex- mixed cohort (n = 677).[126] Further validation using RNAseq data (n = 384) gave a total of 11 proteins replicated at protein level or with consistent association with age at gene expression level. The estimated heritability of these proteins was low to moder- ate, confirming the proposition that human aging and longevity have only a modest genetic contribution, instead being strongly influenced by environmental factors. The strongest association with age was shown for chordin-like protein 1 (CHRDL1), which is involved in bone morphogenic protein signaling,[127] retinal angiogenesis,[128] and brain plasticity.[129] Levels of CHRDL1 in- crease in older individuals, correlating with lower cardiovascular risk and higher birth weight (a well-known determinant of aging trajectories), suggesting its potential as a biomarker of healthy aging. Surprisingly, the discovered set of 11 proteins does not in- clude broadly accepted aging biomarkers, such as IL-6 or CRP, which is explainable by insufficient proteome coverage and speci-