The multiomics – a precision approach for aging measurement

Highlights:

• Multiomics analyses data are integrated from multiple specific fields or "omes" like genome, transcriptome, and proteome
• Biomarkers of human aging derived from omics data could be used for personal aging speed measurement
• Multiomics is still a costly approach in need of upgrade and standardization 

Introduction

Aging is a complex process influenced by external factors like environment, lifestyle as well as our internal biology, genes, epigenetic changes, metabolic regulation, interactions between host and microbiome, and others. Biological processes, such as the development of diseases and aging, depend on a dynamic and interactive system of molecular layers such as genetics, proteomics, or transcriptomics that, taken together, can be called multiomics. The analysis of high-throughput multiomics datasets can provide a complex and integrated profile of the multifactorial aging process in detail never available before. Multiomics strategies let scientists explore the molecular profile and regulatory status during aging and disease processes, leading to the discovery of new interventions.

The science of omics

The word omics refers to a field of biological sciences study ending with -omics: genomics, transcriptomics, proteomics, metabolomics, and others. The suffix -ome forms nouns denoting fields of a specified nature and addresses the objects of study of such fields as:

• Genome - an organism's complete set of genetic information. Each genome contains all of the genes needed to build, grow and develop an organism
• Transcriptome - the complete set of RNA transcripts in a specific cell type or tissue at a particular developmental stage, including messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA (rRNA), and the non-coding RNAs (1)
• Proteome - the sum of all the proteins in a cell, tissue, or organism.
• Metabolome - the complete set of metabolites in the cell, tissue, organ, or organism, which are the end products of cellular processes (2)
• Epigenome - all modifications to the genome that do not affect the DNA sequence but determine the gene expression (2)

The omics sciences identify, characterize, and quantify all biological molecules involved in the structure, function, and dynamics of a cell, tissue, or whole organism. The multiomics analysis is used in different fields of bioscience (3). It helps to understand age-related diseases such as cardiovascular disease (4), dementia (5), diabetes (6), and cancer (7), as well as aging itself. The multiomics approach provides the understanding of hidden associations and pathways which may be critical for both patients and specialists (8).

Biomarkers of human aging derived from omics data

Without personal aging speed measurement, successful preventive interventions for aging-related conditions are impossible (9). Aging biomarkers are molecular, cellular, or physiological parameters of the body demonstrating reproducible quantitative or qualitative changes with age. Multiomics measurement of aging is an analysis of age-related correlations among the big data obtained from the analysis of various omics. The newest way of identifying the biomarkers of human aging utilizes deep neural networks (10). The multiomics gives the possibility to evaluate complex quantitative and phenotypic aging biomarkers. This evaluation is essential due to the weak understanding of the nature of aging and the fact that distinguishing between the causes and effects of aging is difficult. Applying the whole set of omics data in aging biomarker design could create a holistic view of the aging landscape (11). Multiomics gives the possibility of growth to newly emerging companies providing easily accessible express aging clock tests that can be purchased online.

Aging-associated transcriptome features

Aging is characterized by great changes in the transcriptional profile in absolutely all human tissues. It is possible to sort out six gene expression hallmarks of aging (11):

1. genes encoding mitochondrial proteins are downregulated
2. protein synthesis machinery genes are downregulated
3. genes controlling immune response are deregulated
4. the signaling cascade of growth factors has a lower expression profile
5. the DNA damage and stress response genes are activated constantly
6. transcriptional drift – overall expression profile is deregulated

Genomics of human aging

Genetic contribution to human variation in aging is still questioned, varying from around 15% to 30%. Genome-wide association studies (GWAS) are a powerful approach to examine the genetic architecture of aging in the genome. The increasing number of analyzed genomes of the elderly, particularly centenarians, provides insights into the genetic predisposition of exceptional longevity. Until now, only the APOE (apolipoprotein E), FOXO3 (stress-induced transcription factor forkhead box O3), and 5q33.3 (longevity locus on chromosome 5q33.3) loci are repeatedly connected with longevity across studies. The largest GWAS on human lifespan to date validated seven previously identified loci and proposed five novel genomic regions implicated in lifespan heritability, such as KCNK3 (Potassium channel subfamily K member 3) (12). However, even this study failed to thoroughly explain the heritability of lifespan and longevity. Further investigations using different populations and ethnic groups are needed (13).

Metabolome-based aging biomarkers

The whole metabolome is an informative complex biomarker, mainly when associated with other omics. It is noteworthy that metabolism can simultaneously be a driver and a marker of aging (11). Since the circulating blood collects metabolites from all organs, metabolomic profiling provides integral data of human physiology and age-related degenerative processes. In aging research, the advantages of metabolomics are enhanced sensitivity and predictability to the body's physiological state and the potential quick responsiveness changes in diet, lifestyle, or medicine intake. Fourteen circulating biomarkers were independently associated with all-cause mortality (14). The identified biomarkers are related to lipoprotein and fatty acids metabolism, glycolysis, fluid balance, and inflammation. Most of them, such as lipids, glucose, or albumin, are well-known risk factors for age-related diseases (13).

Epigenomic-based aging biomarkers

Epigenetic modifications such as DNA methylation, histone methylation, and acetylation are highly dynamic processes influenced by environmental and genetic factors. The total DNA methylation level slowly decreases with age. In contrast, cytosine methylation at specific loci containing CpG dinucleotides becomes both hyper- or hypo-methylated in different genomic locations. CpG dinucleotides are places in DNA where a nucleotide cytosine is followed by a guanine in the linear sequence (15). A considerable improvement in biological age measurement allowed to develop well-known epigenetic clocks based on a correlation between the chronological age and methylation status of selected CpG sites. Methylation clocks have been well studied and are among the most accurate chronological age predictors (13, 16). The epigenetic age may respond quickly to anti-aging interventions, offering new validating strategies to delay aging instead of conducting lengthy and expensive longitudinal trials. One of such strategy is the TRIIM (Thymus Re-generation, Immunorestoration, and Insulin Mitigation) treatment, which exhibited epigenetic rejuvenation properties. It reversed the epigenetic age by 2.5 years in the time of one-year of treatment. However, still little is known about which aging mechanisms epigenetic clocks reflect and what is the role of the changes in the epigenome with age (17)(13). 

The proteomic landscape of human aging

Proteins usually directly influence the information transduction in signaling pathways. Thus, they can tell much about the aging process more precisely (11). The number of proteins is at least two orders of magnitudes higher than the number of genes because of alternative splicing and post-translational protein modifications. More than 10 thousands proteins have been identified to date in human plasma. The proteomics study of plasma proteins using SOMAscan (a Slow Off-rate Modified Aptamer) assay discovered 13 proteins associated with chronological age and age-related phenotypes (18). Further validation using RNAseq data gave a total of 11 proteins replicated at the protein level or with consistent association with age at the gene expression level. The strongest association with age was shown for chordin-like protein 1 (CHRDL1), involved in bone morphogenic protein signaling, retinal angiogenesis, and brain plasticity. Mentioned set of 11 proteins does not include broadly accepted aging biomarkers, such as interleukin 6 (IL-6) or C-reactive protein (CRP), which is explainable by insufficient proteome coverage and specificity (13).

The Aging Multiomics Databases

The development of novel multi-omics techniques has resulted in the rapid accumulation of many aging-related datasets providing information on aging. Currently, there are several publicly available databases of aging-specific gene information, including the Human Aging Genomic Resources (HAGR)(19), AgeFactDB (20), and AGEMAP (21). They compile aging phenotypes, longevity records, aging- and longevity-related genes, and lifespan-extending factors (11). Another example is The Aging Atlas database which aims to provide a wide range of life science researchers with valuable resources that allow access to large-scale gene expression and regulation datasets created by various high-throughput omics technologies. Atlas offers user-friendly functionalities for exploring age-related changes in gene expression, as well as free raw data download services (9).

Disadvantages of a multiomic approach

Multiomics approaches are still expensive and require special equipment and qualified personnel. Moreover, the reliability of digital tools is still limited by data quality. Obvious problems occur with the collection and verification of reliable big medical data. Inaccurate data sources, problems with normalization or particular sampling features lead to the situation when algorithms trained on one data type do not fit other independent samples or make irreproducible predictions. The research platforms and bioinformatics approaches for processing extensive omics data are not unified. There is a difficult challenge of big data compatibility obtained by different techniques (8, 11).

Conclusions

Aging is a complex process occurring at all levels of the organization of biological systems. That is why every new anti-aging intervention implemented in the clinical practice needs a multidimensional systemic approach to measuring the patient's rate of aging and biological age. Original multiomics studies have begun to appear regularly and become a rich database for new aging biomarkers (11). The multiomics approach gives the most comprehensive and complex insight into age-related diseases or aging and can be personalized. Unfortunately, it still has many disadvantages and limitations that require further investigation of available multiomics datasets and new algorithms testing. 

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