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The heart, the first organ formed during mammalian development, is also the leading cause of death. The World Health Organization estimates that in 2022 cardiovascular diseases (CVD) resulted in an estimated 17.9 million deaths globally.1
Despite these statistics, a detailed understanding of the molecular mechanisms associated with CVD underlying causes is still lacking in some areas, particularly for non-ischemic CVD.2 This category includes all causes of decreased heart function other than those triggered by heart attacks or blockages in the arteries of the heart.
To understand disease development and progression it is crucial to decipher the underlying molecular mechanisms associated with the different CVD disease states. But despite the number of identified genes and loci, the precise mechanisms by which these genes influence CVD risk are not yet well understood.
Multiomics analyses help decipher CVD
The complexity and heterogeneity of CVD suggest a real-world requirement to understand underlying individual causes that contributes toward disease development and progression. This includes atherosclerosis, thrombosis, adverse cardiac remodeling, chronic kidney disease and the gut microbiota.2
Systems biology approaches incorporating multiomics data are pinpointed as an invaluable tool to aid in establishing alterations in specific cell types and identifying modifications in signaling events that promote CVD disease development.2,3 A strength of this approach is the potential to include measurements from multiple omics platforms.4
“Multiomics allows you to more completely understand the complicated biological interactions that can lead to CVD,” said Steven Hoffman, segment head of single cell sequencing at Illumina.
“Approaches like genome wide association studies (GWAS) demonstrate the correlation between genomic biomarkers and phenotype but lack the ability to completely illuminate the cause or mode of action,” added Hoffman. “It is becoming critical to connect the dots between genome, epigenetic regulation, including tissue or cell type specific expression of genes, and important functional proteins to identify therapeutic targets.”
Advances in high-throughput technology enhance researchers’ ability to analyze whole genomes, epigenomes, transcriptomes, proteomes, metabolomes, and metagenomes. Integrative multiomics data analyses facilitate identification of causal genes and reveal underlying molecular mechanisms that are involved in the progression of cardiovascular events.2
Importantly to note, most of the risk variants associated with coronary artery disease (CAD), the most common cause of cardiovascular death, or other CVD identified by GWAS, are located in noncoding, intronic or intergenic, regions of the genome. This suggests that these variants are likely to affect cis or trans regulatory elements that bind transcription factors, enhancers, or promoters.3
Integrating multiomics strategies can accelerate more precise identification of novel molecular mechanisms implicated in CVD. For instance, a global transcriptomic and proteomic analysis of human stenotic valves identified novel potential molecular drivers in calcified aortic valve disease (CAVD), including alkaline phosphatase, apolipoprotein B, matrix metalloproteinase activation, and mitogen-activated protein kinase.5
The goal is that additional multiomics work may eventually result in the characterization of novel pathways and drug targets to yield more data on this complex disease.3
Understanding cardiac development
To investigate the multilayered control and functional characterization of cardiac development and maturation, researchers at Nanjing Medical University applied comprehensive mapping and integrative analysis of the proteome, phosphoproteome, transcriptome, and metabolome of mice hearts along with functional omics.6
The work revealed the sequence of molecular events that underlie cardiac development and maturation and provided new insights into the multilayered control of developmental transformation. Notably, this study produced an updated interactive multiomics resource for use in further investigation.6
Multi-tissue approaches needed
The heart does not operate in a vacuum. To further understand tissue-specific metabolic crosstalk and to simulate whole-body metabolic functions in health and CVD, models of the heart and other human tissues need integration with the gut microbiota. This multitissue approach has been limited to date.2
A predictive model incorporating essential metabolic interactions or signaling pathways in each individual tissue/microbiota model that are known to either protect against or contribute towards the development and progression of CVD helps arrest this lethal disease.2 Multiomics analyses can only aid this effort.
2. Doran S, Arif M, Lam S et al. Multi-omics approaches for revealing the complexity of cardiovascular disease. Brief Bioinform. 2021 Sep 2;22(5):bbab061. DOI: 10.1093/bib/bbab061.
3. Leon-Mimila P, Wang J and Huertas-Vazquez A. Relevance of Multi-Omics Studies in Cardiovascular Diseases. Front. Cardiovasc. Med. 2019; 6:91. DOI: 10.3389/fcvm.2019.00091
4. Joshi, A, Rienks, M, Theofilatos, K et al. Systems biology in cardiovascular disease: a multiomics approach. Nat Rev Cardiol. 2021; 18, 313–330 (2021). DOI: 10.1038/s41569-020-00477-1
5. Schlotter F, Halu A, Goto S et al. Spatiotemporal Multi-Omics Mapping Generates a Molecular Atlas of the Aortic Valve and Reveals Networks Driving Disease. Circulation. 2018 Jul 24;138(4):377-393. DOI: 10.1161/CIRCULATIONAHA.117.032291
6. Gu Y, Zhou Y, Ju S, et al. Multi-omics profiling visualizes dynamics of cardiac development and functions. Cell Rep. 2022, Dec 27; 41(13):111891. DOI: 10.1016/j.celrep.2022.111891.
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