Integration of Transformative Platforms for the Discovery of Causative Genes in Cardiovascular Diseases

Overview

Journal Cardiovasc Drugs Ther

Specialties Cardiology & Vascular Diseases
Pharmacology

Date 2021 Apr 15

PMID 33856594

Authors

Haocheng Lu

Jifeng Zhang

Y Eugene Chen

Minerva T Garcia-Barrio

Affiliations

Soon will be listed here.

Abstract

Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. Genome-wide association studies (GWAS) are powerful epidemiological tools to find genes and variants associated with cardiovascular diseases while follow-up biological studies allow to better understand the etiology and mechanisms of disease and assign causality. Improved methodologies and reduced costs have allowed wider use of bulk and single-cell RNA sequencing, human-induced pluripotent stem cells, organoids, metabolomics, epigenomics, and novel animal models in conjunction with GWAS. In this review, we feature recent advancements relevant to cardiovascular diseases arising from the integration of genetic findings with multiple enabling technologies within multidisciplinary teams to highlight the solidifying transformative potential of this approach. Well-designed workflows integrating different platforms are greatly improving and accelerating the unraveling and understanding of complex disease processes while promoting an effective way to find better drug targets, improve drug design and repurposing, and provide insight towards a more personalized clinical practice.

References

Virani S, Alonso A, Benjamin E, Bittencourt M, Callaway C, Carson A . Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation. 2020; 141(9):e139-e596. DOI: 10.1161/CIR.0000000000000757. View

Benjamin E, Muntner P, Alonso A, Bittencourt M, Callaway C, Carson A . Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation. 2019; 139(10):e56-e528. DOI: 10.1161/CIR.0000000000000659. View

Lander E, Linton L, Birren B, Nusbaum C, Zody M, Baldwin J . Initial sequencing and analysis of the human genome. Nature. 2001; 409(6822):860-921. DOI: 10.1038/35057062. View

Shearman A, Ordovas J, Cupples L, Schaefer E, Harmon M, Shao Y . Evidence for a gene influencing the TG/HDL-C ratio on chromosome 7q32.3-qter: a genome-wide scan in the Framingham study. Hum Mol Genet. 2000; 9(9):1315-20. DOI: 10.1093/hmg/9.9.1315. View

Elbein S, Hasstedt S . Quantitative trait linkage analysis of lipid-related traits in familial type 2 diabetes: evidence for linkage of triglyceride levels to chromosome 19q. Diabetes. 2002; 51(2):528-35. DOI: 10.2337/diabetes.51.2.528. View

Malhotra A, Wolford J . Analysis of quantitative lipid traits in the genetics of NIDDM (GENNID) study. Diabetes. 2005; 54(10):3007-14. DOI: 10.2337/diabetes.54.10.3007. View

Tam V, Patel N, Turcotte M, Bosse Y, Pare G, Meyre D . Benefits and limitations of genome-wide association studies. Nat Rev Genet. 2019; 20(8):467-484. DOI: 10.1038/s41576-019-0127-1. View

Mortazavi A, Williams B, McCue K, Schaeffer L, Wold B . Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008; 5(7):621-8. DOI: 10.1038/nmeth.1226. View

Xu Q, Modrek B, Lee C . Genome-wide detection of tissue-specific alternative splicing in the human transcriptome. Nucleic Acids Res. 2002; 30(17):3754-66. PMC: 137414. DOI: 10.1093/nar/gkf492. View

10.

Cloonan N, Forrest A, Kolle G, Gardiner B, Faulkner G, Brown M . Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods. 2008; 5(7):613-9. DOI: 10.1038/nmeth.1223. View

11.

Schena M, Shalon D, Davis R, Brown P . Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995; 270(5235):467-70. DOI: 10.1126/science.270.5235.467. View

12.

Marioni J, Mason C, Mane S, Stephens M, Gilad Y . RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008; 18(9):1509-17. PMC: 2527709. DOI: 10.1101/gr.079558.108. View

13.

Velculescu V, Zhang L, Vogelstein B, Kinzler K . Serial analysis of gene expression. Science. 1995; 270(5235):484-7. DOI: 10.1126/science.270.5235.484. View

14.

Stark R, Grzelak M, Hadfield J . RNA sequencing: the teenage years. Nat Rev Genet. 2019; 20(11):631-656. DOI: 10.1038/s41576-019-0150-2. View

15.

Heap G, Yang J, Downes K, Healy B, Hunt K, Bockett N . Genome-wide analysis of allelic expression imbalance in human primary cells by high-throughput transcriptome resequencing. Hum Mol Genet. 2009; 19(1):122-34. PMC: 2792152. DOI: 10.1093/hmg/ddp473. View

16.

Nielsen J, Rom O, Surakka I, Graham S, Zhou W, Roychowdhury T . Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease. Nat Commun. 2020; 11(1):6417. PMC: 7749177. DOI: 10.1038/s41467-020-20086-3. View

17.

Lescroart F, Wang X, Lin X, Swedlund B, Gargouri S, Sanchez-Danes A . Defining the earliest step of cardiovascular lineage segregation by single-cell RNA-seq. Science. 2018; 359(6380):1177-1181. PMC: 6556615. DOI: 10.1126/science.aao4174. View

18.

Lu D, Thum T . RNA-based diagnostic and therapeutic strategies for cardiovascular disease. Nat Rev Cardiol. 2019; 16(11):661-674. DOI: 10.1038/s41569-019-0218-x. View

19.

Natarajan K, Miao Z, Jiang M, Huang X, Zhou H, Xie J . Comparative analysis of sequencing technologies for single-cell transcriptomics. Genome Biol. 2019; 20(1):70. PMC: 6454680. DOI: 10.1186/s13059-019-1676-5. View

20.

Alencar G, Owsiany K, Karnewar S, Sukhavasi K, Mocci G, Nguyen A . Stem Cell Pluripotency Genes Klf4 and Oct4 Regulate Complex SMC Phenotypic Changes Critical in Late-Stage Atherosclerotic Lesion Pathogenesis. Circulation. 2020; 142(21):2045-2059. PMC: 7682794. DOI: 10.1161/CIRCULATIONAHA.120.046672. View