A review of standardized high-throughput cardiovascular phenotyping with a link to metabolism in mice.
Jiri LindovskyZuzana NichtovaNathalia R V DraganoDavid Pajuelo RegueraJan ProchazkaHelmut FuchsSusan MarschallValérie Gailus-DurnerRadislav SedlacekMartin Hrabě de AngelisJan RozmanNadine SpielmannPublished in: Mammalian genome : official journal of the International Mammalian Genome Society (2023)
Cardiovascular diseases cause a high mortality rate worldwide and represent a major burden for health care systems. Experimental rodent models play a central role in cardiovascular disease research by effectively simulating human cardiovascular diseases. Using mice, the International Mouse Phenotyping Consortium (IMPC) aims to target each protein-coding gene and phenotype multiple organ systems in single-gene knockout models by a global network of mouse clinics. In this review, we summarize the current advances of the IMPC in cardiac research and describe in detail the diagnostic requirements of high-throughput electrocardiography and transthoracic echocardiography capable of detecting cardiac arrhythmias and cardiomyopathies in mice. Beyond that, we are linking metabolism to the heart and describing phenotypes that emerge in a set of known genes, when knocked out in mice, such as the leptin receptor (Lepr), leptin (Lep), and Bardet-Biedl syndrome 5 (Bbs5). Furthermore, we are presenting not yet associated loss-of-function genes affecting both, metabolism and the cardiovascular system, such as the RING finger protein 10 (Rfn10), F-box protein 38 (Fbxo38), and Dipeptidyl peptidase 8 (Dpp8). These extensive high-throughput data from IMPC mice provide a promising opportunity to explore genetics causing metabolic heart disease with an important translational approach.
Keyphrases
- high throughput
- cardiovascular disease
- high fat diet induced
- healthcare
- genome wide
- left ventricular
- binding protein
- adipose tissue
- cardiovascular events
- primary care
- heart failure
- insulin resistance
- wild type
- pulmonary hypertension
- small molecule
- skeletal muscle
- type diabetes
- copy number
- amino acid
- atrial fibrillation
- protein protein
- electronic health record
- social media
- deep learning