Knockout mice are an important tool for human monogenic heart disease studies.
Pilar CacheiroNadine SpielmannHamed Haseli MashhadiHelmut FuchsValérie Gailus-DurnerDamian SmedleyMartin Hrabě de AngelisPublished in: Disease models & mechanisms (2023)
Mouse models are relevant to studying the functionality of genes involved in human diseases; however, translation of phenotypes can be challenging. Here, we investigated genes related to monogenic forms of cardiovascular disease based on the Genomics England PanelApp and aligned them to International Mouse Phenotyping Consortium (IMPC) data. We found 153 genes associated with cardiomyopathy, cardiac arrhythmias or congenital heart disease in humans, of which 151 have one-to-one mouse orthologues. For 37.7% (57/151), viability and heart data captured by electrocardiography, transthoracic echocardiography, morphology and pathology from embryos and young adult mice are available. In knockout mice, 75.4% (43/57) of these genes showed non-viable phenotypes, whereas records of prenatal, neonatal or infant death in humans were found for 35.1% (20/57). Multisystem phenotypes are common, with 58.8% (20/34) of heterozygous (homozygous lethal) and 78.6% (11/14) of homozygous (viable) mice showing cardiovascular, metabolic/homeostasis, musculoskeletal, hematopoietic, nervous system and/or growth abnormalities mimicking the clinical manifestations observed in patients. These IMPC data are critical beyond cardiac diagnostics given their multisystemic nature, allowing detection of abnormalities across physiological systems and providing a valuable resource to understand pleiotropic effects.
Keyphrases
- congenital heart disease
- endothelial cells
- cardiovascular disease
- left ventricular
- electronic health record
- end stage renal disease
- young adults
- big data
- heart failure
- chronic kidney disease
- pulmonary hypertension
- newly diagnosed
- mouse model
- pregnant women
- genome wide
- high fat diet induced
- induced pluripotent stem cells
- early onset
- pluripotent stem cells
- computed tomography
- prognostic factors
- peritoneal dialysis
- gene expression
- bone marrow
- single cell
- genome wide identification
- atrial fibrillation
- machine learning
- patient reported outcomes
- bioinformatics analysis
- label free
- dna methylation
- artificial intelligence
- case control
- transcription factor
- single molecule