Selected Grantee Publications
- Clear All
- 4 results found
- Cardiovascular
- 2023
The Power of the Heterogeneous Stock Rat Founder Strains in Modeling Metabolic Disease
Wagner et al., Endocrinology. 2023.
https://pubmed.ncbi.nlm.nih.gov/37882530/
Metabolic diseases are a host of complex conditions, including obesity, diabetes mellitus, and metabolic syndrome. Endocrine control systems (e.g., adrenals, thyroid, gonads) are causally linked to metabolic health outcomes. In this study, investigators determined novel metabolic and endocrine health characteristics in both sexes of six available substrains similar to the N/NIH Heterogeneous Stock (HS) rat founders. This deep-phenotyping protocol provides new insight into the exceptional potential of the HS rat population to model complex metabolic health states. The following hypothesis was tested: The genetic diversity in the HS rat founder strains represents a range of endocrine health conditions contributing to the diversity of cardiometabolic disease risks exhibited in the HS rat population. Supported by ORIP (R24OD024617), NHLBI, NIGMS and NIDDK.
Zebrafish as a High Throughput Model for Organ Preservation and Transplantation Research
Da Silveira Cavalcante et al., The FASEB Journal. 2023.
https://faseb.onlinelibrary.wiley.com/doi/10.1096/fj.202300076R
Organ transplantation increases the quality of life and life expectancy of patients with chronic end-stage diseases, but the preservation of organs for transplantation remains a significant barrier. In the current study, researchers demonstrate the value of zebrafish as a high-throughput model organism in the fields of solid-organ preservation and transplantation, with a focus on heart preservation via partial freezing. Their techniques have the potential to advance research in the fields of cryobiology and solid-organ transplantation. Supported by ORIP (R24OD031955) and NHLBI.
A Comprehensive Drosophila Resource to Identify Key Functional Interactions Between SARS-CoV-2 Factors and Host Proteins
Guichard et al., Cell Reports. 2023.
https://pubmed.ncbi.nlm.nih.gov/37480566/
To address how interactions between SARS-CoV-2 factors and host proteins affect COVID-19 symptoms, including long COVID, and facilitate developing effective therapies against SARS-CoV-2 infections, researchers reported the generation of a comprehensive set of resources, mainly genetic stocks and a human cDNA library, for studying viral–host interactions in Drosophila. Researchers further demonstrated the utility of these resources and showed that the interaction between NSP8, a SARS-CoV-2 factor, and ATE1 arginyltransferase, a host factor, causes actin arginylation and cytoskeleton disorganization, which may be relevant to several pathogenesis processes (e.g., coagulation, cardiac inflammation, fibrosis, neural damage). Supported by ORIP (R24OD028242, R24OD022005, R24OD031447), NIAID, NICHD, NIGMS, and NINDS.
A Deep Learning Platform to Assess Drug Proarrhythmia Risk
Serrano et al., Cell Stem Cell. 2023.
https://www.sciencedirect.com/science/article/pii/S1934590922004866?via%3Dihub=
Investigators trained a convolutional neural network (CNN) classifier to learn and ultimately identify features of in vitro action potential recordings of human induced pluripotent stem cell (iPSC)–derived cardiomyocytes (hiPSC-CMs) that are associated with lethal Torsade de Pointes arrhythmia. The CNN classifier accurately predicted the risk of drug-induced arrhythmia. The risk profiles of the test drugs were similar across hiPSC-CMs derived from different healthy donors. In addition, pathogenic mutations that cause arrhythmogenic cardiomyopathies in patients significantly increased the proarrhythmic propensity to certain intermediate and high‑risk drugs in the hiPSC-CMs. These data indicate that deep learning can identify in vitro arrhythmic features that correlate with clinical arrhythmia and discern the influence of patient genetics on the risk of drug-induced arrhythmia. Supported by ORIP (S10OD030264) and NHLBI.