Introduction: Cardiac rehabilitation (CR) is a primary prescribed treatment for a variety of cardiovascular disease states, including: coronary artery disease, percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG), myocardial infarction (MI), and heart failure. For this reason, exercise prescription guidelines for cardiac patients have been established. However, it is unclear how these guidelines are being administered at cardiac rehabilitation centers. The purpose of this study is to assess current exercise prescription techniques at cardiac rehabilitation clinics across several Midwest states in the United States. Methods: Fifty-eight CR programs from Michigan, Indiana, Illinois, Minnesota, Wisconsin, and Ohio were administered a questionnaire assessing clinic characteristics, aerobic and resistance exercise prescription techniques. Results: Most reported patient types were PCI, CABG, and MI. Clinical exercise physiologists were the primary exercise prescription writers (81%). Only 32% of the clinics required a clinical certification. Baseline stress tests prior to CR were performed in 33% of programs. Rating of Perceived Exertion (RPE) was the most commonly used indicator of exercise intensity, followed by heart rate reserve (HRR), and METs. Resistance exercise was practiced in 89% of CR programs. The most common intensity indicator was trial and error, and RPE. Conclusion: Results demonstrate exercise prescription variability among CR programs. This emphasizes the complexity and expertise among clinical exercise physiologists. These results also highlight the importance that academic programs place on training students across all prescription techniques, and utilization of research-based prescription guidelines published by professional organizations.

Cardiovascular disease is characterized by aberrant and excessive extracellular matrix (ECM) remodelling, termed fibrosis. Fibrotic remodelling is typically triggered by inflammation, which occurs systemically in obesity. Despite the contribution of fibrosis to adverse clinical outcomes and disease progression, there are no available treatments for this condition. Developing therapeutics for chronic conditions requires an understanding ofin vivoECM regulation, and how the ECM responds to a systemic challenge. We have therefore developed aDrosophilamodel for obesity via chronic high fat diet feeding and evaluated the response of the cardiac ECM to this metabolic challenge. We found that this model displays a striking disorganization of the cardiac ECM, with corresponding deficits in heart function. Our study shows that different genotypes tolerate varying levels of high fat diets, and that some genotypes may require a different percentage of fat supplementation for achieving an optimal obesity phenotype.

Identifying the genetic architecture of complex traits is of interest to many geneticists, including those interested in human disease, plant and animal breeding and evolutionary genetics. Despite advances in sequencing technologies and GWAS statistical methods improving our ability to identify variants with smaller effect sizes, many of these identified polymorphisms fail to be replicated in subsequent studies. In addition to sampling variation, this reflects the complexities introduced by factors including environmental variation, genetic background and differences in allele frequencies among populations. Using Drosophila melanogaster wing shape, we ask if we can replicate allelic effects of polymorphisms first identified in a GWAS (Pitchers et al. 2019) in three genes: dachsous (ds), extra-macrochaete (emc) and neuralized (neur), using artificial selection in the lab and bulk segregant mapping in natural populations. We demonstrate that shape changes associated with these genes is aligned with major axes of phenotypic and genetic variation in natural populations. Following 7 generations of artificial selection along ds and emc shape change vectors, we observe genetic differentiation of variants in ds and in genomic regions with other genes in the hippo signaling pathway, indicating available genetic diversity of a population summarized in G influences alleles captured by selection. Despite the success with artificial selection, bulk segregant analysis using natural populations did not detect these same variants, likely due to the contribution of environmental variation, low minor allele frequencies coupled with small effect sizes of the contributing variants.

Identifying the genetic architecture of complex traits is important to many geneticists, including those interested in human disease, plant and animal breeding, and evolutionary genetics. Advances in sequencing technology and statistical methods for genome-wide association studies (GWAS) have allowed for the identification of more variants with smaller effect sizes, however, many of these identified polymorphisms fail to be replicated in subsequent studies. In addition to sampling variation, this failure to replicate reflects the complexities introduced by factors including environmental variation, genetic background, and differences in allele frequencies among populations. Using Drosophila melanogaster wing shape, we ask if we can replicate allelic effects of polymorphisms first identified in a GWAS (Pitchers et al. 2019) in three genes: dachsous (ds), extra-macrochaete (emc) and neuralized (neur), using artificial selection in the lab, and bulk segregant mapping in natural populations. We demonstrate that multivariate wing shape changes associated with these genes are aligned with major axes of phenotypic and genetic variation in natural populations. Following seven generations of artificial selection along the ds shape change vector, we observe genetic differentiation of variants in ds and genomic regions containing other genes in the hippo signaling pathway. This suggests a shared direction of effects within a developmental network. We also performed artificial selection with the emc shape change vector, which is not a part of the hippo signaling network, but showed a largely shared direction of effects. The response to selection along the emc vector was similar to that of ds, suggesting that the available genetic diversity of a population, summarized by the genetic (co)variance matrix (G), influenced alleles captured by selection. Despite the success with artificial selection, bulk segregant analysis using natural populations did not detect these same variants, likely due to the contribution of environmental variation and low minor allele frequencies, coupled with small effect sizes of the contributing variants.