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Biology subjects

Andrews, R. M.

Publications and source records attributed to Andrews, R. M..

3 recordsLinked to original sources

Matrisome gene expression is altered during non-allometric heart growth in genetically enlarged Drosophila

The cardiac extracellular matrix (ECM) is critical to defining the biophysical properties of the heart that adapt to changing stresses with growth or disease. The ECM is commonly dysregulated in chronic disease such as hypertension, diabetes, and cardiomyopathies, often leading to the development of fibrosis. There are no treatment options to address most ECM cardiomyopathies, but developing therapeutic targets necessitates an understanding of the regulation of ECM remodelling. Here, we employ a larval Drosophila overgrowth model ("giant larvae") to overload the heart and alter ECM remodelling in vivo. These larvae grow to immense sizes without exhibiting hallmarks of obesity. Remarkably, cardiac ECM organization scales allometrically despite overload. The main effect observed is a change in Collagen fibril thickness, possibly reflecting changes to tension in the system. Overgrowth-induced changes in gene expression similarly suggest changes in Collagen assembly, such as a dramatic increase in LOXL2, the main Collagen crosslinking enzyme. This could indicate that larvae may compensate for the stress of overgrowth by stabilizing the Collagen network. The enlarged hearts of giant larvae cannot contract fully at systole. Taken together, this reveals non-allometric changes to cardiac form and output with increasing body size. Overall, our overgrowth model presents an intriguing opportunity to examine the ability of a system to tolerate overgrowth without the metabolic inputs of obesity.

molecular biology↗

Cardiac function and ECM morphology are altered with high fat diets in Drosophila

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 of in vivo ECM regulation, and how the ECM responds to a systemic challenge. We have therefore developed a Drosophila model 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.

molecular biology↗

Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data

Prevalence of impetigo (skin sores) remains high in remote Australian Aboriginal communities, Fiji, and other areas of socio-economic disadvantage. Skin sore infections, driven primarily in these settings by Group A Streptococcus (GAS) contribute substantially to the disease burden in these areas. Despite this, estimates for the force of infection, infectious period and basic reproductive ratio -- all necessary for the construction of dynamic transmission models -- have not been obtained. By utilising three datasets each containing longitudinal infection information on individuals, we estimate each of these epidemiologically important parameters. With an eye to future study design, we also quantify the optimal sampling intervals for obtaining information about these parameters. We verify the estimation method through a simulation estimation study, and test each dataset to ensure suitability to the estimation method. We find that the force of infection differs by population prevalence, and the infectious period is estimated to be between 12 and 20 days. We also find that optimal sampling interval depends on setting, with an optimal sampling interval between 9 and 11 days in a high prevalence setting, and 21 and 27 days for a lower prevalence setting. These estimates unlock future model-based investigations on the transmission dynamics of GAS and skin sores.

epidemiology↗