Modelling the developmental mechanisms of metabolic disease using Grb10 knockdown in Zebrafish

Obesity and its associated metabolic dysfunction are a major cause of morbidity and mortality. Therapies are available to treat the metabolic consequences of obesity but a better strategy would be to control weight gain early in life in our current obesogenic environment.

GRB10 is an adaptor protein modulating insulin and IGF-I receptor signalling. We have recently identified that transient Grb10 knockdown in Zebrafish generates an increase in growth rate with a reduced weight-to-length ratio and that oxygen consumption is increased, implying a “leaner” more metabolically active phenotype. This project will identify the mechanisms through which Grb10 knockdown produces this phenotype and the key molecules that could be targets for therapies that could modify metabolic rate.
Hypothesis: The metabolically healthy growth trajectory associated with Grb10 knockdown in zebrafish directly relates to pathways that regulate linear growth, weight gain and metabolic activity.

Objectives:
• Compare activity in metabolic pathways in Grb10 KO fish and control fish in early life with adult fish using transcriptomic modelling.
• Measure and compare O2 consumption in early life with adulthood using respirometry.
• Assess markers of cardiometabolic health in Grb10 KO fish and controls in relation to dietary manipulation.

A CRISPR/Cas9 Grb10 knockdown in zebrafish will be generated using routine procedures. Time series analysis of ‘omic data using network models will be performed over extremes of metabolic phenotype (upper and lower tertile) as defined by weight to length ratio measurements and respirometry. This analysis will allow the identification of mechanistic regulatory network elements that can be assessed using further KO work and/or chemical inhibition. Physiological and anatomical markers of cardiometabolic health will be assessed and the effect of high-calorie and high-cholesterol diets will be determined.

Outcomes/Impact:
• Generation of zebrafish models of weight control
• Identification of the regulatory mechanisms associated with weight control

This project links in vivo whole animal metabolic respirometry and measurements of growth phenotype with the computational and mathematical analysis of transcriptomic data to understand how periods of growth are co-ordinated with metabolic rate. The candidate will learn how to use traditional bench skills in molecular biology, in combination with animal handling, to make accurate phenotypic measurements of growth and metabolism using both control and CRISPR modified animals. These observations will then be linked with transcriptomic data using machine learning approaches. Comparisons will be made to available data deposited in databases such as Zfin, Gene Expression Omnibus and ArrayExpress.

Applicants should hold (or expect to obtain) a minimum upper-second honours degree (or equivalent) in a relevant subject area. A Masters qualification in a similar area and/or experience in molecular biology, animal handling, and computational biology would be a significant advantage.