Molecular modelling of the links between growth, environment and metabolism using Grb10 knockdown in Zebrafish.

与环境有关的生长和新陈代谢的协调控制是生物有机体的一个基本特征。对控制这些过程的基因网络之间的相互作用进行表征，可以对这些途径进行可能的操作，以促进对农业重要的更好的生长曲线，以及对医学重要的健康衰老的表征。我们最近发现，GRB10是一个重要的基因，通过暴露在夏季日光下来衡量纬度，连接生长和新陈代谢。Grb10是一种调节胰岛素和IGF-I受体信号的接头蛋白。我们已经证明，斑马鱼中瞬时的Grb10基因敲除会导致生长速度增加，体重与长度的比例降低，耗氧量增加，这意味着斑马鱼的表型更“瘦”，代谢更活跃。该项目将确定Grb10基因敲除产生这种表型并改变与光照相关的代谢率的机制。该项目在体内将整个动物的代谢呼吸测量和生长表型的测量与转录数据的计算和数学分析联系起来，以了解生长期如何与代谢率协调并受到环境决定因素的影响。候选人将学习如何使用分子生物学中的传统板凳技能，结合动物处理，使用对照和CRISPR改良动物对生长和新陈代谢进行准确的表型测量。然后，这些观察结果将与使用机器学习方法的转录数据联系起来。将与存放在ZFIN、gene Expression Omnibus和ArrayExpress等数据库中的现有数据进行比较。因此，这个项目将教学生“常规地将计算和数学技术应用于高质量的定量生物数据”，以及如何按照BBSRC的要求访问和利用“综合的、集成的和可互操作的数据资源”。

The co-ordinated control of growth and metabolism in relation to the environment is an essential feature of living organisms. Characterisation of the interactions between the networks of genes that control these processes allows possible manipulation of these pathways to facilitate both better growth profiles, important for agriculture, and the characterisation of healthy ageing, important for medicine.We have recently identified GRB10 as an important gene linking growth and metabolism in response to latitude as measured by exposure to summer daylight1. Grb10 is an adaptor protein modulating insulin and IGF-I receptor signalling. We have shown 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 modifies metabolic rate in relation to exposure to light.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 and affected by environmental determinants. 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. This project will, therefore, teach the student to "routinely apply computational and mathematical techniques to high-quality quantitative biological data" and how to access and utilise "comprehensive, integrated and interoperable data resources" as required by the BBSRC.