Kexin Kong

Thesis title: Leveraging large-scale CRISPR mutagenesis in zebrafish to understand fat distribution and obesity-associated disease

Biography

CV

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Qualifications

MSc Applied Genomics, Imperial College London

BSc (Hons) Aquaculture, Ocean University of China

 

 

Research summary

Obesity affects approximately 25% of all UK adults and is a primary risk factor for atherosclerotic cardiovascular disease, diabetes, cancer and severe COVID-19 symptoms. Among obese patients, there is large inter-individual variation in risk for developing obesity-associated diseases. Therefore, predicting and stratifying obese patients according to disease risk, and understanding the causal mechanisms driving this disease risk, are essential prerequisites for developing effective obesity treatments.

Body fat distribution is a key determinant of obesity-associated disease risk. In similarly obese individuals, increased accumulation of visceral adipose tissue is associated with cardiometabolic disorders, whereas increased levels of subcutaneous adipose is associated with more metabolically healthy obesity. Indeed, fat distribution is a more accurate predictor of disease risk than obesity alone. Body fat distribution is highly heritable, suggesting a strong genetic basis, and over 96 genome-wide association studies (GWAS) have identified >9,000 genetic associations with waist-hip ratio (a proxy for fat distribution). Mechanistically understanding how these genetic associations impact fat distribution and cardiometabolic disease risk represents a significant hurdle, but will inform patient complexity in obesity and enable more targeted and effective strategies for obesity-associated disease prevention and treatment. I am interested in translating findings from GWAS into molecular, cellular and physiological fat distribution phenotypes. By doing so, I aim to understand the genetics of fat distribution in the context of obesity-associated disease.

Current research interests

My current research focus on generating a prioritized list of candidate fat distribution genes, differentially expressed across adipose tissues and associated with beneficial/detrimental cardiometabolic traits through bioinformatics. I will leverage GWAS summary statistics for waist-hip ratio (fat distribution), together with visceral/subcutaneous adipose eQTL data to identify candidate fat distribution gene targets. Gene targets will then be prioritized by (i) assessing differential expression between human visceral and subcutaneous adipose, and (ii) correlating with 23 cardiometabolic-related physiological traits.

Past research interests

MSc project: Identification and functional characterization of genes implicated in obesity-induced metabolic dysfunction in human adipocytes BSc project: Whether knockdown of gamma-secretase activating protein (gsap) would reduce the accumulation of neurotoxic amyloid in zebrafish

Project activity

MRC Precision Medicine Doctoral Training Programme

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