Functionally characterizing obesity-susceptibility genes using CRISPR/Cas9, in vivo imaging and deep learning

Area of Science:

• Genetics and Developmental Biology
• Cardiovascular and Metabolic Diseases
• Bioimaging and Computational Biology

Background:

• Hundreds of genetic loci are linked to obesity, but understanding gene function is challenging.
• Functional characterization of obesity-associated genes requires robust and scalable models.
• Adipocyte lipid accumulation and cardiometabolic traits are key areas for obesity research.

Purpose of the Study:

• To systematically characterize candidate obesity genes using a novel zebrafish larvae pipeline.
• To investigate the role of specific genes in lipid accumulation and other cardiometabolic traits.
• To establish a method for early functional screening of obesity-related genes.

Main Methods:

• Development of a CRISPR/Cas9 gene editing pipeline in zebrafish larvae.
• Non-invasive, semi-automated fluorescence imaging for lipid detection.
• Deep learning-based image analysis for quantitative assessment of traits.
• Dietary overfeeding intervention to induce lipid accumulation.

Main Results:

• Overfeeding at 5 days post-fertilization (dpf) increases adipocyte lipid accumulation by 10 dpf.
• CRISPR/Cas9 mutations in 12-16 obesity genes did not affect adipocyte lipid accumulation at 10 dpf.
• Mutations impacted ectopic lipid accumulation in vasculature and liver, body size, cholesterol levels, and pancreatic beta cell function.
• Observed effects on food intake were inconsistent with mammalian studies.

Conclusions:

• CRISPR/Cas9 and image-based screening in zebrafish larvae can effectively identify direct effects of obesity genes on cardiometabolic traits.
• This model can reveal gene functions related to cardiometabolic health independent of adiposity effects at early stages.
• The study provides a powerful platform for dissecting the genetic underpinnings of obesity and related disorders.