The Ancient Genetic Networks of Obesity: Whole-Animal Automated Screening for Conserved Fat Regulators

  • Wenfan Ke
  • Anna Drangowska-Way
  • Daniel Katz
  • Karsten Siller
  • Eyleen J. O’Rourke
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1787)

Abstract

Caenorhabditis elegans is the first and only metazoan model that enables whole-body gene knockdown by simply feeding their standard laboratory diet, E. coli, carrying RNA interference (RNAi)-expressing constructs. The simplicity of the RNAi treatment, small size, and fast reproduction rate of C. elegans allow us to perform whole-animal high-throughput genetic screens in wild-type, mutant, or otherwise genetically modified C. elegans. In addition, more than 65% of C. elegans genes are conserved in mammals including human. In particular, C. elegans metabolic pathways are highly conserved, which supports the study of complex diseases such as obesity in this genetically tractable model system. In this chapter, we present a detailed protocol for automated high-throughput whole-animal RNAi screening to identify the pathways promoting obesity in diet-induced and genetically driven obese C. elegans. We describe an optimized high-content screening protocol to score fat mass and body fat distribution in whole animals at large scale. We provide optimized pipelines to automatically score phenotypes using the open-source CellProfiler platform within the context of supercomputer clusters. Further, we present a guideline to optimize information workflow from the automated microscope to a searchable database. The approaches described here enable unveiling the whole network of gene-gene and gene-environment interactions that define metabolic health or disease status in this proven model of human disease, but similar principles can be applied to other disease models.

Key words

Whole-animal screening C. elegans Genetics of obesity Automated microscopy Live screening Fat screening 

Notes

Acknowledgments

We want to thank Carolina Wählby, Anne Carpenter, Jonah Larkins-For, and Annie Lee-Conery for their contributions to development of the earlier versions of these protocols. We would like to thank Wei Ma for practical advice on developing the assays. We also thank the University of Virginia Advanced Research Computing Services for support on Rivanna HPC cluster usage. This work would not have been possible without the generous support of the W. M. Keck Foundation.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Wenfan Ke
    • 1
  • Anna Drangowska-Way
    • 1
  • Daniel Katz
    • 2
  • Karsten Siller
    • 3
  • Eyleen J. O’Rourke
    • 1
  1. 1.Department of BiologyUniversity of VirginiaCharlottesvilleUSA
  2. 2.Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleUSA
  3. 3.Advanced Computing ServicesUniversity of VirginiaCharlottesvilleUSA

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