High-throughput behavioral screen in C. elegans reveals Parkinson’s disease drug candidates

Salman Sohrabi, Danielle E. Mor, Rachel Kaletsky, William Keyes, Coleen T. Murphy

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


We recently linked branched-chain amino acid transferase 1 (BCAT1) dysfunction with the movement disorder Parkinson’s disease (PD), and found that RNAi-mediated knockdown of neuronal bcat-1 in C. elegans causes abnormal spasm-like ‘curling’ behavior with age. Here we report the development of a machine learning-based workflow and its application to the discovery of potentially new therapeutics for PD. In addition to simplifying quantification and maintaining a low data overhead, our simple segment-train-quantify platform enables fully automated scoring of image stills upon training of a convolutional neural network. We have trained a highly reliable neural network for the detection and classification of worm postures in order to carry out high-throughput curling analysis without the need for user intervention or post-inspection. In a proof-of-concept screen of 50 FDA-approved drugs, enasidenib, ethosuximide, metformin, and nitisinone were identified as candidates for potential late-in-life intervention in PD. These findings point to the utility of our high-throughput platform for automated scoring of worm postures and in particular, the discovery of potential candidate treatments for PD.

Original languageEnglish (US)
Article number203
JournalCommunications Biology
Issue number1
StatePublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine (miscellaneous)


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