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

1 Scopus citations

Abstract

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
Volume4
Issue number1
DOIs
StatePublished - Dec 2021

All Science Journal Classification (ASJC) codes

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

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