Evidence map›Paper›PMID 33589689›Full record

ArticleCommunications biology2021

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

Open access · goldAbstract readVideo-Audio Media
In one paragraph

Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
2.8field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 44 citations in OpenAlex.

  1. Article
  2. Article
  3. Frontiers in aging neuroscience · 2026
    Review
  4. Article
  5. Automated Platforms inMicromachines · 2025
    Review
  6. The dopaminergic system ofRoyal Society open science · 2025
    Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Icariin Improves Stress Resistance and Extends Lifespan inInternational journal of molecular sciences · 2023
    Article
  16. Article
  17. Lab on a chip · 2023
    Article
  18. Article
  19. Biomolecules · 2023
    Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Salman SohrabiDepartment of Molecular Biology & Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA.
Danielle E MorDepartment of Molecular Biology & Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA.
Rachel KaletskyDepartment of Molecular Biology & Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA.ORCID 0000-0002-7176-9154
William KeyesDepartment of Molecular Biology & Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA.
Coleen T MurphyDepartment of Molecular Biology & Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA. ctmurphy@princeton.edu.ORCID 0000-0002-8257-984X
Princeton University · US

Funding

Enhancing and expanding the CGC Strain CollectionP40OD010440 · OD · UNIVERSITY OF MINNESOTA · PI Ann E. Rougvie · 2012 to 2026
$7.5M
Toward the Tissue-ome: A Map of the C. elegans Cell-specific TranscriptomeDP1GM119167 · NIGMS · PRINCETON UNIVERSITY · PI MURPHY, COLEEN TARA · 2015 to 2019
$4.0M
Systems Modeling of Alzheimers Disease in C. elegansRF1AG057341 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MURPHY, COLEEN TARA, SUH, YOUSIN · 2017 to 2017
$3.7M
Molecular Mechanisms Regulating Age-Related Cognitive Decline in C. elegansR01AG034446 · NIA · PRINCETON UNIVERSITY · PI MURPHY, COLEEN TARA · 2009 to 2019
$3.1M
The role of branched chain amino acid metabolism in the regulation of lifespan and cognitive declineF32AG062036 · NIA · PRINCETON UNIVERSITY · PI MOR, DANIELLE EMILLE · 2019 to 2019
$53k
NIA NIH HHS F32 AG062036NIA NIH HHS R01 AG034446NIA NIH HHS RF1 AG057341NIGMS NIH HHS DP1 GM119167NIH HHS P40 OD010440
6 · The paper itself

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.

Indexed as

Drug RepositioningHigh-Throughput Screening AssaysPostureAnimalsAntiparkinson AgentsBehavior, AnimalCaenorhabditis elegansCaenorhabditis elegans ProteinsImage Interpretation, Computer-AssistedMachine LearningNeural Networks, ComputerProof of Concept StudyRNA InterferenceTransaminasesWorkflowAntiparkinson Agentsbcat-1 protein, C elegansCaenorhabditis elegans ProteinsTransaminases

Identifiers

PMID33589689
PMCPMC7884385
OpenAlexW3131675026

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.