J Andrew Hardaway1, Jing Wang2, Paul A Fleming3, Katherine A Fleming3, Sarah M Whitaker1, Alex Nackenoff1, Chelsea L Snarrenberg1, Shannon L Hardie1, Bing Zhang2, Randy D Blakely4. 1. Department of Pharmacology, Nashville, TN 37232-8548, USA. 2. Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232-8548, USA. 3. Departments of Electrical Engineering and Computer Science, Vanderbilt University School of Medicine, Nashville, TN 37232-8548, USA. 4. Department of Pharmacology, Nashville, TN 37232-8548, USA; Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN 37232-8548, USA. Electronic address: randy.blakely@vanderbilt.edu.
Abstract
BACKGROUND: The nematode Caenhorhabditis elegans offers great power for the identification and characterization of genes that regulate behavior. In support of this effort, analytical methods are required that provide dimensional analyses of subcomponents of behavior. Previously, we demonstrated that loss of the presynaptic dopamine (DA) transporter, dat-1, evokes DA-dependent Swimming-Induced Paralysis (Swip) (Mcdonald et al., 2007), a behavior compatible with forward genetic screens (Hardaway et al., 2012). NEW METHOD: Here, we detail the development and implementation of SwimR, a set of tools that provide for an automated, kinetic analysis of C. elegans Swip. SwimR relies on open source programs that can be freely implemented and modified. RESULTS: We show that SwimR can display time-dependent alterations of swimming behavior induced by drug-treatment, illustrating this capacity with the dat-1 blocker and tricyclic antidepressant imipramine (IMI). We demonstrate the capacity of SwimR to extract multiple kinetic parameters that are impractical to obtain in manual assays. COMPARISON WITH EXISTING METHODS: Standard measurements of C. elegans swimming utilizes manual assessments of the number of animals exhibiting swimming versus paralysis. Our approach deconstructs the time course and rates of movement in an automated fashion, offering a significant increase in the information that can be obtained from swimming behavior. CONCLUSIONS: The SwimR platform is a powerful tool for the deconstruction of worm thrashing behavior in the context of both genetic and pharmacological manipulations that can be used to segregate pathways that underlie nematode swimming mechanics.
BACKGROUND: The nematode Caenhorhabditis elegans offers great power for the identification and characterization of genes that regulate behavior. In support of this effort, analytical methods are required that provide dimensional analyses of subcomponents of behavior. Previously, we demonstrated that loss of the presynaptic dopamine (DA) transporter, dat-1, evokes DA-dependent Swimming-Induced Paralysis (Swip) (Mcdonald et al., 2007), a behavior compatible with forward genetic screens (Hardaway et al., 2012). NEW METHOD: Here, we detail the development and implementation of SwimR, a set of tools that provide for an automated, kinetic analysis of C. elegansSwip. SwimR relies on open source programs that can be freely implemented and modified. RESULTS: We show that SwimR can display time-dependent alterations of swimming behavior induced by drug-treatment, illustrating this capacity with the dat-1 blocker and tricyclic antidepressant imipramine (IMI). We demonstrate the capacity of SwimR to extract multiple kinetic parameters that are impractical to obtain in manual assays. COMPARISON WITH EXISTING METHODS: Standard measurements of C. elegans swimming utilizes manual assessments of the number of animals exhibiting swimming versus paralysis. Our approach deconstructs the time course and rates of movement in an automated fashion, offering a significant increase in the information that can be obtained from swimming behavior. CONCLUSIONS: The SwimR platform is a powerful tool for the deconstruction of worm thrashing behavior in the context of both genetic and pharmacological manipulations that can be used to segregate pathways that underlie nematode swimming mechanics.
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Authors: J Andrew Hardaway; Sarah M Sturgeon; Chelsea L Snarrenberg; Zhaoyu Li; X Z Shawn Xu; Daniel P Bermingham; Peace Odiase; W Clay Spencer; David M Miller; Lucia Carvelli; Shannon L Hardie; Randy D Blakely Journal: J Neurosci Date: 2015-06-24 Impact factor: 6.167
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Authors: Sarah B Robinson; Osama Refai; J Andrew Hardaway; Sarah Sturgeon; Tessa Popay; Daniel P Bermingham; Phyllis Freeman; Jane Wright; Randy D Blakely Journal: PLoS One Date: 2019-05-13 Impact factor: 3.240