Zebrafish Behavioral Profiling Links Drugs to Biological Targets and Rest/Wake Regulation

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Science  15 Jan 2010:
Vol. 327, Issue 5963, pp. 348-351
DOI: 10.1126/science.1183090

Behavioral Profiling

The complexity of the brain makes it difficult to predict how a drug will affect behavior without direct testing in live animals. Rihel et al. (p. 348) developed a high-throughput assay to assess the effects of thousands of drugs on sleep/wake behaviors of zebrafish larvae. The data set reveals a broad conservation of zebrafish and mammalian sleep/wake pharmacology and identifies pathways that regulate sleep. Moreover, the biological targets of poorly characterized small molecules can be predicted by matching their behavioral profiles to those of well-known drugs. Thus, behavioral profiling in zebrafish offers a cost-effective way to characterize neuroactive drugs and to predict biological targets of novel compounds.


A major obstacle for the discovery of psychoactive drugs is the inability to predict how small molecules will alter complex behaviors. We report the development and application of a high-throughput, quantitative screen for drugs that alter the behavior of larval zebrafish. We found that the multidimensional nature of observed phenotypes enabled the hierarchical clustering of molecules according to shared behaviors. Behavioral profiling revealed conserved functions of psychotropic molecules and predicted the mechanisms of action of poorly characterized compounds. In addition, behavioral profiling implicated new factors such as ether-a-go-go–related gene (ERG) potassium channels and immunomodulators in the control of rest and locomotor activity. These results demonstrate the power of high-throughput behavioral profiling in zebrafish to discover and characterize psychotropic drugs and to dissect the pharmacology of complex behaviors.

Most current drug discovery efforts focus on simple in vitro screening assays. Although such screens can be successful, they cannot recreate the complex network interactions of whole organisms. These limitations are particularly acute for psychotropic drugs because brain activity cannot be modeled in vitro (13). Motivated by recent small-molecule screens that probed zebrafish developmental processes (47), we developed a whole organism, high-throughput screen for small molecules that alter larval zebrafish locomotor behavior. We used an automated rest/wake behavioral assay (3, 8) to monitor the activity of larvae exposed to small molecules at 10 to 30 μM for 3 days (Fig. 1A) (3). Multiple behavioral parameters were measured, including the number and duration of rest bouts, rest latency, and waking activity (i.e., activity not including time spent at rest) (Fig. 1B) (3). We screened 5648 compounds representing 3968 unique structures and 1680 duplicates and recorded more than 60,000 behavioral profiles. Of these, 547 compounds representing 463 unique structures significantly altered behavior relative to controls, according to a stringent statistical cutoff (3).

Fig. 1

Larval zebrafish locomotor activity assay. (A) At 4 days post-fertilization (dpf), an individual zebrafish larva is pipetted into each well of a 96-well plate with small molecules. Automated analysis software tracks the movement of each larva for 3 days. Each compound is tested on 10 larvae. (B) Locomotor activity of a representative larva. The rest and wake dynamics were recorded, including the number and duration of rest bouts [i.e., a continuous minute of inactivity (8)], the timing of the first rest bout after a light transition (rest latency), the average waking activity (average activity excluding rest bouts), and the average total activity. Together, these measurements generate a behavioral fingerprint for each compound.

Because the alterations in behavior were multidimensional and quantitative, we assigned a behavioral fingerprint to each compound and applied clustering algorithms to organize molecules according to their fingerprints (Fig. 2A and figs. S1 to S3). This analysis organized the data set broadly into arousing and sedating compounds and identified multiple clusters corresponding to specific phenotypes (Fig. 2, B to F; Fig. 3, A to C; Fig. 4, B and C; and figs. S1 to S4). Clustering allowed us to address three questions: (i) Do structural, functional, and behavioral profiles overlap? (ii) Does the data set predict links between known and unknown small molecules and their mechanisms of action? (iii) Does the data set identify unexpected candidate pathways that regulate rest/wake states?

Fig. 2

Hierarchical clustering reveals the diversity of drug-induced behaviors. (A) Behavioral profiles are hierarchically clustered to link compounds to behaviors. Each square of the clustergram represents the average relative value in standard deviations (yellow, higher than controls; blue, lower than controls) for a single behavioral measurement. Dark bars indicate specific clusters analyzed in subsequent figures. (B to F) Normalized waking activity and rest graphs are plotted for behavior-altering compounds (red trace; average of 10 larvae) and representative controls (10 blue traces; average of 10 larvae each). Compounds that altered behavior include the mood stabilizer and antiepileptic drug sodium valproate (B), the psychotomimetic NMDA antagonist L-701324 (C), the sodium channel agonist pesticide DDT (D), the antimalarial halofantrine (E), and the calcium channel blocker methoxyverapamil (F).

Fig. 3

Predicting primary and secondary biological targets for poorly characterized compounds. (A) The pesticide amitraz coclusters with α2-adrenergic agonists. (B) Sinapic acid methyl ether coclusters with NMDA antagonists. (C) MRS-1220 coclusters with MAO inhibitors. (D) MRS-1220 inhibits MAO-B activity in an enzymatic assay with an IC50 of ~1 μM. Pargyline is a known MAO-B inhibitor (19). The clusters include repeats from different chemical libraries.

Fig. 4

Unexpected regulators of zebrafish rest/wake states. (A) Podocarpatrien-3-one analogs increase rest latency, the time from light transition to the first rest bout, relative to controls. Error bars represent SEM. (B) Many wake-promoting anti-inflammatory and immunomodulating compounds cocluster (blue, NSAIDs; green, glucocorticoids; pink, PDE inhibitors; yellow, miscellaneous anti-inflammatories; white, no anti-inflammatory annotation). See fig. S17 for an extended list. (C) A cluster of ERG-blocking compounds specifically increases waking activity at night. (D) Rank-sorting the data set by correlation to the ERG blocking cluster results in a significant enrichment of ERG blockers in the top ranks [P < 10–13 by the Kolmogorov-Smirnov statistic (3)]. Black lines indicate known ERG blockers; red indicates high correlation, green indicates low correlation to the ERG cluster. This analysis also detected potential indirect regulators of ERG function, such as the organophosphate coumaphos (marked with an asterisk), which causes long QT through an unknown mechanism (20).

Cluster analysis revealed several lines of evidence that molecules with correlated behavioral phenotypes often shared annotated targets or therapeutic indications (Fig. 2, B to F, and figs. S1 to S4). First, drug pairs were more likely to be correlated if the compounds shared at least one annotated target (median correlation when sharing one target, 0.561 versus 0.297 when sharing zero targets; fig. S5, A and B). Second, analysis of 50 different structural and therapeutic classes revealed that drugs belonging to the same class produced highly correlated behaviors in nearly all cases (fig. S5C and fig. S6) (3). For example, several structurally diverse selective serotonin reuptake inhibitors (SSRIs) similarly reduced waking, and sodium channel agonist insecticides induced large increases in waking activity (fig. S5C and fig. S6). Third, behavioral profiling uncovered the polypharmacology of drugs with multiple targets. For example, the profile of the dopamine reuptake inhibitor and muscarinic acetylcholine receptor antagonist 3α-bis-(4-fluorophenyl) methoxytropane correlated only with drugs that also shared both properties, such as the anti-Parkinson’s drug trihexyphenidyl (fig. S7) (3). Fourth, modulators of the major neurotransmitter pathways often induced locomotor and rest/wake effects in zebrafish larvae similar to those seen in mammals (figs. S8 to S15) (3). For example, α2-adrenergic receptor agonists (e.g., clonidine) were sedating, whereas β-adrenergic agonists (e.g., clenbuterol) were arousing, as in mammals (fig. S8). These analyses indicate that compounds with shared biological targets yield similar and conserved phenotypes in our high-throughput behavioral profiling.

Detailed analyses revealed that the clustering of well-known and poorly characterized drugs could predict targets for compounds whose mode of action has been unclear (Fig. 3). For example, the pesticide amitraz coclustered with α2-adrenergic agonists (Fig. 3A), consistent with reports that amitraz causes clonidine-like side effects in mammals and binds to α2-adrenergic receptors (9). Similarly, sinapic acid methyl ether coclustered with N-methyl-d-aspartate (NMDA) receptor antagonists (Fig. 3B), which suggests that the mild anxiolytic effect of sinapic acid in mice is due to NMDA receptor antagonism rather than γ-aminobutyric acid (GABA) receptor activation, as proposed (10). Indeed, several sinapic acid analogs are known to block NMDA-induced excitotoxicity in vitro (11, 12). Finally, MRS-1220, an adenosine A3 receptor antagonist (13), clustered with monoamine oxidase (MAO) inhibitor antidepressants (Fig. 3C). To directly test whether MRS-1220 inhibits MAO, we performed an in vitro activity assay and found a median inhibitory concentration (IC50) of ~1 μM (Fig. 3D). Thus, behavioral profiling in zebrafish larvae can predict and identify targets of poorly characterized compounds.

In addition to revealing a conserved neuropharmacology between zebrafish and mammalian rest/wake states (figs. S8 to S15) (3), behavioral profiling identified additional pathways involved in rest/wake behaviors:

1) L-type calcium channel inhibitors of the verapamil class increased rest with minimal effects on waking activity (fig. S16). This is likely a direct effect on rest regulation, because average waking activity and associated muscle activity were unaffected.

2) Cluster analysis identified two structurally related podocarpatrien-3-ones that specifically increased rest latency (Fig. 4A). These and other compounds also revealed that total rest, rest latency, and waking activity can be disassociated, indicating that these processes can be regulated by distinct mechanisms (3).

3) Although inflammatory cytokine signaling has long been known to promote sleep during infection, a role for the immune system in normal vertebrate sleep/wake behavior has not been described (14). Behavioral profiling revealed that a diverse set of anti-inflammatory compounds increased waking activity during the day with much less effect at night (Fig. 4B and fig. S17). These anti-inflammatory compounds included the steroidal glucocorticoids, the nonsteroidal anti-inflammatory drugs (NSAIDs), phosphodiesterase (PDE) inhibitors, and other compounds with anti-inflammatory properties, including the immunosuppressant cyclosporine and the mood stabilizer valproic acid. Taken together, these data suggest that inflammatory signaling pathways not only induce sleep during infection (14) but also play a role in setting normal daytime activity levels.

4) Ether-a-go-go–related gene (ERG) potassium channel blockers selectively increased waking activity at night without affecting total rest (Fig. 4C and fig. S18). This phenotype was induced by compounds with divergent therapeutic indications (e.g., the antimalarial halofantrine, the antipsychotic haloperidol, the antihistamine terfenadine); however, these drugs also inhibit the ERG channel and can cause the heart rhythm disorder long QT syndrome (15, 16). Rank-sorting all the screened compounds by their fingerprints’ mean correlation to the ERG-blocking cluster resulted in a significant enrichment of known ERG blockers in the top ranks (Fig. 4D). Moreover, the specific ERG inhibitor dofetilide increased nighttime activity, whereas structurally related non-ERG blocking compounds, including the antihistamines fexofenadine and cetirizine, did not (fig. S18B). Finally, this phenotype was not caused by general misregulation of potassium channels, because psora-4, a drug that blocks the related shaker potassium channel Kv1.3, induced a distinct phenotype (fig. S18A). These results suggest that ERG potassium channels play a role in regulating wakefulness at night that is distinct from the role of shaker channels in regulating sleep in flies and mice (17, 18).

As applied here, behavioral profiling reveals relationships between drugs and their targets, demonstrates a conserved vertebrate neuropharmacology, and identifies regulators of rest/wake states. Our findings have two major implications for the fields of neurobiology, pharmacology, and systems biology. First, behavioral profiling has the potential to complement traditional drug discovery methodologies by combining the physiological relevance of in vivo assays with high-throughput, low-cost screening (3). Future screens can be expanded to include many more uncharacterized compounds and to assay additional phenotypes, including those associated with human psychiatric disorders. In this way, behavioral profiling can characterize large classes of compounds and reveal differences in effectiveness, potential side effects, and combinatorial properties that might not be detected in vitro. Second, behavioral profiling allows for the systematic dissection of the pharmacology of complex behaviors. Our screen profiled the effects of dozens of neurotransmitter pathways and identified small molecules that regulate discrete aspects of rest/wake states. Future experiments can test drug combinations to identify synergistic or antagonistic effects among psychotropic compounds and to build interaction maps. High-throughput behavioral profiling thus may enable application of the logic and approaches of systems biology to neuropharmacology and behavior.

Supporting Online Material

Materials and Methods

SOM Text

Figs. S1 to S18

Table S1


  • * These authors contributed equally to this work.

  • Present address: Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA.

References and Notes

  1. See supporting material on Science Online.
  2. We thank J. Dowling, D. Milan, and G. Vanderlaan for suggestions and reagents and D. Schoppik, G. Uhl, and I. Woods for critical reading of the manuscript. Supported by a Bristol-Myers Squibb postdoctoral fellowship of the Life Sciences Research Foundation (J.R.), a Helen Hay Whitney Foundation postdoctoral fellowship (D.A.P.), a NIH Pathway to Independence grant (D.A.P.), the Stanley Medical Research Institute (S.J.H.), the Harvard Stem Cell Institute (L.L.R.), NIH grants MH086867 and MH085205 (R.T.P.), and grants from NIH and the McKnight Endowment Fund for Neuroscience (A.F.S.). L.L.R. is a founder of iPierian Inc., a biotechnology company, and is a member of its scientific advisory board.
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