Adgrg6 (Gpr126) is an adhesion (B2) class G protein-coupled receptor (aGPCR) with conserved roles in myelination of the vertebrate peripheral nervous system (PNS) (reviewed in Langenhan et al., 2016; Patra et al., 2014). In homozygous loss-of-function adgrg6 zebrafish and mouse mutants, peripheral myelination is severely impaired: Schwann cells associate with axons, but are unable to generate the myelin sheath, and show reduced expression of the myelin basic protein (mbp) gene (Glenn and Talbot, 2013; Mogha et al., 2013; Monk et al., 2009; Monk et al., 2011). Targeted disruption of Adgrg6 in the mouse results in additional abnormal phenotypes, including limb and cardiac abnormalities, axon degeneration and embryonic lethality (Monk et al., 2011; Patra et al., 2013; Waller-Evans et al., 2010). In humans, mutations in ADGRG6 are causative for congenital contracture syndrome 9, a severe type of arthrogryposis multiplex congenita (Ravenscroft et al., 2015). Peripheral nerves from affected individuals have reduced expression of myelin basic protein, suggesting that the function of ADGRG6 in myelination is evolutionarily conserved from teleosts to humans (Ravenscroft et al., 2015). Human ADGRG6 variants have also been proposed to underlie some paediatric musculoskeletal disorders, including adolescent idiopathic scoliosis (Karner et al., 2015) (and references within).

In zebrafish, homozygous loss-of-function adgrg6 mutants exhibit an inner ear defect in addition to deficiencies in myelination (Geng et al., 2013; Monk et al., 2009). In the otic vesicle, the epithelial projections that prefigure formation of the semicircular canal ducts overgrow and fail to fuse, resulting in morphological defects and ear swelling. Analysis of the zebrafish adgrg6 mutant ear shows a dramatic alteration in the expression of genes coding for several extracellular matrix (ECM) components and ECM-modifying enzymes (Geng et al., 2013) (Figure 1A). Notably, transcripts coding for core proteins of the chondroitin sulphate proteoglycan Versican, normally transiently expressed in the outgrowing projections and then down-regulated once projection fusion has occurred, remain highly expressed in the overgrown and unfused projections of adgrg6 mutants (Geng et al., 2013). Although Adgrg6 (Gpr126) mRNA is known to be expressed in the mouse ear (Patra et al., 2013), a role in otic development in the mammal has yet to be determined.

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Like all aGPCR members, the zebrafish Adgrg6 receptor consists of a long extracellular domain (ECD), a seven-pass transmembrane domain (7TM), and a short intracellular domain (reviewed in Langenhan et al., 2016) (Figure 1B). The ECD includes a GPCR autoproteolysis-inducing (GAIN) domain, which incorporates the GPCR proteolytic site (GPS) and the conserved Stachel sequence (Liebscher et al., 2014; Patra et al., 2014). Proteolysis at the GPS results in two fragments, an N-terminal fragment (NTF) and a C-terminal fragment (CTF), which can remain associated with one another, or may dissociate, the NTF binding to cell surface or extracellular matrix ligands (Patra et al., 2014; Petersen et al., 2015). Dissociation of the NTF triggers binding of the Stachel sequence to the 7TM domain, thereby activating the CTF (Liebscher et al., 2014). This feature provides a variety of CTF-dependent or -independent signalling capabilities that orchestrate cell adhesion and other cell-cell or cell-matrix interactions. For example, during Schwann cell development and terminal differentiation, the Adgrg6 NTF promotes radial sorting of axons, whereas the CTF is thought to signal through a stimulatory Gα subunit (Gα_s), leading to elevated cAMP levels and activated protein kinase A (PKA) to induce transcription of downstream target genes, such as egr2 and oct6 (Petersen et al., 2015). Compounds that act to raise intracellular cAMP levels, such as the phosphodiesterase inhibitor 3-isobutyl-1-methylxanthine (IBMX) and the adenylyl cyclase activator forskolin, can rescue phenotypic defects in both the inner ear and PNS in adgrg6 mutants (Geng et al., 2013; Monk et al., 2009).

Despite the enormous importance of GPCRs as drug targets (Hauser et al., 2017; Sriram and Insel, 2018; Wootten et al., 2018), adhesion class GPCRs remain poorly characterised, representing a valuable untapped resource as targets of future therapeutics (Hamann et al., 2015; Monk et al., 2015). The identification of specific modulators of aGPCR activity is an essential step for understanding the mechanism of aGPCR function and to inform the design of new drugs. Recent successful approaches include the use of Stachel sequence peptides as aGPCR agonists (Demberg et al., 2017), or synthetic monobodies directed against domains within the NTF (Salzman et al., 2017). A promising alternative approach lies in the potential of unbiased whole-animal screening of small molecules. In recent years, zebrafish have emerged as an important tool for in vivo phenotypic screening for new therapeutics (Brady et al., 2016) and for understanding biological mechanisms (Baxendale et al., 2017; Richter et al., 2017). Zebrafish have many advantages for drug discovery: they are a vertebrate species whose embryos can fit into individual wells of a multiwell plate, facilitating high-throughput analysis; they generate large numbers of offspring; they can absorb compounds directly added to the water, and whole-organism screening enables toxicity, absorption, metabolism and excretion of compounds to be assayed early in the screening pipeline.

To date, over one hundred drug screens using different zebrafish disease models have been conducted, some identifying lead compounds that have subsequently been tested in mammalian model systems or entered clinical trials (Chowdhury et al., 2013; Griffin et al., 2017; North et al., 2007; Owens et al., 2008) (reviewed in Baxendale et al., 2017). Two screens have been performed to identify compounds that promote myelination in the central nervous system (Buckley et al., 2010; Early et al., 2018). These studies used live imaging of Tg(olig2:GFP) or Tg(mbp:eGFP) fluorescent transgenic lines to screen for small molecules that increase progenitor or myelinating oligodendrocyte cell number. While elegant in design, and successful in identifying hit compounds, these screens required the use of sophisticated and costly high-resolution imaging platforms and relied on detailed quantitative assays for cell number, techniques that are not available to all labs and are potentially limiting in scalability and throughput.

In this study, we have developed an in vivo drug screening assay based on semi-automated in situ hybridisation (ISH) to identify modulators of the Adgrg6 pathway. We have used the otic expression of versican b (vcanb) as an easily-scored qualitative readout to identify compounds that can reduce vcanb overexpression back to normal levels in a hypomorphic mutant allele for adgrg6, tb233c. We used expression of mbp in the posterior lateral line ganglion of adgrg6^tb233c mutants as a secondary screening assay, with the aim of identifying chemical classes capable of rescuing the expression of both genes, which may thus represent agonists of the Adgrg6 signalling pathway. To identify ligands that potentially bind directly to Adgrg6, we then tested hit compounds for their ability to rescue a strong loss-of-function adgrg6 allele, fr24, which predicts a severely truncated protein. Several compounds were unable to rescue adgrg6^fr24 mutants, including a group with similar structures from the gedunin family of compounds. Compounds able to rescue both alleles include colforsin, a known activator of adenylyl cyclase, demonstrating proof-of-principle that our screen can identify compounds that restore GPCR pathway activity downstream of the receptor. These alternative assays for both down-regulation and up-regulation of gene expression, combined with a comparison of rescue in both weak and strong alleles, have facilitated selection of a strong cohort of hit compounds that can be differentiated by the different screens used. Our approach is scalable and can be used to screen additional compound collections. In parallel, chemoinformatics analysis of the compound libraries and identified hits has enabled classification and prioritisation of selected hit compounds.

Adhesion GPCRs are critical regulators of development and disease, driving cell-cell and cell-ECM communications to elicit internal responses to extrinsic cues. This study set out to identify positive modulators of the Adgrg6 signalling pathway, a key regulator of myelination and inner ear development in the zebrafish embryo. Use of a whole-animal phenotypic (mutant rescue) screen gave the potential to identify compounds affecting the entire Adgrg6 pathway in the correct cellular context. We have used a simple in situ hybridisation approach to assay vcanb expression in the inner ear of adgrg6 mutants, exploiting an easily identifiable phenotype that could be scored manually. Following our primary screen of 3120 small molecules, we tested 89 hit compounds in a counter screen for rescue of the myelination defect in adgrg6 mutant embryos. We identified 41 compounds that can both rescue vcanb expression in the inner ear and mbp expression in Schwann cells of adgrg6 hypomorphic mutants, suggesting these are Adgrg6 pathway-specific modulators. Further analysis of a strong adgrg6 allele, fr24, identified a subset of 19 compounds that are potential direct interactors of the Adgrg6 receptor. This analysis, combined with chemoinformatics analysis of the identified hit compounds, has identified clusters of compounds acting at different levels of the Adgrg6 pathway.

An optimal drug screening assay design identifies the maximum number of hit compounds with the minimum number of false positives and false negatives. Chemical screening assays using zebrafish range from simple morphology screens (Yu et al., 2008) through to high-tech, automated methods for quantitative image analysis (Early et al., 2018) or behavioural analysis (Bruni et al., 2016; Rennekamp et al., 2016) (reviewed in Kalueff et al., 2016). We used an in situ hybridisation screen to analyse gene expression changes, as this has the advantages of being scalable to different sized projects and relatively inexpensive to perform—with results that are stable and reproducible. Spatial resolution of staining patterns can be accurately scored: expression pattern screens have recently been used to identify small molecules that can induce subtle differences in gene expression domains along the pronephros (Poureetezadi et al., 2016) and in the somites (Richter et al., 2017). Although quantification of gene expression levels is less reliable with an enzymatic reaction compared with a fluorescent signal, we utilised the strong contrast between the high vcanb expression in the ear of adgrg6 mutant fish compared with the low expression in a small dorsal region of the wild-type ear at four dpf to produce a robust scoring system for our phenotype rescue.

Relatively few zebrafish screens have been undertaken to identify compounds that can increase myelination (Buckley et al., 2010; Early et al., 2018) or restore myelination in neuropathy models (Zada et al., 2016), which is in part due to the complex distribution of glial cells in both the CNS and PNS. Performing the primary screen using an ear marker, vcanb, enabled us to bypass the difficulties of scoring and quantification of mbp staining on a large scale; instead, mbp expression was used as a counter screen on a limited number of cherry-picked hits. Contrary to the primary assays, which screened for down-regulation of vcanb expression, the counter screen assayed for up-regulation of mbp expression, enabling the identification of 21 false-positive compounds that down-regulate the expression of both genes (presumably by inhibiting transcription).

Determining the false-negative rate for any screen is difficult. In our assay we used only one concentration of compound (25 µM), so it is likely that some of the compounds that were toxic or showed no effect at 25 µM—and thus eliminated from our screen—would be effective at lower or higher concentrations, respectively. One possibility would be to run a parallel screen at a lower or higher concentration or use an alternative protocol with shorter incubation times, an approach that has recently proved successful at identifying different compounds influencing segmentation in zebrafish (Richter et al., 2017). Here, the compounds were found to be most active in the range of 10–50 µM, supporting our choice of 25 µM for the primary screen. However, increasing the number of replicates with different drug concentrations or assay conditions has significant implications on the cost and time taken to complete the screen, reducing the number of compounds analysed and the potential hits identified. An alternative method is to use a structural network to select and prioritise candidate compounds that have a similar structure to hit compounds and test these at different concentrations. Using this approach, we identified (±)-Bay K 8644 as an additional hit from the pyridine cluster when tested at a higher concentration. Our minimum estimate for the false-negative rate is 5%, based on the seven compounds (out of a total of 155) that were duplicated in both libraries and had a significantly different score after retesting, being classified as a hit in one library but not in the other. It is possible that this is due to differences in chemical purity from the different suppliers. Other false-negative compounds could include those that are unable to penetrate into the ear. Neomycin, for example, is toxic to the superficial hair cells of the lateral line system, but is ineffective on inner ear hair cells unless microinjected into the ear (Buck et al., 2012). Other compounds that we will have missed could include myelination-specific compounds, as the primary assay scored for the ear phenotype only. Given that several compounds were positive hits for the rescue of vcanb in the ear and negative for mbp, it is likely that tissue-specific functions of Adgrg6 are mediated through different downstream pathways or are stimulated by different ligands.

Our positive control compound, IBMX, was identified independently through our screen as a category A hit. The hit compound colforsin was found to be more potent and less toxic than the related control compound forskolin, and had the highest score in every assay, showing full rescue of the strong fr24 allele. Both these observations highlight the robustness of the assay and the consistency of the scoring process. In total, the final number of hit compounds identified was similar in both the compound libraries screened, with 42 compounds identified from the Spectrum library (2.1%) and 27 compounds from the Tocris library (2.4%). These hit rates are comparable to those found in other similar screens (Baxendale et al., 2012; Vettori et al., 2017; Wiley et al., 2017).

Chemoinformatics analysis and visualisation of the results provided additional context to the identified hit compounds. The polar scatter plot displayed an initial overview of the results and allowed the identification of six different structurally-related clusters of active compounds with similar structure. The compound network focused the analysis on highly detailed similarity relationships inside each compound cluster, yielding a wealth of structure-activity relationship information that could prove very useful for any future optimisation of the identified hit compounds. Seven of the 41 hit compounds that rescued vcanb and mbp expression are Ca²⁺-channel modulators. Six of these (nifedipine, cilnidipine, nitrendipine, nimodipine, efonidipine, niguldipine) belong to the chemical group of dihydropyridines (cluster 1), and initial investigations into compounds with similar structures identified four additional compounds in this class. Several dihydropyridines are known to have neuroprotective effects in mammalian models. Nimodipine, for example, has been shown to trigger remyelination in a mouse model of multiple sclerosis and to improve repair in peripheral nerve crush injuries in rats (Schampel et al., 2017; Tang et al., 2015), and some compounds have shown promise at clearing toxic proteins in animal models of neurodegeneration, including felodipine (Siddiqi et al., 2019) and nilvadipine (Paris et al., 2011). As dihydropyridines have been reported to inhibit cAMP phosphodiesterases (Sharma et al., 1997), protection of cAMP from degradation might be another mechanism whereby these molecules exert their ameliorating action on the adgrg6 mutant phenotype.

Phenotypic screens are advantageous for assessing models of multifactorial pathological conditions, such as hereditary neuropathies and cancer (reviewed in Baxendale et al., 2017). However, one of the challenges for phenotypic screening is the identification of the specific target for any hit compound, as multiple pathways and different cell types can contribute to a positive read-out in the screening assay. Our aim was to identify compounds that could potentially interact directly with the Adgrg6 receptor. We were able to separate hit compounds into different groups based on their ability to rescue otic phenotypes caused by missense (tb233c) and nonsense (fr24) mutations. In total, we found 19 hits that could rescue vcanb expression and mbp expression in the tb233c allele, but were unable to rescue the fr24 allele. We hypothesise that the fr24 allele is unable to produce the full-length Adgrg6 protein including the CTF, and therefore any compounds that interact directly with the receptor would not be able to rescue any CTF-dependent function in this strong allele. Further analysis will be needed to determine whether any of these compounds can bind directly to the Adgrg6 receptor, for example by assessing stimulation of cAMP and direct binding in in vitro assays. However, this approach of using a combination of null and hypomorphic alleles in zebrafish whole-organism screening with the aim of identifying target-specific compounds is particularly exciting and one that the advent of CRISPR/Cas9 technology is placed to take full advantage of, since it is now possible to generate designer mutations in the zebrafish through homology-directed repair (Hruscha et al., 2013; Hwang et al., 2013; Komor et al., 2016).

It is of interest to note that one of the main groups of compounds identified as potential interactors of the receptor in the fr24 screen is a cluster of gedunin derivatives (cluster 2). One of these compounds, deoxygedunin, has previously been identified as a TrkB agonist that has neuroprotective properties (Nie et al., 2015), can promote axon regeneration after nerve injury (English et al., 2013), and, interestingly, has been found to protect the vestibular ganglion from degeneration in mice mutant for BDNF (Jang et al., 2010). More recently, gedunin derivatives, including 3-α-DOG, have been shown to act as partial agonists for the closely related aGPCR, ADGRG1 (formerly GPR56) (Stoveken et al., 2018), a key regulator of myelination in both the CNS and PNS (Ackerman et al., 2015; Ackerman et al., 2018; Giera et al., 2015; Salzman et al., 2016). While further work will be necessary to determine if gedunin-type molecules can also bind and activate zebrafish Adgrg6 by interacting directly, these studies set a precedent for this type of interaction.

GPCRs can be modulated by the membrane lipid cholesterol, where interactions with the 7TM domain can provide plasticity for the receptors by altering their stability and structure (Huang et al., 2018; Prasanna et al., 2016). In addition, cholesterol can activate the hedgehog signalling pathway directly by binding to the extracellular domain of the GPCR Smoothened (Huang et al., 2018; Luchetti et al., 2016). Although cholesterol was not identified as a hit in our primary screen, we did identify two cholesterol-lowering drugs, ezetimibe (Altmann et al., 2004) and rosuvastatin (Istvan and Deisenhofer, 2001), as putative modulators of the Adgrg6 pathway. Whether these act by altering the activity of Adgrg6 through altering cholesterol levels remains to be determined.

In addition to the dihydropyridines (cluster 1) and the tetranortriterpenoid (gedunin-derived) compounds (cluster 2), there are also clusters of steroid hormones (danazol, hydroxyprogesterone, pregnenalone succinate, hydrocortisone hemisuccinate) and flavonoid compounds (baicalein, tangeritin, nobiletin, dimethylnobiletin, hexamethylquercetagetin). The flavonoids are a group of molecules with wide ranging activities, including anti-cancer (Ma et al., 2015) and neuroprotective properties (reviewed in Braidy et al., 2017). All four O-methylated flavonoids that rescued vcanb and mbp expression in tb233c mutants were also able to rescue fr24 allele in our assay, suggesting that they act downstream of the Adgrg6 receptor.

Our screen identified 28 compounds that down-regulated vcanb expression, but did not rescue mbp expression, which may provide useful tools to manipulate semicircular canal formation in vivo. Versican and other chondroitin sulphate proteoglycans (CSPGs) are associated with a number of human pathologies; Versican overexpression has been shown to be strongly involved in inflammation, cancer progression and the development of lung disorders (reviewed in Andersson-Sjöland et al., 2015; Ricciardelli et al., 2009; Wight et al., 2017). CSPGs and hyaluronan are components of the inhibitory scar that forms at the site of injury after CNS damage, preventing axon regeneration (Silver and Miller, 2004). In addition, CSPGs have been shown to inhibit the ability of oligodendrocytes to remyelinate axons, a process that is reversed by reduction of CSPG levels (Keough et al., 2016; Pendleton et al., 2013). Whether the down-regulation of CSPGs to promote remyelination occurs via a similar mechanism to that involved in Adgrg6-regulated projection fusion remains to be determined. However, it is of interest that a key regulator of myelination, Adgrg1, has also been recently shown to reduce fibronectin deposition and inhibit cell-ECM signalling to prevent metastatic melanoma growth (Millar et al., 2018).

In conclusion, our data show that vcanb expression in the adgrg6^tb233c mutant ear provides a robust, easy-to-use screening tool to identify drugs that target the Adgrg6 pathway. In combination with the different alleles available for adgrg6 in zebrafish, this in vivo platform provides an excellent opportunity to find hit compounds that may be specific for Adgrg6 in counter screens. These may provide a starting point for the development of therapeutic approaches towards human diseases where ADGRG6 or myelination is affected. We have identified groups of structurally-related compounds that can rescue adgrg6 mutant defects, including those that are likely to act downstream of the Adgrg6 pathway, and others that are candidates for interacting with the Adgrg6 receptor. The chemical analysis and structural comparison of the compounds shown to be putative Adgrg6 receptor agonists will contribute to the elucidation of the physical properties responsible for ligand binding and will provide further insight on the underlying mechanism of Adgrg6 signalling.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Table 1 and Figure 1-figure supplement 1, Figure 3, Figure 7 and Figure 7-figure supplements. Links to interactive files are given in the manuscript and in a supplementary file.

1. 1. Ackerman SD
   2. Garcia C
   3. Piao X
   4. Gutmann DH
   5. Monk KR

   (2015) The adhesion GPCR Gpr56 regulates oligodendrocyte development via interactions with gα12/13 and RhoA

   Nature Communications 6:6122.

   https://doi.org/10.1038/ncomms7122

   • PubMed
   • Google Scholar
2. 1. Ackerman SD
   2. Luo R
   3. Poitelon Y
   4. Mogha A
   5. Harty BL
   6. D'Rozario M
   7. Sanchez NE
   8. Lakkaraju AKK
   9. Gamble P
   10. Li J
   11. Qu J
   12. MacEwan MR
   13. Ray WZ
   14. Aguzzi A
   15. Feltri ML
   16. Piao X
   17. Monk KR

   (2018) GPR56/ADGRG1 regulates development and maintenance of peripheral myelin

   The Journal of Experimental Medicine 215:941–961.

   https://doi.org/10.1084/jem.20161714

   • PubMed
   • Google Scholar
3. 1. Altmann SW
   2. Davis HR
   3. Zhu LJ
   4. Yao X
   5. Hoos LM
   6. Tetzloff G
   7. Iyer SP
   8. Maguire M
   9. Golovko A
   10. Zeng M
   11. Wang L
   12. Murgolo N
   13. Graziano MP

   (2004) Niemann-Pick C1 like 1 protein is critical for intestinal cholesterol absorption

   Science 303:1201–1204.

   https://doi.org/10.1126/science.1093131

   • PubMed
   • Google Scholar
4. 1. Andersson-Sjöland A
   2. Hallgren O
   3. Rolandsson S
   4. Weitoft M
   5. Tykesson E
   6. Larsson-Callerfelt AK
   7. Rydell-Törmänen K
   8. Bjermer L
   9. Malmström A
   10. Karlsson JC
   11. Westergren-Thorsson G

   (2015) Versican in inflammation and tissue remodeling: the impact on lung disorders

   Glycobiology 25:243–251.

   https://doi.org/10.1093/glycob/cwu120

   • PubMed
   • Google Scholar
5. 1. Baxendale S
   2. Holdsworth CJ
   3. Meza Santoscoy PL
   4. Harrison MR
   5. Fox J
   6. Parkin CA
   7. Ingham PW
   8. Cunliffe VT

   (2012) Identification of compounds with anti-convulsant properties in a zebrafish model of epileptic seizures

   Disease Models & Mechanisms 5:773–784.

   https://doi.org/10.1242/dmm.010090

   • PubMed
   • Google Scholar
6. 1. Baxendale S
   2. van Eeden F
   3. Wilkinson R

   (2017) The power of zebrafish in personalised medicine

   Advances in Experimental Medicine and Biology 1007:179–197.

   https://doi.org/10.1007/978-3-319-60733-7_10

   • PubMed
   • Google Scholar
7. 1. Baxendale S
   2. Whitfield TT

   (2016) Methods to study the development, anatomy, and function of the zebrafish inner ear across the life course

   Methods in Cell Biology 134:165–209.

   https://doi.org/10.1016/bs.mcb.2016.02.007

   • PubMed
   • Google Scholar
8. 1. Bemis GW
   2. Murcko MA

   (1996) The properties of known drugs. 1. molecular frameworks

   Journal of Medicinal Chemistry 39:2887–2893.

   https://doi.org/10.1021/jm9602928

   • PubMed
   • Google Scholar
9. 1. Berthold MR
   2. Cebron N
   3. Dill F
   4. Gabriel TR
   5. Kötter T
   6. Meinl T
   7. Ohl P
   8. Thiel K
   9. Wiswedel B

   (2009) KNIME - the Konstanz information miner

   ACM SIGKDD Explorations Newsletter 11:26–31.

   https://doi.org/10.1145/1656274.1656280

   • Google Scholar
10. 1. Brady CA
    2. Rennekamp AJ
    3. Peterson RT

    (2016) Chemical screening in zebrafish

    Methods in Molecular Biology 1451:3–16.

    https://doi.org/10.1007/978-1-4939-3771-4_1

    • PubMed
    • Google Scholar
11. 1. Braidy N
    2. Behzad S
    3. Habtemariam S
    4. Ahmed T
    5. Daglia M
    6. Nabavi SM
    7. Sobarzo-Sanchez E
    8. Nabavi SF

    (2017) Neuroprotective effects of citrus Fruit-Derived flavonoids, nobiletin and Tangeretin in Alzheimer's and Parkinson's Disease

    CNS & Neurological Disorders - Drug Targets 16:387–397.

    https://doi.org/10.2174/1871527316666170328113309

    • PubMed
    • Google Scholar
12. 1. Brösamle C
    2. Halpern ME

    (2002) Characterization of myelination in the developing zebrafish

    Glia 39:47–57.

    https://doi.org/10.1002/glia.10088

    • PubMed
    • Google Scholar
13. 1. Bruni G
    2. Rennekamp AJ
    3. Velenich A
    4. McCarroll M
    5. Gendelev L
    6. Fertsch E
    7. Taylor J
    8. Lakhani P
    9. Lensen D
    10. Evron T
    11. Lorello PJ
    12. Huang XP
    13. Kolczewski S
    14. Carey G
    15. Caldarone BJ
    16. Prinssen E
    17. Roth BL
    18. Keiser MJ
    19. Peterson RT
    20. Kokel D

    (2016) Zebrafish behavioral profiling identifies multitarget antipsychotic-like compounds

    Nature Chemical Biology 12:559–566.

    https://doi.org/10.1038/nchembio.2097

    • PubMed
    • Google Scholar
14. 1. Buck LM
    2. Winter MJ
    3. Redfern WS
    4. Whitfield TT

    (2012) Ototoxin-induced cellular damage in neuromasts disrupts lateral line function in larval zebrafish

    Hearing Research 284:67–81.

    https://doi.org/10.1016/j.heares.2011.12.001

    • PubMed
    • Google Scholar
15. 1. Buckley CE
    2. Marguerie A
    3. Roach AG
    4. Goldsmith P
    5. Fleming A
    6. Alderton WK
    7. Franklin RJ

    (2010) Drug reprofiling using zebrafish identifies novel compounds with potential pro-myelination effects

    Neuropharmacology 59:149–159.

    https://doi.org/10.1016/j.neuropharm.2010.04.014

    • PubMed
    • Google Scholar
16. 1. Cheng HL
    2. Hsieh MJ
    3. Yang JS
    4. Lin CW
    5. Lue KH
    6. Lu KH
    7. Yang SF

    (2016) Nobiletin inhibits human osteosarcoma cells metastasis by blocking ERK and JNK-mediated MMPs expression

    Oncotarget 7:35208–35223.

    https://doi.org/10.18632/oncotarget.9106

    • PubMed
    • Google Scholar
17. 1. Chowdhury R
    2. Candela-Lena JI
    3. Chan MC
    4. Greenald DJ
    5. Yeoh KK
    6. Tian YM
    7. McDonough MA
    8. Tumber A
    9. Rose NR
    10. Conejo-Garcia A
    11. Demetriades M
    12. Mathavan S
    13. Kawamura A
    14. Lee MK
    15. van Eeden F
    16. Pugh CW
    17. Ratcliffe PJ
    18. Schofield CJ

    (2013) Selective small molecule probes for the hypoxia inducible factor (HIF) prolyl hydroxylases

    ACS Chemical Biology 8:1488–1496.

    https://doi.org/10.1021/cb400088q

    • PubMed
    • Google Scholar
18. 1. Demberg LM
    2. Winkler J
    3. Wilde C
    4. Simon KU
    5. Schön J
    6. Rothemund S
    7. Schöneberg T
    8. Prömel S
    9. Liebscher I

    (2017) Activation of adhesion G Protein-coupled receptors: agonist SPECIFICITY OF STACHEL SEQUENCE-DERIVED PEPTIDES

    The Journal of Biological Chemistry 292:4383–4394.

    https://doi.org/10.1074/jbc.M116.763656

    • PubMed
    • Google Scholar
19. 1. Early JJ
    2. Cole KL
    3. Williamson JM
    4. Swire M
    5. Kamadurai H
    6. Muskavitch M
    7. Lyons DA

    (2018) An automated high-resolution in vivo screen in zebrafish to identify chemical regulators of myelination

    eLife 7:e35136.

    https://doi.org/10.7554/eLife.35136

    • PubMed
    • Google Scholar
20. 1. English AW
    2. Liu K
    3. Nicolini JM
    4. Mulligan AM
    5. Ye K

    (2013) Small-molecule trkB agonists promote axon regeneration in cut peripheral nerves

    PNAS 110:16217–16222.

    https://doi.org/10.1073/pnas.1303646110

    • PubMed
    • Google Scholar
21. 1. Geng FS
    2. Abbas L
    3. Baxendale S
    4. Holdsworth CJ
    5. Swanson AG
    6. Slanchev K
    7. Hammerschmidt M
    8. Topczewski J
    9. Whitfield TT

    (2013) Semicircular canal morphogenesis in the zebrafish inner ear requires the function of gpr126 (lauscher), an adhesion class G protein-coupled receptor gene

    Development 140:4362–4374.

    https://doi.org/10.1242/dev.098061

    • PubMed
    • Google Scholar
22. 1. Giera S
    2. Deng Y
    3. Luo R
    4. Ackerman SD
    5. Mogha A
    6. Monk KR
    7. Ying Y
    8. Jeong SJ
    9. Makinodan M
    10. Bialas AR
    11. Chang BS
    12. Stevens B
    13. Corfas G
    14. Piao X

    (2015) The adhesion G protein-coupled receptor GPR56 is a cell-autonomous regulator of oligodendrocyte development

    Nature Communications 6:6121.

    https://doi.org/10.1038/ncomms7121

    • PubMed
    • Google Scholar
23. 1. Glenn TD
    2. Talbot WS

    (2013) Analysis of Gpr126 function defines distinct mechanisms controlling the initiation and maturation of myelin

    Development 140:3167–3175.

    https://doi.org/10.1242/dev.093401

    • PubMed
    • Google Scholar
24. 1. Griffin A
    2. Hamling KR
    3. Knupp K
    4. Hong S
    5. Lee LP
    6. Baraban SC

    (2017) Clemizole and modulators of serotonin signalling suppress seizures in dravet syndrome

    Brain 64:aww342–aww683.

    https://doi.org/10.1093/brain/aww342

    • Google Scholar
25. 1. Hamann J
    2. Aust G
    3. Araç D
    4. Engel FB
    5. Formstone C
    6. Fredriksson R
    7. Hall RA
    8. Harty BL
    9. Kirchhoff C
    10. Knapp B
    11. Krishnan A
    12. Liebscher I
    13. Lin HH
    14. Martinelli DC
    15. Monk KR
    16. Peeters MC
    17. Piao X
    18. Prömel S
    19. Schöneberg T
    20. Schwartz TW
    21. Singer K
    22. Stacey M
    23. Ushkaryov YA
    24. Vallon M
    25. Wolfrum U
    26. Wright MW
    27. Xu L
    28. Langenhan T
    29. Schiöth HB

    (2015) International union of basic and clinical pharmacology. XCIV. adhesion G protein-coupled receptors

    Pharmacological Reviews 67:338–367.

    https://doi.org/10.1124/pr.114.009647

    • PubMed
    • Google Scholar
26. 1. Hauser AS
    2. Attwood MM
    3. Rask-Andersen M
    4. Schiöth HB
    5. Gloriam DE

    (2017) Trends in GPCR drug discovery: new agents, targets and indications

    Nature Reviews Drug Discovery 16:829–842.

    https://doi.org/10.1038/nrd.2017.178

    • PubMed
    • Google Scholar
27. 1. Heller SR
    2. McNaught A
    3. Pletnev I
    4. Stein S
    5. Tchekhovskoi D

    (2015) InChI, the IUPAC international chemical identifier

    Journal of Cheminformatics 7:23.

    https://doi.org/10.1186/s13321-015-0068-4

    • PubMed
    • Google Scholar
28. 1. Hieronymus H
    2. Lamb J
    3. Ross KN
    4. Peng XP
    5. Clement C
    6. Rodina A
    7. Nieto M
    8. Du J
    9. Stegmaier K
    10. Raj SM
    11. Maloney KN
    12. Clardy J
    13. Hahn WC
    14. Chiosis G
    15. Golub TR

    (2006) Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators

    Cancer Cell 10:321–330.

    https://doi.org/10.1016/j.ccr.2006.09.005

    • PubMed
    • Google Scholar
29. 1. Hruscha A
    2. Krawitz P
    3. Rechenberg A
    4. Heinrich V
    5. Hecht J
    6. Haass C
    7. Schmid B

    (2013) Efficient CRISPR/Cas9 genome editing with low off-target effects in zebrafish

    Development 140:4982–4987.

    https://doi.org/10.1242/dev.099085

    • PubMed
    • Google Scholar
30. 1. Hu HH
    2. Li SJ
    3. Wang P
    4. Yan HC
    5. Cao X
    6. Hou FQ
    7. Fang YY
    8. Zhu XH
    9. Gao TM

    (2013) An L-type calcium channel agonist, bay K8644, extends the window of intervention against ischemic neuronal injury

    Molecular Neurobiology 47:280–289.

    https://doi.org/10.1007/s12035-012-8362-x

    • PubMed
    • Google Scholar
31. 1. Huang P
    2. Zheng S
    3. Wierbowski BM
    4. Kim Y
    5. Nedelcu D
    6. Aravena L
    7. Liu J
    8. Kruse AC
    9. Salic A

    (2018) Structural basis of smoothened activation in hedgehog signaling

    Cell 174:312–324.

    https://doi.org/10.1016/j.cell.2018.04.029

    • PubMed
    • Google Scholar
32. 1. Hunter JD

    (2007) Matplotlib: a 2D graphics environment

    Computing in Science & Engineering 9:90–95.

    https://doi.org/10.1109/MCSE.2007.55

    • Google Scholar
33. 1. Hwang WY
    2. Fu Y
    3. Reyon D
    4. Maeder ML
    5. Tsai SQ
    6. Sander JD
    7. Peterson RT
    8. Yeh JR
    9. Joung JK

    (2013) Efficient genome editing in zebrafish using a CRISPR-Cas system

    Nature Biotechnology 31:227–229.

    https://doi.org/10.1038/nbt.2501

    • PubMed
    • Google Scholar
34. 1. Istvan ES
    2. Deisenhofer J

    (2001) Structural mechanism for statin inhibition of HMG-CoA reductase

    Science 292:1160–1164.

    https://doi.org/10.1126/science.1059344

    • PubMed
    • Google Scholar
35. 1. Jang SW
    2. Liu X
    3. Chan CB
    4. France SA
    5. Sayeed I
    6. Tang W
    7. Lin X
    8. Xiao G
    9. Andero R
    10. Chang Q
    11. Ressler KJ
    12. Ye K

    (2010) Deoxygedunin, a natural product with potent neurotrophic activity in mice

    PLOS ONE 5:e11528.

    https://doi.org/10.1371/journal.pone.0011528

    • PubMed
    • Google Scholar
36. 1. Kalueff AV
    2. Echevarria DJ
    3. Homechaudhuri S
    4. Stewart AM
    5. Collier AD
    6. Kaluyeva AA
    7. Li S
    8. Liu Y
    9. Chen P
    10. Wang J
    11. Yang L
    12. Mitra A
    13. Pal S
    14. Chaudhuri A
    15. Roy A
    16. Biswas M
    17. Roy D
    18. Podder A
    19. Poudel MK
    20. Katare DP
    21. Mani RJ
    22. Kyzar EJ
    23. Gaikwad S
    24. Nguyen M
    25. Song C
    26. International Zebrafish Neuroscience Research Consortium ZNRC

    (2016) Zebrafish neurobehavioral phenomics for aquatic neuropharmacology and toxicology research

    Aquatic Toxicology 170:297–309.

    https://doi.org/10.1016/j.aquatox.2015.08.007

    • PubMed
    • Google Scholar
37. 1. Kang JS
    2. Oohashi T
    3. Kawakami Y
    4. Bekku Y
    5. Izpisúa Belmonte JC
    6. Ninomiya Y

    (2004) Characterization of Dermacan, a novel zebrafish lectican gene, expressed in dermal bones

    Mechanisms of Development 121:301–312.

    https://doi.org/10.1016/j.mod.2004.01.007

    • PubMed
    • Google Scholar
38. 1. Kang SI
    2. Shin HS
    3. Kim SJ

    (2015) Sinensetin enhances adipogenesis and lipolysis by increasing cyclic adenosine monophosphate levels in 3T3-L1 adipocytes

    Biological & Pharmaceutical Bulletin 38:552–558.

    https://doi.org/10.1248/bpb.b14-00700

    • PubMed
    • Google Scholar
39. 1. Karner CM
    2. Long F
    3. Solnica-Krezel L
    4. Monk KR
    5. Gray RS

    (2015) Gpr126/Adgrg6 deletion in cartilage models idiopathic scoliosis and pectus excavatum in mice

    Human Molecular Genetics 24:4365–4373.

    https://doi.org/10.1093/hmg/ddv170

    • PubMed
    • Google Scholar
40. 1. Keough MB
    2. Rogers JA
    3. Zhang P
    4. Jensen SK
    5. Stephenson EL
    6. Chen T
    7. Hurlbert MG
    8. Lau LW
    9. Rawji KS
    10. Plemel JR
    11. Koch M
    12. Ling C-C
    13. Yong VW

    (2016) An inhibitor of chondroitin sulfate proteoglycan synthesis promotes central nervous system remyelination

    Nature Communications 7:e11312.

    https://doi.org/10.1038/ncomms11312

    • Google Scholar
41. 1. Komor AC
    2. Kim YB
    3. Packer MS
    4. Zuris JA
    5. Liu DR

    (2016) Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage

    Nature 533:420–424.

    https://doi.org/10.1038/nature17946

    • PubMed
    • Google Scholar
42. 1. Langenhan T
    2. Piao X
    3. Monk KR

    (2016) Adhesion G protein-coupled receptors in nervous system development and disease

    Nature Reviews Neuroscience 17:550–561.

    https://doi.org/10.1038/nrn.2016.86

    • PubMed
    • Google Scholar
43. 1. Liebscher I
    2. Schön J
    3. Petersen SC
    4. Fischer L
    5. Auerbach N
    6. Demberg LM
    7. Mogha A
    8. Cöster M
    9. Simon KU
    10. Rothemund S
    11. Monk KR
    12. Schöneberg T

    (2014) A tethered agonist within the ectodomain activates the adhesion G protein-coupled receptors GPR126 and GPR133

    Cell Reports 9:2018–2026.

    https://doi.org/10.1016/j.celrep.2014.11.036

    • PubMed
    • Google Scholar
44. 1. Lister JA
    2. Robertson CP
    3. Lepage T
    4. Johnson SL
    5. Raible DW

    (1999)

    Nacre encodes a zebrafish microphthalmia-related protein that regulates neural-crest-derived pigment cell fate

    Development 126:3757–3767.

    • PubMed
    • Google Scholar
45. 1. Liu L
    2. Gonzalez PK
    3. Barrett CF
    4. Rittenhouse AR

    (2003) The calcium channel ligand FPL 64176 enhances L-type but inhibits N-type neuronal calcium currents

    Neuropharmacology 45:281–292.

    https://doi.org/10.1016/S0028-3908(03)00153-9

    • PubMed
    • Google Scholar
46. 1. Luchetti G
    2. Sircar R
    3. Kong JH
    4. Nachtergaele S
    5. Sagner A
    6. Byrne EF
    7. Covey DF
    8. Siebold C
    9. Rohatgi R

    (2016) Cholesterol activates the G-protein coupled receptor smoothened to promote hedgehog signaling

    eLife 5:e20304.

    https://doi.org/10.7554/eLife.20304

    • PubMed
    • Google Scholar
47. 1. Ma W
    2. Feng S
    3. Yao X
    4. Yuan Z
    5. Liu L
    6. Xie Y

    (2015) Nobiletin enhances the efficacy of chemotherapeutic agents in ABCB1 overexpression cancer cells

    Scientific Reports 5:e18789.

    https://doi.org/10.1038/srep18789

    • Google Scholar
48. 1. Millar MW
    2. Corson N
    3. Xu L

    (2018) The adhesion G-Protein-Coupled receptor, GPR56/ADGRG1, inhibits Cell-Extracellular matrix signaling to prevent metastatic melanoma growth

    Frontiers in Oncology 8:8.

    https://doi.org/10.3389/fonc.2018.00008

    • PubMed
    • Google Scholar
49. 1. Mogha A
    2. Benesh AE
    3. Patra C
    4. Engel FB
    5. Schöneberg T
    6. Liebscher I
    7. Monk KR

    (2013) Gpr126 functions in schwann cells to control differentiation and myelination via G-protein activation

    Journal of Neuroscience 33:17976–17985.

    https://doi.org/10.1523/JNEUROSCI.1809-13.2013

    • PubMed
    • Google Scholar
50. 1. Monk KR
    2. Naylor SG
    3. Glenn TD
    4. Mercurio S
    5. Perlin JR
    6. Dominguez C
    7. Moens CB
    8. Talbot WS

    (2009) A G protein-coupled receptor is essential for schwann cells to initiate myelination

    Science 325:1402–1405.

    https://doi.org/10.1126/science.1173474

    • PubMed
    • Google Scholar
51. 1. Monk KR
    2. Oshima K
    3. Jörs S
    4. Heller S
    5. Talbot WS

    (2011) Gpr126 is essential for peripheral nerve development and myelination in mammals

    Development 138:2673–2680.

    https://doi.org/10.1242/dev.062224

    • PubMed
    • Google Scholar
52. 1. Monk KR
    2. Hamann J
    3. Langenhan T
    4. Nijmeijer S
    5. Schöneberg T
    6. Liebscher I

    (2015) Adhesion G Protein-Coupled receptors: from in vitro pharmacology to in vivo mechanisms

    Molecular Pharmacology 88:617–623.

    https://doi.org/10.1124/mol.115.098749

    • PubMed
    • Google Scholar
53. 1. Nie S
    2. Xu Y
    3. Chen G
    4. Ma K
    5. Han C
    6. Guo Z
    7. Zhang Z
    8. Ye K
    9. Cao X

    (2015) Small molecule TrkB agonist deoxygedunin protects nigrostriatal dopaminergic neurons from 6-OHDA and MPTP induced neurotoxicity in rodents

    Neuropharmacology 99:448–458.

    https://doi.org/10.1016/j.neuropharm.2015.08.016

    • PubMed
    • Google Scholar
54. 1. North TE
    2. Goessling W
    3. Walkley CR
    4. Lengerke C
    5. Kopani KR
    6. Lord AM
    7. Weber GJ
    8. Bowman TV
    9. Jang IH
    10. Grosser T
    11. Fitzgerald GA
    12. Daley GQ
    13. Orkin SH
    14. Zon LI

    (2007) Prostaglandin E2 regulates vertebrate haematopoietic stem cell homeostasis

    Nature 447:1007–1011.

    https://doi.org/10.1038/nature05883

    • PubMed
    • Google Scholar
55. 1. Owens KN
    2. Santos F
    3. Roberts B
    4. Linbo T
    5. Coffin AB
    6. Knisely AJ
    7. Simon JA
    8. Rubel EW
    9. Raible DW

    (2008) Identification of genetic and chemical modulators of zebrafish mechanosensory hair cell death

    PLOS Genetics 4:e1000020.

    https://doi.org/10.1371/journal.pgen.1000020

    • PubMed
    • Google Scholar
56. 1. Paris D
    2. Bachmeier C
    3. Patel N
    4. Quadros A
    5. Volmar CH
    6. Laporte V
    7. Ganey J
    8. Beaulieu-Abdelahad D
    9. Ait-Ghezala G
    10. Crawford F
    11. Mullan MJ

    (2011) Selective antihypertensive dihydropyridines lower aβ accumulation by targeting both the production and the clearance of aβ across the blood-brain barrier

    Molecular Medicine 17:149–162.

    https://doi.org/10.2119/molmed.2010.00180

    • PubMed
    • Google Scholar
57. 1. Patra C
    2. van Amerongen MJ
    3. Ghosh S
    4. Ricciardi F
    5. Sajjad A
    6. Novoyatleva T
    7. Mogha A
    8. Monk KR
    9. Mühlfeld C
    10. Engel FB

    (2013) Organ-specific function of adhesion G protein-coupled receptor GPR126 is domain-dependent

    PNAS 110:16898–16903.

    https://doi.org/10.1073/pnas.1304837110

    • PubMed
    • Google Scholar
58. 1. Patra C
    2. Monk KR
    3. Engel FB

    (2014) The multiple signaling modalities of adhesion G protein-coupled receptor GPR126 in development

    Receptors & Clinical Investigation 1:79.

    https://doi.org/10.14800/rci.79

    • PubMed
    • Google Scholar
59. 1. Pedregosa F
    2. Varoquaux G
    3. Gramfort A
    4. Michel V
    5. Thirion B
    6. Grisel O
    7. Blondel M
    8. Prettenhofer P
    9. Weiss R
    10. Dubourg V

    (2011)

    Scikit-learn: machine learning in python

    Journal of Machine Learning Research 12:2825–2830.

    • Google Scholar
60. 1. Pendleton JC
    2. Shamblott MJ
    3. Gary DS
    4. Belegu V
    5. Hurtado A
    6. Malone ML
    7. McDonald JW

    (2013) Chondroitin sulfate proteoglycans inhibit oligodendrocyte myelination through PTPσ

    Experimental Neurology 247:113–121.

    https://doi.org/10.1016/j.expneurol.2013.04.003

    • PubMed
    • Google Scholar
61. 1. Petersen SC
    2. Luo R
    3. Liebscher I
    4. Giera S
    5. Jeong SJ
    6. Mogha A
    7. Ghidinelli M
    8. Feltri ML
    9. Schöneberg T
    10. Piao X
    11. Monk KR

    (2015) The adhesion GPCR GPR126 has distinct, domain-dependent functions in schwann cell development mediated by interaction with laminin-211

    Neuron 85:755–769.

    https://doi.org/10.1016/j.neuron.2014.12.057

    • PubMed
    • Google Scholar
62. 1. Poureetezadi SJ
    2. Cheng CN
    3. Chambers JM
    4. Drummond BE
    5. Wingert RA

    (2016) Prostaglandin signaling regulates nephron segment patterning of renal progenitors during zebrafish kidney development

    eLife 5:e17551.

    https://doi.org/10.7554/eLife.17551

    • PubMed
    • Google Scholar
63. 1. Prasanna X
    2. Sengupta D
    3. Chattopadhyay A

    (2016) Cholesterol-dependent conformational plasticity in GPCR dimers

    Scientific Reports 6:e31858.

    https://doi.org/10.1038/srep31858

    • PubMed
    • Google Scholar
64. 1. Rampe D
    2. Anderson B
    3. Rapien-Pryor V
    4. Li T
    5. Dage RC

    (1993)

    Comparison of the in vitro and in vivo cardiovascular effects of two structurally distinct ca++ channel activators, BAY K 8644 and FPL 64176

    The Journal of Pharmacology and Experimental Therapeutics 265:1125–1130.

    • PubMed
    • Google Scholar
65. 1. Ravenscroft G
    2. Nolent F
    3. Rajagopalan S
    4. Meireles AM
    5. Paavola KJ
    6. Gaillard D
    7. Alanio E
    8. Buckland M
    9. Arbuckle S
    10. Krivanek M
    11. Maluenda J
    12. Pannell S
    13. Gooding R
    14. Ong RW
    15. Allcock RJ
    16. Carvalho ED
    17. Carvalho MD
    18. Kok F
    19. Talbot WS
    20. Melki J
    21. Laing NG

    (2015) Mutations of GPR126 are responsible for severe arthrogryposis multiplex congenita

    The American Journal of Human Genetics 96:955–961.

    https://doi.org/10.1016/j.ajhg.2015.04.014

    • PubMed
    • Google Scholar
66. 1. Rennekamp AJ
    2. Huang XP
    3. Wang Y
    4. Patel S
    5. Lorello PJ
    6. Cade L
    7. Gonzales AP
    8. Yeh JR
    9. Caldarone BJ
    10. Roth BL
    11. Kokel D
    12. Peterson RT

    (2016) σ1 receptor ligands control a switch between passive and active threat responses

    Nature Chemical Biology 12:552–558.

    https://doi.org/10.1038/nchembio.2089

    • PubMed
    • Google Scholar
67. 1. Ricciardelli C
    2. Sakko AJ
    3. Ween MP
    4. Russell DL
    5. Horsfall DJ

    (2009) The biological role and regulation of versican levels in cancer

    Cancer and Metastasis Reviews 28:233–245.

    https://doi.org/10.1007/s10555-009-9182-y

    • PubMed
    • Google Scholar
68. 1. Richter S
    2. Schulze U
    3. Tomançak P
    4. Oates AC

    (2017) Small molecule screen in embryonic zebrafish using modular variations to target segmentation

    Nature Communications 8:1901.

    https://doi.org/10.1038/s41467-017-01469-5

    • PubMed
    • Google Scholar
69. 1. Rogers D
    2. Hahn M

    (2010) Extended-connectivity fingerprints

    Journal of Chemical Information and Modeling 50:742–754.

    https://doi.org/10.1021/ci100050t

    • PubMed
    • Google Scholar
70. 1. Salzman GS
    2. Ackerman SD
    3. Ding C
    4. Koide A
    5. Leon K
    6. Luo R
    7. Stoveken HM
    8. Fernandez CG
    9. Tall GG
    10. Piao X
    11. Monk KR
    12. Koide S
    13. Araç D

    (2016) Structural basis for regulation of GPR56/ADGRG1 by its alternatively spliced extracellular domains

    Neuron 91:1292–1304.

    https://doi.org/10.1016/j.neuron.2016.08.022

    • PubMed
    • Google Scholar
71. 1. Salzman GS
    2. Zhang S
    3. Gupta A
    4. Koide A
    5. Koide S
    6. Araç D

    (2017) Stachel-independent modulation of GPR56/ADGRG1 signaling by synthetic ligands directed to its extracellular region

    PNAS 114:10095–10100.

    https://doi.org/10.1073/pnas.1708810114

    • PubMed
    • Google Scholar
72. 1. Schampel A
    2. Volovitch O
    3. Koeniger T
    4. Scholz CJ
    5. Jörg S
    6. Linker RA
    7. Wischmeyer E
    8. Wunsch M
    9. Hell JW
    10. Ergün S
    11. Kuerten S

    (2017) Nimodipine fosters remyelination in a mouse model of multiple sclerosis and induces microglia-specific apoptosis

    PNAS 114:E3295–E3304.

    https://doi.org/10.1073/pnas.1620052114

    • PubMed
    • Google Scholar
73. 1. Shannon P
    2. Markiel A
    3. Ozier O
    4. Baliga NS
    5. Wang JT
    6. Ramage D
    7. Amin N
    8. Schwikowski B
    9. Ideker T

    (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks

    Genome Research 13:2498–2504.

    https://doi.org/10.1101/gr.1239303

    • PubMed
    • Google Scholar
74. 1. Sharma RK
    2. Wang JH
    3. Wu Z

    (1997) Mechanisms of inhibition of calmodulin-stimulated cyclic nucleotide phosphodiesterase by dihydropyridine calcium antagonists

    Journal of Neurochemistry 69:845–850.

    https://doi.org/10.1046/j.1471-4159.1997.69020845.x

    • PubMed
    • Google Scholar
75. 1. Siddiqi FH
    2. Menzies FM
    3. Lopez A
    4. Stamatakou E
    5. Karabiyik C
    6. Ureshino R
    7. Ricketts T
    8. Jimenez-Sanchez M
    9. Esteban MA
    10. Lai L
    11. Tortorella MD
    12. Luo Z
    13. Liu H
    14. Metzakopian E
    15. Fernandes HJR
    16. Bassett A
    17. Karran E
    18. Miller BL
    19. Fleming A
    20. Rubinsztein DC

    (2019) Felodipine induces autophagy in mouse brains with pharmacokinetics amenable to repurposing

    Nature Communications 10:1817.

    https://doi.org/10.1038/s41467-019-09494-2

    • PubMed
    • Google Scholar
76. 1. Silver J
    2. Miller JH

    (2004) Regeneration beyond the glial scar

    Nature Reviews Neuroscience 5:146–156.

    https://doi.org/10.1038/nrn1326

    • PubMed
    • Google Scholar
77. 1. Sriram K
    2. Insel PA

    (2018) G Protein-Coupled receptors as targets for approved drugs: how many targets and how many drugs?

    Molecular Pharmacology 93:251–258.

    https://doi.org/10.1124/mol.117.111062

    • PubMed
    • Google Scholar
78. 1. Stoveken HM
    2. Larsen SD
    3. Smrcka AV
    4. Tall GG

    (2018) Gedunin- and Khivorin-Derivatives are Small-Molecule partial agonists for adhesion G Protein-Coupled receptors GPR56/ADGRG1 and GPR114/ADGRG5

    Molecular Pharmacology 93:477–488.

    https://doi.org/10.1124/mol.117.111476

    • PubMed
    • Google Scholar
79. 1. Subramani R
    2. Gonzalez E
    3. Nandy SB
    4. Arumugam A
    5. Camacho F
    6. Medel J
    7. Alabi D
    8. Lakshmanaswamy R

    (2017) Gedunin inhibits pancreatic cancer by altering sonic hedgehog signaling pathway

    Oncotarget 8:10891–10904.

    https://doi.org/10.18632/oncotarget.8055

    • PubMed
    • Google Scholar
80. 1. Tang YD
    2. Zheng XS
    3. Ying TT
    4. Yuan Y
    5. Li ST

    (2015) Nimodipine-mediated re-myelination after facial nerve crush injury in rats

    Journal of Clinical Neuroscience 22:1661–1668.

    https://doi.org/10.1016/j.jocn.2015.03.048

    • PubMed
    • Google Scholar
81. 1. Thisse C
    2. Thisse B

    (2008) High-resolution in situ hybridization to whole-mount zebrafish embryos

    Nature Protocols 3:59–69.

    https://doi.org/10.1038/nprot.2007.514

    • Google Scholar
82. 1. Thompson SA
    2. Wingrove PB
    3. Connelly L
    4. Whiting PJ
    5. Wafford KA

    (2002) Tracazolate reveals a novel type of allosteric interaction with recombinant gamma-aminobutyric acid(A) receptors

    Molecular Pharmacology 61:861–869.

    https://doi.org/10.1124/mol.61.4.861

    • PubMed
    • Google Scholar
83. 1. Tocci G
    2. Desideri G
    3. Roca E
    4. Calcullo C
    5. Crippa M
    6. De Luca N
    7. Gaudio GV
    8. Lonati LM
    9. Orselli L
    10. Scuteri A
    11. Vulpis V
    12. Acone B
    13. Zaninelli A
    14. THYPERevolution Steering Committee

    (2018) How to improve effectiveness and adherence to antihypertensive drug therapy: central role of dihydropyridinic calcium channel blockers in hypertension

    High Blood Pressure & Cardiovascular Prevention 25:25–34.

    https://doi.org/10.1007/s40292-017-0242-z

    • PubMed
    • Google Scholar
84. 1. Urban N
    2. Wang L
    3. Kwiek S
    4. Rademann J
    5. Kuebler WM
    6. Schaefer M

    (2016) Identification and validation of larixyl acetate as a potent TRPC6 inhibitor

    Molecular Pharmacology 89:197–213.

    https://doi.org/10.1124/mol.115.100792

    • PubMed
    • Google Scholar
85. 1. Vettori A
    2. Greenald D
    3. Wilson GK
    4. Peron M
    5. Facchinello N
    6. Markham E
    7. Sinnakaruppan M
    8. Matthews LC
    9. McKeating JA
    10. Argenton F
    11. van Eeden FJM

    (2017) Glucocorticoids promote Von Hippel lindau degradation and Hif-1α stabilization

    PNAS 114:9948–9953.

    https://doi.org/10.1073/pnas.1705338114

    • PubMed
    • Google Scholar
86. 1. Waller-Evans H
    2. Prömel S
    3. Langenhan T
    4. Dixon J
    5. Zahn D
    6. Colledge WH
    7. Doran J
    8. Carlton MB
    9. Davies B
    10. Aparicio SA
    11. Grosse J
    12. Russ AP

    (2010) The orphan adhesion-GPCR GPR126 is required for embryonic development in the mouse

    PLOS ONE 5:e14047.

    https://doi.org/10.1371/journal.pone.0014047

    • PubMed
    • Google Scholar
87. 1. Weininger D

    (1988) SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules

    Journal of Chemical Information and Modeling 28:31–36.

    https://doi.org/10.1021/ci00057a005

    • Google Scholar
88. 1. Weisman JL
    2. Liou AP
    3. Shelat AA
    4. Cohen FE
    5. Guy RK
    6. DeRisi JL

    (2006) Searching for new antimalarial therapeutics amongst known drugs

    Chemical Biology & Drug Design 67:409–416.

    https://doi.org/10.1111/j.1747-0285.2006.00391.x

    • PubMed
    • Google Scholar
89. Book

    1. Westerfield M

    (2000)

    The Zebrafish Book: A Guide for the Laboratory Use of Zebrafish (Danio rerio) (4th Edition)

    Eugene: University of Oregon Press.

    • Google Scholar
90. 1. Whitfield TT
    2. Granato M
    3. van Eeden FJ
    4. Schach U
    5. Brand M
    6. Furutani-Seiki M
    7. Haffter P
    8. Hammerschmidt M
    9. Heisenberg CP
    10. Jiang YJ
    11. Kane DA
    12. Kelsh RN
    13. Mullins MC
    14. Odenthal J
    15. Nüsslein-Volhard C

    (1996)

    Mutations affecting development of the zebrafish inner ear and lateral line

    Development 123:241–254.

    • PubMed
    • Google Scholar
91. 1. Wight TN
    2. Frevert CW
    3. Debley JS
    4. Reeves SR
    5. Parks WC
    6. Ziegler SF

    (2017) Interplay of extracellular matrix and leukocytes in lung inflammation

    Cellular Immunology 312:1–14.

    https://doi.org/10.1016/j.cellimm.2016.12.003

    • PubMed
    • Google Scholar
92. 1. Wiley DS
    2. Redfield SE
    3. Zon LI

    (2017) Chemical screening in zebrafish for novel biological and therapeutic discovery

    Methods in Cell Biology 138:651–679.

    https://doi.org/10.1016/bs.mcb.2016.10.004

    • PubMed
    • Google Scholar
93. 1. Willett P
    2. Barnard JM
    3. Downs GM

    (1998) Chemical similarity searching

    Journal of Chemical Information and Computer Sciences 38:983–996.

    https://doi.org/10.1021/ci9800211

    • Google Scholar
94. 1. Wootten D
    2. Christopoulos A
    3. Marti-Solano M
    4. Babu MM
    5. Sexton PM

    (2018) Mechanisms of signalling and biased agonism in G protein-coupled receptors

    Nature Reviews Molecular Cell Biology 19:638–653.

    https://doi.org/10.1038/s41580-018-0049-3

    • PubMed
    • Google Scholar
95. 1. Yu PB
    2. Hong CC
    3. Sachidanandan C
    4. Babitt JL
    5. Deng DY
    6. Hoyng SA
    7. Lin HY
    8. Bloch KD
    9. Peterson RT

    (2008) Dorsomorphin inhibits BMP signals required for embryogenesis and iron metabolism

    Nature Chemical Biology 4:33–41.

    https://doi.org/10.1038/nchembio.2007.54

    • PubMed
    • Google Scholar
96. 1. Zada D
    2. Tovin A
    3. Lerer-Goldshtein T
    4. Appelbaum L

    (2016) Pharmacological treatment and BBB-targeted genetic therapy for MCT8-dependent hypomyelination in zebrafish

    Disease Models & Mechanisms 9:1339–1348.

    https://doi.org/10.1242/dmm.027227

    • PubMed
    • Google Scholar
97. 1. Zhang XD

    (2007) A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays

    Genomics 89:552–561.

    https://doi.org/10.1016/j.ygeno.2006.12.014

    • PubMed
    • Google Scholar

Author details

1. Elvira Diamantopoulou

   Bateson Centre and Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Writing—original draft, Writing—review and editing

   Contributed equally with

   Sarah Baxendale

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9336-7965

2. Sarah Baxendale

   Bateson Centre and Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Writing—original draft, Project administration, Writing—review and editing

   Contributed equally with

   Elvira Diamantopoulou

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6760-9457

3. Antonio de la Vega de León

   Information School, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Data curation, Formal analysis, Validation, Visualization, Writing—review and editing, Chemoinformatics analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0927-2099

4. Anzar Asad

   Bateson Centre and Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Data curation, Validation, Investigation, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

5. Celia J Holdsworth

   Bateson Centre and Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Leila Abbas

   Bateson Centre and Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Conceptualization, Investigation

   Competing interests

   No competing interests declared

7. Valerie J Gillet

   Information School, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Visualization, Writing—review and editing, Chemoinformatics analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8403-3111

8. Giselle R Wiggin

   Sosei Heptares, Cambridge, United Kingdom

   Contribution

   Conceptualization, Supervision, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4436-9208

9. Tanya T Whitfield

   Bateson Centre and Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   t.whitfield@sheffield.ac.uk

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0003-1575-1504

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