Zebrafish behavioural profiling identifies GABA and serotonin receptor ligands related to sedation and paradoxical excitation.

Anesthetics are generally associated with sedation, but some anesthetics can also increase brain and motor activity-a phenomenon known as paradoxical excitation. Previous studies have identified GABAA receptors as the primary targets of most anesthetic drugs, but how these compounds produce paradoxical excitation is poorly understood. To identify and understand such compounds, we applied a behavior-based drug profiling approach. Here, we show that a subset of central nervous system depressants cause paradoxical excitation in zebrafish. Using this behavior as a readout, we screened thousands of compounds and identified dozens of hits that caused paradoxical excitation. Many hit compounds modulated human GABAA receptors, while others appeared to modulate different neuronal targets, including the human serotonin-6 receptor. Ligands at these receptors generally decreased neuronal activity, but paradoxically increased activity in the caudal hindbrain. Together, these studies identify ligands, targets, and neurons affecting sedation and paradoxical excitation in vivo in zebrafish

The Contribution of Fermi Gamma-Ray Pulsars to the local Flux of Cosmic-Ray Electrons and Positrons

We analyze the contribution of gamma-ray pulsars from the first Fermi-Large Area Telescope (LAT) catalogue to the local flux of cosmic-ray electrons and positrons (e+e-). We present new distance estimates for all Fermi gamma-ray pulsars, based on the measured gamma-ray flux and pulse shape. We then estimate the contribution of gamma-ray pulsars to the local e+e- flux, in the context of a simple model for the pulsar e+e- emission. We find that 10 of the Fermi pulsars potentially contribute significantly to the measured e+e- flux in the energy range between 100 GeV and 1 TeV. Of the 10 pulsars, 2 are old EGRET gamma-ray pulsars, 2 pulsars were discovered with radio ephemerides, and 6 were discovered with the Fermi pulsar blind-search campaign. We argue that known radio pulsars fall in regions of parameter space where the e+e- contribution is predicted to be typically much smaller than from those regions where Fermi-LAT pulsars exist. However, comparing the Fermi gamma-ray flux sensitivity to the regions of pulsar parameter space where a significant e+e- contribution is predicted, we find that a few known radio pulsars that have not yet been detected by Fermi can also significantly contribute to the local e+e- flux if (i) they are closer than 2 kpc, and if (ii) they have a characteristic age on the order of one mega-year.Comment: 21 pages, 6 figures, accepted for publication in JCA

Evolution of Blister-Type HII Regions in a Magnetized Medium

We use the three-dimensional Athena ionizing radiation-magnetohydrodynamics (IRMHD) code to simulate blister-type HII regions driven by stars on the edge of magnetized gas clouds. We compare these to simulations of spherical HII regions where the star is embedded deep within a cloud, and to non-magnetized simulations of both types, in order to compare their ability to drive turbulence and influence star formation. We find that magnetized blister HII regions can be very efficient at injecting energy into clouds. This is partly a magnetic effect: the magnetic energy added to a cloud by an HII region is comparable to or larger than the kinetic energy, and magnetic fields can also help collimate the ejected gas, increasing its energy yield. As a result of these effects, a blister HII region expanding into a cloud with a magnetic field perpendicular to its edge injects twice as much energy by 5 Myr as a non-magnetized blister HII region driven by a star of the same luminosity. Blister HII regions are also more efficient at injecting kinetic energy than spherical HII regions, due to the recoil provided by escaping gas, but not by as much as predicted by some analytic approximations.Comment: 15 pages, 17 figures, 1 tabl

Deep phenotypic profiling of neuroactive drugs in larval zebrafish

In-vivo phenotypic screening in larval zebrafish has shown much promise for neuroactive drug discovery, but unleashing its full potential remains a challenge. How do we robustly quantify how drugs modulate zebrafish behavior, and in parallel, how do we unravel which targets or pathways they act by? We develop a phenotypic screening and computational pipeline to begin meeting these challenges. Starting with motion index (MI) as a readout for phenotype, and correlation distance (CD) as a measure of phenotypic similarity, we extend the similarity ensemble approach to computationally predict targets for sets of phenotypic screening hits. Using this approach, we predict an “antipsychotic” target profile for previously uncharacterized hit compounds with MI’s matching those of known antipsychotic compounds. For a novel phenotype associated with sedation and paradoxical excitation caused by anesthetics such as etomidate and propofol, we predict not only the canonical GABAergic pathway, but a novel target entirely; the serotonin-6 receptor, which we validate with both in-vitro and in-vivo experiments. However, our initial attempts at extending this approach to other known drug classes such as stimulants and convulsants are met with unexpected challenges; we hypothesize that the MI signatures and the CD used to compare might not be robust enough for these more subtle phenotypes. And so the Deepfish project is born. We train Siamese Neural Networks (SNNs) on a highly replicated screen of 650 known neuroactive drugs to learn a custom distance metric for comparing MI. This new distance function scores higher than CD at the task of separating same-drug replicate pairs versus different-drug pairs, all while generalizing to a quality control screen done months prior. In that arena, the new distance metric gets higher classification accuracy on average, but also strikingly outperforms CD for 3 drugs with more subtle phenotypes. Armed with a way of training robust distance metrics, we make progress on using unsupervised deep-learning approaches to find more robust representations of behavior. We discover that for computing similarities between these high-dimensional embedded fingerprints, training custom distance metrics is even more imperative. However, we see signs that overfitting is possible with the Siamese Networks on our highly-replicated dataset - both with the raw MI and high-dimensional embedded representations - so we design and perform a version of the screen with fully randomized drug layouts, which we will use to benchmark our methods in the near future

Deep phenotypic profiling of neuroactive drugs in larval zebrafish

In-vivo phenotypic screening in larval zebrafish has shown much promise for neuroactive drug discovery, but unleashing its full potential remains a challenge. How do we robustly quantify how drugs modulate zebrafish behavior, and in parallel, how do we unravel which targets or pathways they act by? We develop a phenotypic screening and computational pipeline to begin meeting these challenges. Starting with motion index (MI) as a readout for phenotype, and correlation distance (CD) as a measure of phenotypic similarity, we extend the similarity ensemble approach to computationally predict targets for sets of phenotypic screening hits. Using this approach, we predict an “antipsychotic” target profile for previously uncharacterized hit compounds with MI’s matching those of known antipsychotic compounds. For a novel phenotype associated with sedation and paradoxical excitation caused by anesthetics such as etomidate and propofol, we predict not only the canonical GABAergic pathway, but a novel target entirely; the serotonin-6 receptor, which we validate with both in-vitro and in-vivo experiments. However, our initial attempts at extending this approach to other known drug classes such as stimulants and convulsants are met with unexpected challenges; we hypothesize that the MI signatures and the CD used to compare might not be robust enough for these more subtle phenotypes. And so the Deepfish project is born. We train Siamese Neural Networks (SNNs) on a highly replicated screen of 650 known neuroactive drugs to learn a custom distance metric for comparing MI. This new distance function scores higher than CD at the task of separating same-drug replicate pairs versus different-drug pairs, all while generalizing to a quality control screen done months prior. In that arena, the new distance metric gets higher classification accuracy on average, but also strikingly outperforms CD for 3 drugs with more subtle phenotypes. Armed with a way of training robust distance metrics, we make progress on using unsupervised deep-learning approaches to find more robust representations of behavior. We discover that for computing similarities between these high-dimensional embedded fingerprints, training custom distance metrics is even more imperative. However, we see signs that overfitting is possible with the Siamese Networks on our highly-replicated dataset - both with the raw MI and high-dimensional embedded representations - so we design and perform a version of the screen with fully randomized drug layouts, which we will use to benchmark our methods in the near future

Leveraging Large-scale Behavioral Profiling in Zebrafish to Explore Neuroactive Polypharmacology

Many psychiatric drugs modulate the nervous system through multitarget mechanisms. However, systematic identification of multitarget compounds has been difficult using traditional in vitro screening assays. New approaches to phenotypic profiling in zebrafish can help researchers identify novel compounds with complex polypharmacology. For example, large-scale behavior-based chemical screens can rapidly identify large numbers of structurally diverse and phenotype-related compounds. Once these compounds have been identified, a systems-level analysis of their structures may help to identify statistically enriched target pathways. Together, systematic behavioral profiling and multitarget predictions may help researchers identify new behavior-modifying pathways and CNS therapeutics

A Simple Representation of Three-Dimensional Molecular Structure

Statistical and machine learning approaches predict drug-to-target relationships from 2D small-molecule topology patterns. One might expect 3D information to improve these calculations. Here we apply the logic of the extended connectivity fingerprint (ECFP) to develop a rapid, alignment-invariant 3D representation of molecular conformers, the extended three-dimensional fingerprint (E3FP). By integrating E3FP with the similarity ensemble approach (SEA), we achieve higher precision-recall performance relative to SEA with ECFP on ChEMBL20 and equivalent receiver operating characteristic performance. We identify classes of molecules for which E3FP is a better predictor of similarity in bioactivity than is ECFP. Finally, we report novel drug-to-target binding predictions inaccessible by 2D fingerprints and confirm three of them experimentally with ligand efficiencies from 0.442–0.637 kcal/mol/heavy atom

Zebrafish behavioral profiling identifies multi-target antipsychotic-like compounds

Many psychiatric drugs act on multiple targets and therefore require screening assays that encompass a wide target space. With sufficiently rich phenotyping, and a large sampling of compounds, it should be possible to identify compounds with desired mechanisms of action based on their behavioral profiles alone. Although zebrafish (Danio rerio) behaviors have been used to rapidly identify neuroactive compounds, it remains unclear exactly what kind of behavioral assays might be necessary to identify multi-target compounds such as antipsychotics. Here, we developed a battery of behavioral assays in larval zebrafish to determine if behavioral profiles could provide sufficient phenotypic resolution to identify and classify psychiatric drugs. Using the antipsychotic drug haloperidol as a test case, we found that behavioral profiles of haloperidol-treated animals could be used to identify previously uncharacterized compounds with desired antipsychotic-like activities and multi-target mechanisms of action