1
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Tanaka R, Portugues R. On analogies in vertebrate and insect visual systems. Nat Rev Neurosci 2025:10.1038/s41583-025-00932-3. [PMID: 40410391 DOI: 10.1038/s41583-025-00932-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/01/2025] [Indexed: 05/25/2025]
Abstract
Despite the large evolutionary distance between vertebrates and insects, the visual systems of these two taxa bear remarkable similarities that have been noted repeatedly, including by pioneering neuroanatomists such as Ramón y Cajal. Fuelled by the advent of transgenic approaches in neuroscience, studies of visual system anatomy and function in both vertebrates and insects have made dramatic progress during the past two decades, revealing even deeper analogies between their visual systems than were noted by earlier observers. Such across-taxa comparisons have tended to focus on either elementary motion detection or relatively peripheral layers of the visual systems. By contrast, the aims of this Review are to expand the scope of this comparison to pathways outside visual motion detection, as well as to deeper visual structures. To achieve these aims, we primarily discuss examples from recent work in larval zebrafish (Danio rerio) and the fruitfly (Drosophila melanogaster), a pair of genetically tractable model organisms with comparatively sized, small brains. In particular, we argue that the brains of both vertebrates and insects are equipped with third-order visual structures that specialize in shared behavioural tasks, including postural and course stabilization, approach and avoidance, and some other behaviours. These wider analogies between the two distant taxa highlight shared behavioural goals and associated evolutionary constraints and suggest that studies on vertebrate and insect vision have a lot to inspire each other.
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Affiliation(s)
- Ryosuke Tanaka
- Institute of Neuroscience, Technical University of Munich, Munich, Germany.
| | - Ruben Portugues
- Institute of Neuroscience, Technical University of Munich, Munich, Germany.
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany.
- Max Planck Fellow Group - Mechanisms of Cognition, MPI Psychiatry, Munich, Germany.
- Bernstein Center for Computational Neuroscience Munich, Munich, Germany.
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2
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Nern A, Loesche F, Takemura SY, Burnett LE, Dreher M, Gruntman E, Hoeller J, Huang GB, Januszewski M, Klapoetke NC, Koskela S, Longden KD, Lu Z, Preibisch S, Qiu W, Rogers EM, Seenivasan P, Zhao A, Bogovic J, Canino BS, Clements J, Cook M, Finley-May S, Flynn MA, Hameed I, Fragniere AMC, Hayworth KJ, Hopkins GP, Hubbard PM, Katz WT, Kovalyak J, Lauchie SA, Leonard M, Lohff A, Maldonado CA, Mooney C, Okeoma N, Olbris DJ, Ordish C, Paterson T, Phillips EM, Pietzsch T, Salinas JR, Rivlin PK, Schlegel P, Scott AL, Scuderi LA, Takemura S, Talebi I, Thomson A, Trautman ET, Umayam L, Walsh C, Walsh JJ, Xu CS, Yakal EA, Yang T, Zhao T, Funke J, George R, Hess HF, Jefferis GSXE, Knecht C, Korff W, Plaza SM, Romani S, Saalfeld S, Scheffer LK, Berg S, Rubin GM, Reiser MB. Connectome-driven neural inventory of a complete visual system. Nature 2025; 641:1225-1237. [PMID: 40140576 PMCID: PMC12119369 DOI: 10.1038/s41586-025-08746-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 02/06/2025] [Indexed: 03/28/2025]
Abstract
Vision provides animals with detailed information about their surroundings and conveys diverse features such as colour, form and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons. Consequently, from flies to humans, visual regions in the brain constitute half its volume. These visual regions often have marked structure-function relationships, with neurons organized along spatial maps and with shapes that directly relate to their roles in visual processing. More than a century of anatomical studies have catalogued in detail cell types in fly visual systems1-3, and parallel behavioural and physiological experiments have examined the visual capabilities of flies. To unravel the diversity of a complex visual system, careful mapping of the neural architecture matched to tools for targeted exploration of this circuitry is essential. Here we present a connectome of the right optic lobe from a male Drosophila melanogaster acquired using focused ion beam milling and scanning electron microscopy. We established a comprehensive inventory of the visual neurons and developed a computational framework to quantify their anatomy. Together, these data establish a basis for interpreting how the shapes of visual neurons relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity and expert curation, we classified the approximately 53,000 neurons into 732 types. These types are systematically described and about half are newly named. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron-type catalogue. Overall, this comprehensive set of tools and data unlocks new possibilities for systematic investigations of vision in Drosophila and provides a foundation for a deeper understanding of sensory processing.
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Affiliation(s)
- Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Frank Loesche
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Shin-Ya Takemura
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Laura E Burnett
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Marisa Dreher
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Judith Hoeller
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Gary B Huang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Nathan C Klapoetke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Sanna Koskela
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Kit D Longden
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Zhiyuan Lu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Stephan Preibisch
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Wei Qiu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Edward M Rogers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Arthur Zhao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - John Bogovic
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Brandon S Canino
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Jody Clements
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Michael Cook
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Miriam A Flynn
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Imran Hameed
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Alexandra M C Fragniere
- MRC Laboratory of Molecular Biology, Cambridge, UK
- Department of Zoology, Cambridge University, Cambridge, UK
| | - Kenneth J Hayworth
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Philip M Hubbard
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - William T Katz
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Julie Kovalyak
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Shirley A Lauchie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Meghan Leonard
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Alanna Lohff
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Charli A Maldonado
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Caroline Mooney
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Nneoma Okeoma
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Donald J Olbris
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Christopher Ordish
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Tyler Paterson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Emily M Phillips
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Tobias Pietzsch
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Patricia K Rivlin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Philipp Schlegel
- MRC Laboratory of Molecular Biology, Cambridge, UK
- Department of Zoology, Cambridge University, Cambridge, UK
| | - Ashley L Scott
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Louis A Scuderi
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Satoko Takemura
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Iris Talebi
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Alexander Thomson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Eric T Trautman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Lowell Umayam
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Claire Walsh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - John J Walsh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - C Shan Xu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Emily A Yakal
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Tansy Yang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Ting Zhao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Reed George
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Harald F Hess
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Gregory S X E Jefferis
- MRC Laboratory of Molecular Biology, Cambridge, UK
- Department of Zoology, Cambridge University, Cambridge, UK
| | - Christopher Knecht
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Wyatt Korff
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Stephen M Plaza
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Stephan Saalfeld
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Louis K Scheffer
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Stuart Berg
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
| | - Michael B Reiser
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
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3
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Nöbel S, Danchin E, Isabel G. Long-term social memory of mate copying in Drosophila melanogaster is localized in mushroom bodies. Sci Rep 2025; 15:5262. [PMID: 39939402 PMCID: PMC11822108 DOI: 10.1038/s41598-025-88535-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2024] [Accepted: 01/29/2025] [Indexed: 02/14/2025] Open
Abstract
Long-term social memory (LTSM) is a key feature to elicit the cultural inheritance of behaviour independently of genetics. However, the neurobiological basis of LTSM remains largely unknown. We previously used the Drosophila animal model, which is known to perform mate copying through observational learning of the mate choice of conspecifics to show that the expression of the rutabaga gene, a calcium/calmodulin-dependent adenylyl cyclase (AC-Rut+) that acts as a coincidence detector enabling associative learning, is necessary and sufficient in the γ-Kenyon cells (KCs) of the mushroom bodies (MBs). Here, we show that the expression of AC-Rut+ in both the γ- and the α/β-KCs is required for LTSM involving de novo protein synthesis in a mate-copying context, whether using demonstrations involving real flies or involving pictures of copulating conspecifics. Thus, pathways of short- and long-term memory show considerable overlap in the MBs across social vs. asocial learning contexts.
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Affiliation(s)
- Sabine Nöbel
- Department of Zoology, Animal Ecology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.
- Université Toulouse 1 Capitole and Institute for Advanced Study in Toulouse (IAST), Toulouse, France.
- Laboratoire Évolution & Diversité Biologique (EDB UMR 5174), Université de Toulouse Midi-Pyrénées, CNRS, IRD, UPS. 118 route de Narbonne, 31062, Toulouse, France.
| | - Etienne Danchin
- Laboratoire Évolution & Diversité Biologique (EDB UMR 5174), Université de Toulouse Midi-Pyrénées, CNRS, IRD, UPS. 118 route de Narbonne, 31062, Toulouse, France
- Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), CNRS UMR 5169, Université de Toulouse Midi-Pyrénées, Toulouse, France
| | - Guillaume Isabel
- Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), CNRS UMR 5169, Université de Toulouse Midi-Pyrénées, Toulouse, France
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4
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Shuai Y, Sammons M, Sterne GR, Hibbard KL, Yang H, Yang CP, Managan C, Siwanowicz I, Lee T, Rubin GM, Turner GC, Aso Y. Driver lines for studying associative learning in Drosophila. eLife 2025; 13:RP94168. [PMID: 39879130 PMCID: PMC11778931 DOI: 10.7554/elife.94168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2025] Open
Abstract
The mushroom body (MB) is the center for associative learning in insects. In Drosophila, intersectional split-GAL4 drivers and electron microscopy (EM) connectomes have laid the foundation for precise interrogation of the MB neural circuits. However, investigation of many cell types upstream and downstream of the MB has been hindered due to lack of specific driver lines. Here we describe a new collection of over 800 split-GAL4 and split-LexA drivers that cover approximately 300 cell types, including sugar sensory neurons, putative nociceptive ascending neurons, olfactory and thermo-/hygro-sensory projection neurons, interneurons connected with the MB-extrinsic neurons, and various other cell types. We characterized activation phenotypes for a subset of these lines and identified a sugar sensory neuron line most suitable for reward substitution. Leveraging the thousands of confocal microscopy images associated with the collection, we analyzed neuronal morphological stereotypy and discovered that one set of mushroom body output neurons, MBON08/MBON09, exhibits striking individuality and asymmetry across animals. In conjunction with the EM connectome maps, the driver lines reported here offer a powerful resource for functional dissection of neural circuits for associative learning in adult Drosophila.
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Affiliation(s)
- Yichun Shuai
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Megan Sammons
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gabriella R Sterne
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Karen L Hibbard
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - He Yang
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Ching-Po Yang
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Claire Managan
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Igor Siwanowicz
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Tzumin Lee
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Glenn C Turner
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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5
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Farnworth MS, Loupasaki T, Couto A, Montgomery SH. Mosaic evolution of a learning and memory circuit in Heliconiini butterflies. Curr Biol 2024; 34:5252-5262.e5. [PMID: 39426379 DOI: 10.1016/j.cub.2024.09.069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 09/05/2024] [Accepted: 09/24/2024] [Indexed: 10/21/2024]
Abstract
How do neural circuits accommodate changes that produce cognitive variation? We explore this question by analyzing the evolutionary dynamics of an insect learning and memory circuit centered within the mushroom body. Mushroom bodies are composed of a conserved wiring logic, mainly consisting of Kenyon cells, dopaminergic neurons, and mushroom body output neurons. Despite this conserved makeup, there is huge diversity in mushroom body size and shape across insects. However, empirical data on how evolution modifies the function and architecture of this circuit are largely lacking. To address this, we leverage the recent radiation of a Neotropical tribe of butterflies, the Heliconiini (Nymphalidae), which show extensive variation in mushroom body size over comparatively short phylogenetic timescales, linked to specific changes in foraging ecology, life history, and cognition. To understand how such an extensive increase in size is accommodated through changes in lobe circuit architecture, we combined immunostainings of structural markers, neurotransmitters, and neural injections to generate new, quantitative anatomies of the Nymphalid mushroom body lobe. Our comparative analyses across Heliconiini demonstrate that some Kenyon cell sub-populations expanded at higher rates than others in Heliconius and identify an additional increase in GABA-ergic feedback neurons, which are essential for non-elemental learning and sparse coding. Taken together, our results demonstrate mosaic evolution of functionally related neural systems and cell types and identify that evolutionary malleability in an architecturally conserved parallel circuit guides adaptation in cognitive ability.
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Affiliation(s)
- Max S Farnworth
- Evolution of Brains and Behaviour lab, School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK.
| | - Theodora Loupasaki
- Evolution of Brains and Behaviour lab, School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
| | - Antoine Couto
- Evolution of Brains and Behaviour lab, School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK; Evolution, Genomes, Behaviour and Ecology (UMR 9191), IDEEV, Université Paris-Saclay, CNRS, IRD, 12 Route 128, Gif-sur-Yvette, 91190, France
| | - Stephen H Montgomery
- Evolution of Brains and Behaviour lab, School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK.
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6
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Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Lin A, Costa M, Eichler K, Yin Y, Silversmith W, Schneider-Mizell C, Jordan CS, Brittain D, Halageri A, Kuehner K, Ogedengbe O, Morey R, Gager J, Kruk K, Perlman E, Yang R, Deutsch D, Bland D, Sorek M, Lu R, Macrina T, Lee K, Bae JA, Mu S, Nehoran B, Mitchell E, Popovych S, Wu J, Jia Z, Castro MA, Kemnitz N, Ih D, Bates AS, Eckstein N, Funke J, Collman F, Bock DD, Jefferis GSXE, Seung HS, Murthy M. Neuronal wiring diagram of an adult brain. Nature 2024; 634:124-138. [PMID: 39358518 PMCID: PMC11446842 DOI: 10.1038/s41586-024-07558-y] [Citation(s) in RCA: 93] [Impact Index Per Article: 93.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 05/10/2024] [Indexed: 10/04/2024]
Abstract
Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1-6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10-12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome-a map of projections between regions-from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Eyewire, Boston, MA, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Yijie Yin
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Will Silversmith
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Kai Kuehner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Ryan Morey
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jay Gager
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | | | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - David Deutsch
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Department of Neurobiology, University of Haifa, Haifa, Israel
| | - Doug Bland
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marissa Sorek
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Eyewire, Boston, MA, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, NJ, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Manuel A Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Harvard Medical School, Boston, MA, USA
- Centre for Neural Circuits and Behaviour, The University of Oxford, Oxford, UK
| | - Nils Eckstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Davi D Bock
- Department of Neurological Sciences, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Computer Science Department, Princeton University, Princeton, NJ, USA.
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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7
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Zhu J, Boivin JC, Garner A, Ning J, Zhao YQ, Ohyama T. Feedback inhibition by a descending GABAergic neuron regulates timing of escape behavior in Drosophila larvae. eLife 2024; 13:RP93978. [PMID: 39196635 DOI: 10.7554/elife.93978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2024] Open
Abstract
Escape behaviors help animals avoid harm from predators and other threats in the environment. Successful escape relies on integrating information from multiple stimulus modalities (of external or internal origin) to compute trajectories toward safe locations, choose between actions that satisfy competing motivations, and execute other strategies that ensure survival. To this end, escape behaviors must be adaptive. When a Drosophila melanogaster larva encounters a noxious stimulus, such as the focal pressure a parasitic wasp applies to the larval cuticle via its ovipositor, it initiates a characteristic escape response. The escape sequence consists of an initial abrupt bending, lateral rolling, and finally rapid crawling. Previous work has shown that the detection of noxious stimuli primarily relies on class IV multi-dendritic arborization neurons (Class IV neurons) located beneath the body wall, and more recent studies have identified several important components in the nociceptive neural circuitry involved in rolling. However, the neural mechanisms that underlie the rolling-escape sequence remain unclear. Here, we present both functional and anatomical evidence suggesting that bilateral descending neurons within the subesophageal zone of D. melanogaster larva play a crucial role in regulating the termination of rolling and subsequent transition to escape crawling. We demonstrate that these descending neurons (designated SeIN128) are inhibitory and receive inputs from a second-order interneuron upstream (Basin-2) and an ascending neuron downstream of Basin-2 (A00c). Together with optogenetic experiments showing that co-activation of SeIN128 neurons and Basin-2 influence the temporal dynamics of rolling, our findings collectively suggest that the ensemble of SeIN128, Basin-2, and A00c neurons forms a GABAergic feedback loop onto Basin-2, which inhibits rolling and thereby facilitates the shift to escape crawling.
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Affiliation(s)
- Jiayi Zhu
- Department of Biology, McGill University, Montreal, Canada
- Integrated Program of Neuroscience, McGill University, Montreal, Canada
| | - Jean-Christophe Boivin
- Department of Biology, McGill University, Montreal, Canada
- Integrated Program of Neuroscience, McGill University, Montreal, Canada
| | - Alastair Garner
- Department of Biology, McGill University, Montreal, Canada
- Integrated Program of Neuroscience, McGill University, Montreal, Canada
| | - Jing Ning
- Department of Biology, McGill University, Montreal, Canada
| | - Yi Q Zhao
- Department of Biology, McGill University, Montreal, Canada
| | - Tomoko Ohyama
- Department of Biology, McGill University, Montreal, Canada
- Alan Edwards Center for Research on Pain, McGill University, Montreal, Canada
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8
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Frank DD, Kronauer DJC. The Budding Neuroscience of Ant Social Behavior. Annu Rev Neurosci 2024; 47:167-185. [PMID: 38603564 DOI: 10.1146/annurev-neuro-083023-102101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Ant physiology has been fashioned by 100 million years of social evolution. Ants perform many sophisticated social and collective behaviors yet possess nervous systems similar in schematic and scale to that of the fruit fly Drosophila melanogaster, a popular solitary model organism. Ants are thus attractive complementary subjects to investigate adaptations pertaining to complex social behaviors that are absent in flies. Despite research interest in ant behavior and the neurobiological foundations of sociality more broadly, our understanding of the ant nervous system is incomplete. Recent technical advances have enabled cutting-edge investigations of the nervous system in a fashion that is less dependent on model choice, opening the door for mechanistic social insect neuroscience. In this review, we revisit important aspects of what is known about the ant nervous system and behavior, and we look forward to how functional circuit neuroscience in ants will help us understand what distinguishes solitary animals from highly social ones.
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Affiliation(s)
- Dominic D Frank
- Laboratory of Social Evolution and Behavior, The Rockefeller University, New York, NY, USA; ,
| | - Daniel J C Kronauer
- Howard Hughes Medical Institute, New York, NY, USA
- Laboratory of Social Evolution and Behavior, The Rockefeller University, New York, NY, USA; ,
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9
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Thornton-Kolbe EM, Ahmed M, Gordon FR, Sieriebriennikov B, Williams DL, Kurmangaliyev YZ, Clowney EJ. Spatial constraints and cell surface molecule depletion structure a randomly connected learning circuit. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.17.603956. [PMID: 39071296 PMCID: PMC11275898 DOI: 10.1101/2024.07.17.603956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
The brain can represent almost limitless objects to "categorize an unlabeled world" (Edelman, 1989). This feat is supported by expansion layer circuit architectures, in which neurons carrying information about discrete sensory channels make combinatorial connections onto much larger postsynaptic populations. Combinatorial connections in expansion layers are modeled as randomized sets. The extent to which randomized wiring exists in vivo is debated, and how combinatorial connectivity patterns are generated during development is not understood. Non-deterministic wiring algorithms could program such connectivity using minimal genomic information. Here, we investigate anatomic and transcriptional patterns and perturb partner availability to ask how Kenyon cells, the expansion layer neurons of the insect mushroom body, obtain combinatorial input from olfactory projection neurons. Olfactory projection neurons form their presynaptic outputs in an orderly, predictable, and biased fashion. We find that Kenyon cells accept spatially co-located but molecularly heterogeneous inputs from this orderly map, and ask how Kenyon cell surface molecule expression impacts partner choice. Cell surface immunoglobulins are broadly depleted in Kenyon cells, and we propose that this allows them to form connections with molecularly heterogeneous partners. This model can explain how developmentally identical neurons acquire diverse wiring identities.
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Affiliation(s)
- Emma M. Thornton-Kolbe
- Neurosciences Graduate Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Maria Ahmed
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Finley R. Gordon
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | | | - Donnell L. Williams
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | | | - E. Josephine Clowney
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
- Michigan Neuroscience Institute, Ann Arbor, MI, USA
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10
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Ganguly I, Heckman EL, Litwin-Kumar A, Clowney EJ, Behnia R. Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body. Nat Commun 2024; 15:5698. [PMID: 38972924 PMCID: PMC11228034 DOI: 10.1038/s41467-024-49616-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 06/11/2024] [Indexed: 07/09/2024] Open
Abstract
The arthropod mushroom body is well-studied as an expansion layer representing olfactory stimuli and linking them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, and their function remains unclear. Here, we identify inputs to visual Kenyon cells using the FlyWire adult whole-brain connectome. Input repertoires are similar across hemispheres and connectomes with certain inputs highly overrepresented. Many visual neurons presynaptic to Kenyon cells have large receptive fields, while interneuron inputs receive spatially restricted signals that may be tuned to specific visual features. Individual visual Kenyon cells randomly sample sparse inputs from combinations of visual channels, including multiple optic lobe neuropils. These connectivity patterns suggest that visual coding in the mushroom body, like olfactory coding, is sparse, distributed, and combinatorial. However, the specific input repertoire to the smaller population of visual Kenyon cells suggests a constrained encoding of visual stimuli.
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Affiliation(s)
- Ishani Ganguly
- Department of Neuroscience, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Emily L Heckman
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Ashok Litwin-Kumar
- Department of Neuroscience, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - E Josephine Clowney
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA.
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA.
| | - Rudy Behnia
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Zuckerman Institute, Columbia University, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
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11
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Fulton KA, Zimmerman D, Samuel A, Vogt K, Datta SR. Common principles for odour coding across vertebrates and invertebrates. Nat Rev Neurosci 2024; 25:453-472. [PMID: 38806946 DOI: 10.1038/s41583-024-00822-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2024] [Indexed: 05/30/2024]
Abstract
The olfactory system is an ideal and tractable system for exploring how the brain transforms sensory inputs into behaviour. The basic tasks of any olfactory system include odour detection, discrimination and categorization. The challenge for the olfactory system is to transform the high-dimensional space of olfactory stimuli into the much smaller space of perceived objects and valence that endows odours with meaning. Our current understanding of how neural circuits address this challenge has come primarily from observations of the mechanisms of the brain for processing other sensory modalities, such as vision and hearing, in which optimized deep hierarchical circuits are used to extract sensory features that vary along continuous physical dimensions. The olfactory system, by contrast, contends with an ill-defined, high-dimensional stimulus space and discrete stimuli using a circuit architecture that is shallow and parallelized. Here, we present recent observations in vertebrate and invertebrate systems that relate the statistical structure and state-dependent modulation of olfactory codes to mechanisms of perception and odour-guided behaviour.
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Affiliation(s)
- Kara A Fulton
- Department of Neuroscience, Harvard Medical School, Boston, MA, USA
| | - David Zimmerman
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Aravi Samuel
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Katrin Vogt
- Department of Physics, Harvard University, Cambridge, MA, USA.
- Department of Biology, University of Konstanz, Konstanz, Germany.
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany.
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12
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Nern A, Loesche F, Takemura SY, Burnett LE, Dreher M, Gruntman E, Hoeller J, Huang GB, Januszewski M, Klapoetke NC, Koskela S, Longden KD, Lu Z, Preibisch S, Qiu W, Rogers EM, Seenivasan P, Zhao A, Bogovic J, Canino BS, Clements J, Cook M, Finley-May S, Flynn MA, Hameed I, Fragniere AM, Hayworth KJ, Hopkins GP, Hubbard PM, Katz WT, Kovalyak J, Lauchie SA, Leonard M, Lohff A, Maldonado CA, Mooney C, Okeoma N, Olbris DJ, Ordish C, Paterson T, Phillips EM, Pietzsch T, Salinas JR, Rivlin PK, Schlegel P, Scott AL, Scuderi LA, Takemura S, Talebi I, Thomson A, Trautman ET, Umayam L, Walsh C, Walsh JJ, Xu CS, Yakal EA, Yang T, Zhao T, Funke J, George R, Hess HF, Jefferis GS, Knecht C, Korff W, Plaza SM, Romani S, Saalfeld S, Scheffer LK, Berg S, Rubin GM, Reiser MB. Connectome-driven neural inventory of a complete visual system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.16.589741. [PMID: 38659887 PMCID: PMC11042306 DOI: 10.1101/2024.04.16.589741] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain's volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly's visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the ~53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.
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Affiliation(s)
- Aljoscha Nern
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Frank Loesche
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Shin-Ya Takemura
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Laura E Burnett
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Marisa Dreher
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | | | - Judith Hoeller
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Gary B Huang
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | | | - Nathan C Klapoetke
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Sanna Koskela
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Kit D Longden
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Zhiyuan Lu
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Stephan Preibisch
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Wei Qiu
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Edward M Rogers
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Pavithraa Seenivasan
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Arthur Zhao
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - John Bogovic
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Brandon S Canino
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Jody Clements
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Michael Cook
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Samantha Finley-May
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Miriam A Flynn
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Imran Hameed
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Alexandra Mc Fragniere
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Kenneth J Hayworth
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Gary Patrick Hopkins
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Philip M Hubbard
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - William T Katz
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Julie Kovalyak
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Shirley A Lauchie
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Meghan Leonard
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Alanna Lohff
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Charli A Maldonado
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Caroline Mooney
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Nneoma Okeoma
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Donald J Olbris
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Christopher Ordish
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Tyler Paterson
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Emily M Phillips
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Tobias Pietzsch
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Jennifer Rivas Salinas
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Patricia K Rivlin
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Philipp Schlegel
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Ashley L Scott
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Louis A Scuderi
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Satoko Takemura
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Iris Talebi
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Alexander Thomson
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Eric T Trautman
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Lowell Umayam
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Claire Walsh
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - John J Walsh
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - C Shan Xu
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Emily A Yakal
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Tansy Yang
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Ting Zhao
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Jan Funke
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Reed George
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Harald F Hess
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Gregory Sxe Jefferis
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Christopher Knecht
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Wyatt Korff
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Stephen M Plaza
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Sandro Romani
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Stephan Saalfeld
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Louis K Scheffer
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Stuart Berg
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Gerald M Rubin
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | - Michael B Reiser
- University of Toronto Scarborough
- Google Research, Google LLC, Switzerland
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
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13
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Dwijesha AS, Eswaran A, Berry JA, Phan A. Diverse memory paradigms in Drosophila reveal diverse neural mechanisms. Learn Mem 2024; 31:a053810. [PMID: 38862165 PMCID: PMC11199951 DOI: 10.1101/lm.053810.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 01/12/2024] [Indexed: 06/13/2024]
Abstract
In this review, we aggregated the different types of learning and memory paradigms developed in adult Drosophila and attempted to assess the similarities and differences in the neural mechanisms supporting diverse types of memory. The simplest association memory assays are conditioning paradigms (olfactory, visual, and gustatory). A great deal of work has been done on these memories, revealing hundreds of genes and neural circuits supporting this memory. Variations of conditioning assays (reversal learning, trace conditioning, latent inhibition, and extinction) also reveal interesting memory mechanisms, whereas mechanisms supporting spatial memory (thermal maze, orientation memory, and heat box) and the conditioned suppression of innate behaviors (phototaxis, negative geotaxis, anemotaxis, and locomotion) remain largely unexplored. In recent years, there has been an increased interest in multisensory and multicomponent memories (context-dependent and cross-modal memory) and higher-order memory (sensory preconditioning and second-order conditioning). Some of this work has revealed how the intricate mushroom body (MB) neural circuitry can support more complex memories. Finally, the most complex memories are arguably those involving social memory: courtship conditioning and social learning (mate-copying and egg-laying behaviors). Currently, very little is known about the mechanisms supporting social memories. Overall, the MBs are important for association memories of multiple sensory modalities and multisensory integration, whereas the central complex is important for place, orientation, and navigation memories. Interestingly, several different types of memory appear to use similar or variants of the olfactory conditioning neural circuitry, which are repurposed in different ways.
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Affiliation(s)
- Amoolya Sai Dwijesha
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Akhila Eswaran
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Jacob A Berry
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Anna Phan
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
- Women and Children's Health Research Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
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14
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Pribbenow C, Owald D. Skewing information flow through pre- and postsynaptic plasticity in the mushroom bodies of Drosophila. Learn Mem 2024; 31:a053919. [PMID: 38876487 PMCID: PMC11199954 DOI: 10.1101/lm.053919.124] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 04/26/2024] [Indexed: 06/16/2024]
Abstract
Animal brains need to store information to construct a representation of their environment. Knowledge of what happened in the past allows both vertebrates and invertebrates to predict future outcomes by recalling previous experience. Although invertebrate and vertebrate brains share common principles at the molecular, cellular, and circuit-architectural levels, there are also obvious differences as exemplified by the use of acetylcholine versus glutamate as the considered main excitatory neurotransmitters in the respective central nervous systems. Nonetheless, across central nervous systems, synaptic plasticity is thought to be a main substrate for memory storage. Therefore, how brain circuits and synaptic contacts change following learning is of fundamental interest for understanding brain computations tied to behavior in any animal. Recent progress has been made in understanding such plastic changes following olfactory associative learning in the mushroom bodies (MBs) of Drosophila A current framework of memory-guided behavioral selection is based on the MB skew model, in which antagonistic synaptic pathways are selectively changed in strength. Here, we review insights into plasticity at dedicated Drosophila MB output pathways and update what is known about the plasticity of both pre- and postsynaptic compartments of Drosophila MB neurons.
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Affiliation(s)
- Carlotta Pribbenow
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - David Owald
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- Einstein Center for Neurosciences Berlin, 10117 Berlin, Germany
- NeuroCure, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
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15
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Stahl A, Tomchik SM. Modeling neurodegenerative and neurodevelopmental disorders in the Drosophila mushroom body. Learn Mem 2024; 31:a053816. [PMID: 38876485 PMCID: PMC11199955 DOI: 10.1101/lm.053816.123] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 05/01/2024] [Indexed: 06/16/2024]
Abstract
The common fruit fly Drosophila melanogaster provides a powerful platform to investigate the genetic, molecular, cellular, and neural circuit mechanisms of behavior. Research in this model system has shed light on multiple aspects of brain physiology and behavior, from fundamental neuronal function to complex behaviors. A major anatomical region that modulates complex behaviors is the mushroom body (MB). The MB integrates multimodal sensory information and is involved in behaviors ranging from sensory processing/responses to learning and memory. Many genes that underlie brain disorders are conserved, from flies to humans, and studies in Drosophila have contributed significantly to our understanding of the mechanisms of brain disorders. Genetic mutations that mimic human diseases-such as Fragile X syndrome, neurofibromatosis type 1, Parkinson's disease, and Alzheimer's disease-affect MB structure and function, altering behavior. Studies dissecting the effects of disease-causing mutations in the MB have identified key pathological mechanisms, and the development of a complete connectome promises to add a comprehensive anatomical framework for disease modeling. Here, we review Drosophila models of human neurodevelopmental and neurodegenerative disorders via the effects of their underlying mutations on MB structure, function, and the resulting behavioral alterations.
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Affiliation(s)
- Aaron Stahl
- Neuroscience and Pharmacology, University of Iowa, Iowa City, Iowa 52242, USA
| | - Seth M Tomchik
- Neuroscience and Pharmacology, University of Iowa, Iowa City, Iowa 52242, USA
- Stead Family Department of Pediatrics, University of Iowa, Iowa City, Iowa 52242, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa 52242, USA
- Hawk-IDDRC, University of Iowa, Iowa City, Iowa 52242, USA
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16
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Suárez-Grimalt R, Grunwald Kadow IC, Scheunemann L. An integrative sensor of body states: how the mushroom body modulates behavior depending on physiological context. Learn Mem 2024; 31:a053918. [PMID: 38876486 PMCID: PMC11199956 DOI: 10.1101/lm.053918.124] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 04/08/2024] [Indexed: 06/16/2024]
Abstract
The brain constantly compares past and present experiences to predict the future, thereby enabling instantaneous and future behavioral adjustments. Integration of external information with the animal's current internal needs and behavioral state represents a key challenge of the nervous system. Recent advancements in dissecting the function of the Drosophila mushroom body (MB) at the single-cell level have uncovered its three-layered logic and parallel systems conveying positive and negative values during associative learning. This review explores a lesser-known role of the MB in detecting and integrating body states such as hunger, thirst, and sleep, ultimately modulating motivation and sensory-driven decisions based on the physiological state of the fly. State-dependent signals predominantly affect the activity of modulatory MB input neurons (dopaminergic, serotoninergic, and octopaminergic), but also induce plastic changes directly at the level of the MB intrinsic and output neurons. Thus, the MB emerges as a tightly regulated relay station in the insect brain, orchestrating neuroadaptations due to current internal and behavioral states leading to short- but also long-lasting changes in behavior. While these adaptations are crucial to ensure fitness and survival, recent findings also underscore how circuit motifs in the MB may reflect fundamental design principles that contribute to maladaptive behaviors such as addiction or depression-like symptoms.
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Affiliation(s)
- Raquel Suárez-Grimalt
- Institute for Biology/Genetics, Freie Universität Berlin, 14195 Berlin, Germany
- Institut für Neurophysiologie and NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
| | | | - Lisa Scheunemann
- Institute for Biology/Genetics, Freie Universität Berlin, 14195 Berlin, Germany
- Institut für Neurophysiologie and NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
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17
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Farnworth MS, Montgomery SH. Evolution of neural circuitry and cognition. Biol Lett 2024; 20:20230576. [PMID: 38747685 PMCID: PMC11285921 DOI: 10.1098/rsbl.2023.0576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 03/08/2024] [Accepted: 03/26/2024] [Indexed: 05/25/2024] Open
Abstract
Neural circuits govern the interface between the external environment, internal cues and outwardly directed behaviours. To process multiple environmental stimuli and integrate these with internal state requires considerable neural computation. Expansion in neural network size, most readily represented by whole brain size, has historically been linked to behavioural complexity, or the predominance of cognitive behaviours. Yet, it is largely unclear which aspects of circuit variation impact variation in performance. A key question in the field of evolutionary neurobiology is therefore how neural circuits evolve to allow improved behavioural performance or innovation. We discuss this question by first exploring how volumetric changes in brain areas reflect actual neural circuit change. We explore three major axes of neural circuit evolution-replication, restructuring and reconditioning of cells and circuits-and discuss how these could relate to broader phenotypes and behavioural variation. This discussion touches on the relevant uses and limitations of volumetrics, while advocating a more circuit-based view of cognition. We then use this framework to showcase an example from the insect brain, the multi-sensory integration and internal processing that is shared between the mushroom bodies and central complex. We end by identifying future trends in this research area, which promise to advance the field of evolutionary neurobiology.
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Affiliation(s)
- Max S. Farnworth
- School of Biological Sciences, University of Bristol, Bristol, UK
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18
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Zhuravlev AV, Vetrovoy OV, Zalomaeva ES, Egozova ES, Nikitina EA, Savvateeva-Popova EV. Overexpression of the limk1 Gene in Drosophila melanogaster Can Lead to Suppression of Courtship Memory in Males. BIOCHEMISTRY. BIOKHIMIIA 2024; 89:393-406. [PMID: 38648760 DOI: 10.1134/s0006297924030015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 04/25/2024]
Abstract
Courtship suppression is a behavioral adaptation of the fruit fly. When majority of the females in a fly population are fertilized and non-receptive for mating, a male, after a series of failed attempts, decreases its courtship activity towards all females, saving its energy and reproductive resources. The time of courtship decrease depends on both duration of unsuccessful courtship and genetically determined features of the male nervous system. Thereby, courtship suppression paradigm can be used for studying molecular mechanisms of learning and memory. p-Cofilin, a component of the actin remodeling signaling cascade and product of LIM-kinase 1 (LIMK1), regulates Drosophila melanogaster forgetting in olfactory learning paradigm. Previously, we have shown that limk1 suppression in the specific types of nervous cells differently affects fly courtship memory. Here, we used Gal4 > UAS system to induce limk1 overexpression in the same types of neurons. limk1 activation in the mushroom body, glia, and fruitless neurons decreased learning index compared to the control strain or the strain with limk1 knockdown. In cholinergic and dopaminergic/serotoninergic neurons, both overexpression and knockdown of limk1 impaired Drosophila short-term memory. Thus, proper balance of the limk1 activity is crucial for normal cognitive activity of the fruit fly.
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Affiliation(s)
- Aleksandr V Zhuravlev
- Pavlov Institute of Physiology, Russian Academy of Sciences, Saint Petersburg, 199034, Russia.
| | - Oleg V Vetrovoy
- Pavlov Institute of Physiology, Russian Academy of Sciences, Saint Petersburg, 199034, Russia.
| | - Ekaterina S Zalomaeva
- Pavlov Institute of Physiology, Russian Academy of Sciences, Saint Petersburg, 199034, Russia.
- Herzen State Pedagogical University of Russia, Saint Petersburg, 191186, Russia
| | - Ekaterina S Egozova
- Herzen State Pedagogical University of Russia, Saint Petersburg, 191186, Russia.
| | - Ekaterina A Nikitina
- Pavlov Institute of Physiology, Russian Academy of Sciences, Saint Petersburg, 199034, Russia.
- Herzen State Pedagogical University of Russia, Saint Petersburg, 191186, Russia
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19
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Kato A, Ohta K, Okanoya K, Kazama H. Dopaminergic neurons dynamically update sensory values during olfactory maneuver. Cell Rep 2023; 42:113122. [PMID: 37757823 DOI: 10.1016/j.celrep.2023.113122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 07/29/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023] Open
Abstract
Dopaminergic neurons (DANs) drive associative learning to update the value of sensory cues, but their contribution to the assessment of sensory values outside the context of association remains largely unexplored. Here, we show in Drosophila that DANs in the mushroom body encode the innate value of odors and constantly update the current value by inducing plasticity during olfactory maneuver. Our connectome-based network model linking all the way from the olfactory neurons to DANs reproduces the characteristics of DAN responses, proposing a concrete circuit mechanism for computation. Downstream of DANs, odors alone induce value- and dopamine-dependent changes in the activity of mushroom body output neurons, which store the current value of odors. Consistent with this neural plasticity, specific sets of DANs bidirectionally modulate flies' steering in a virtual olfactory environment. Thus, the DAN circuit known for discrete, associative learning also continuously updates odor values in a nonassociative manner.
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Affiliation(s)
- Ayaka Kato
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Kazumi Ohta
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; RIKEN CBS-KAO Collaboration Center, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Kazuo Okanoya
- Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Hokto Kazama
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan; RIKEN CBS-KAO Collaboration Center, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
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20
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Arican C, Schmitt FJ, Rössler W, Strube-Bloss MF, Nawrot MP. The mushroom body output encodes behavioral decision during sensory-motor transformation. Curr Biol 2023; 33:4217-4224.e4. [PMID: 37657449 DOI: 10.1016/j.cub.2023.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/30/2023] [Accepted: 08/03/2023] [Indexed: 09/03/2023]
Abstract
Animals form a behavioral decision by evaluating sensory evidence on the background of past experiences and the momentary motivational state. In insects, we still lack understanding of how and at which stage of the recurrent sensory-motor pathway behavioral decisions are formed. The mushroom body (MB), a central brain structure in insects1 and crustaceans,2,3 integrates sensory input of different modalities4,5,6 with the internal state, the behavioral state, and external sensory context7,8,9,10 through a large number of recurrent, mostly neuromodulatory inputs,11,12 implicating a functional role for MBs in state-dependent sensory-motor transformation.13,14 A number of classical conditioning studies in honeybees15,16 and fruit flies17,18,19 have provided accumulated evidence that at its output, the MB encodes the valence of a sensory stimulus with respect to its behavioral relevance. Recent work has extended this notion of valence encoding to the context of innate behaviors.8,20,21,22 Here, we co-analyzed a defined feeding behavior and simultaneous extracellular single-unit recordings from MB output neurons (MBONs) in the cockroach in response to timed sensory stimulation with odors. We show that clear neuronal responses occurred almost exclusively during behaviorally responded trials. Early MBON responses to the sensory stimulus preceded the feeding behavior and predicted its occurrence or non-occurrence from the single-trial population activity. Our results therefore suggest that at its output, the MB does not merely encode sensory stimulus valence. We hypothesize instead that the MB output represents an integrated signal of internal state, momentary environmental conditions, and experience-dependent memory to encode a behavioral decision.
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Affiliation(s)
- Cansu Arican
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Zülpicher Str. 47b, 50674 Cologne, Germany.
| | - Felix Johannes Schmitt
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Zülpicher Str. 47b, 50674 Cologne, Germany
| | - Wolfgang Rössler
- Behavioral Physiology and Sociobiology (Zoology II), Biozentrum, University of Würzburg, Am Hubland, 97074 Würzburg, Germany
| | - Martin Fritz Strube-Bloss
- Department of Biological Cybernetics and Theoretical Biology, University of Bielefeld, Universitätsstr. 25, 33615 Bielefeld, Germany
| | - Martin Paul Nawrot
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Zülpicher Str. 47b, 50674 Cologne, Germany.
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21
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Aso Y, Yamada D, Bushey D, Hibbard KL, Sammons M, Otsuna H, Shuai Y, Hige T. Neural circuit mechanisms for transforming learned olfactory valences into wind-oriented movement. eLife 2023; 12:e85756. [PMID: 37721371 PMCID: PMC10588983 DOI: 10.7554/elife.85756] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 09/07/2023] [Indexed: 09/19/2023] Open
Abstract
How memories are used by the brain to guide future action is poorly understood. In olfactory associative learning in Drosophila, multiple compartments of the mushroom body act in parallel to assign a valence to a stimulus. Here, we show that appetitive memories stored in different compartments induce different levels of upwind locomotion. Using a photoactivation screen of a new collection of split-GAL4 drivers and EM connectomics, we identified a cluster of neurons postsynaptic to the mushroom body output neurons (MBONs) that can trigger robust upwind steering. These UpWind Neurons (UpWiNs) integrate inhibitory and excitatory synaptic inputs from MBONs of appetitive and aversive memory compartments, respectively. After formation of appetitive memory, UpWiNs acquire enhanced response to reward-predicting odors as the response of the inhibitory presynaptic MBON undergoes depression. Blocking UpWiNs impaired appetitive memory and reduced upwind locomotion during retrieval. Photoactivation of UpWiNs also increased the chance of returning to a location where activation was terminated, suggesting an additional role in olfactory navigation. Thus, our results provide insight into how learned abstract valences are gradually transformed into concrete memory-driven actions through divergent and convergent networks, a neuronal architecture that is commonly found in the vertebrate and invertebrate brains.
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Affiliation(s)
- Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Daichi Yamada
- Department of Biology, University of North Carolina at Chapel HillChapel HillUnited States
| | - Daniel Bushey
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Karen L Hibbard
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Megan Sammons
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Hideo Otsuna
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Yichun Shuai
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Toshihide Hige
- Department of Biology, University of North Carolina at Chapel HillChapel HillUnited States
- Department of Cell Biology and Physiology, University of North Carolina at Chapel HillChapel HillUnited States
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel HillChapel HillUnited States
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22
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Nöbel S, Danchin E, Isabel G. Mate copying requires the coincidence detector Rutabaga in the mushroom bodies of Drosophila melanogaster. iScience 2023; 26:107682. [PMID: 37694137 PMCID: PMC10484988 DOI: 10.1016/j.isci.2023.107682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/03/2023] [Accepted: 08/14/2023] [Indexed: 09/12/2023] Open
Abstract
Mate choice constitutes a major fitness-affecting decision often involving social learning leading to copying the preference of other individuals (i.e., mate copying). While mate copying exists in many taxa, its underlying neurobiological mechanisms remain virtually unknown. Here, we show in Drosophila melanogaster that the rutabaga gene is necessary to support mate copying. Rutabaga encodes an adenylyl cyclase (AC-Rut+) acting as a coincidence detector in associative learning. Since the brain localization requirements for AC-Rut+ expression differ in classical and operant learning, we determine the functional localization of AC-Rut+ for mate copying by artificially rescuing the expression of AC-Rut+ in neural subsets of a rutabaga mutant. We found that AC-Rut+ has to be expressed in the mushroom bodies' Kenyon cells (KCs), specifically in the γ-KCs subset. Thus, this form of discriminative social learning requires the same KCs as non-social Pavlovian learning, suggesting that pathways of social and asocial learning overlap significantly.
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Affiliation(s)
- Sabine Nöbel
- Department of Zoology, Animal Ecology, Martin-Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany
- Université Toulouse 1 Capitole and Institute for Advanced Study in Toulouse (IAST), Toulouse, France
- Laboratoire Évolution & Diversité Biologique (EDB UMR 5174), Université de Toulouse Midi-Pyrénées, CNRS, IRD, UPS, 118 route de Narbonne, 31062 Toulouse, France
| | - Etienne Danchin
- Laboratoire Évolution & Diversité Biologique (EDB UMR 5174), Université de Toulouse Midi-Pyrénées, CNRS, IRD, UPS, 118 route de Narbonne, 31062 Toulouse, France
- Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), CNRS UMR 5169, Université de Toulouse Midi-Pyrénées, Toulouse, France
| | - Guillaume Isabel
- Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), CNRS UMR 5169, Université de Toulouse Midi-Pyrénées, Toulouse, France
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23
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Vogt K. Neuroscience: Merging multisensory memories. Curr Biol 2023; 33:R817-R819. [PMID: 37552950 DOI: 10.1016/j.cub.2023.06.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
How animals form and retain memories across multiple sensory modalities and how multisensory learning can enhance memory is largely unknown. A recent study sheds light on the neural mechanism underlying multisensory memory convergence in the Drosophila melanogaster brain.
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Affiliation(s)
- Katrin Vogt
- Department of Biology, University of Konstanz, Konstanz, Germany; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany.
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24
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Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Lin A, Costa M, Eichler K, Yin Y, Silversmith W, Schneider-Mizell C, Jordan CS, Brittain D, Halageri A, Kuehner K, Ogedengbe O, Morey R, Gager J, Kruk K, Perlman E, Yang R, Deutsch D, Bland D, Sorek M, Lu R, Macrina T, Lee K, Bae JA, Mu S, Nehoran B, Mitchell E, Popovych S, Wu J, Jia Z, Castro M, Kemnitz N, Ih D, Bates AS, Eckstein N, Funke J, Collman F, Bock DD, Jefferis GS, Seung HS, Murthy M, FlyWire Consortium. Neuronal wiring diagram of an adult brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.27.546656. [PMID: 37425937 PMCID: PMC10327113 DOI: 10.1101/2023.06.27.546656] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Eyewire, Boston, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Yijie Yin
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Will Silversmith
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Chris S. Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Kai Kuehner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Ryan Morey
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Jay Gager
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - David Deutsch
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Department of Neurobiology, University of Haifa, Haifa, Israel
| | - Doug Bland
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Marissa Sorek
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Eyewire, Boston, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, USA
| | - J. Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Harvard Medical School, Boston, USA
- Centre for Neural Circuits and Behaviour, The University of Oxford, Oxford, UK
| | - Nils Eckstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | | | - Davi D. Bock
- Department of Neurological Sciences, Larner College of Medicine, University of Vermont, Burlington, USA
| | - Gregory S.X.E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - H. Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
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25
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Currier TA, Pang MM, Clandinin TR. Visual processing in the fly, from photoreceptors to behavior. Genetics 2023; 224:iyad064. [PMID: 37128740 PMCID: PMC10213501 DOI: 10.1093/genetics/iyad064] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/22/2023] [Indexed: 05/03/2023] Open
Abstract
Originally a genetic model organism, the experimental use of Drosophila melanogaster has grown to include quantitative behavioral analyses, sophisticated perturbations of neuronal function, and detailed sensory physiology. A highlight of these developments can be seen in the context of vision, where pioneering studies have uncovered fundamental and generalizable principles of sensory processing. Here we begin with an overview of vision-guided behaviors and common methods for probing visual circuits. We then outline the anatomy and physiology of brain regions involved in visual processing, beginning at the sensory periphery and ending with descending motor control. Areas of focus include contrast and motion detection in the optic lobe, circuits for visual feature selectivity, computations in support of spatial navigation, and contextual associative learning. Finally, we look to the future of fly visual neuroscience and discuss promising topics for further study.
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Affiliation(s)
- Timothy A Currier
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michelle M Pang
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA
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26
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Okray Z, Jacob PF, Stern C, Desmond K, Otto N, Talbot CB, Vargas-Gutierrez P, Waddell S. Multisensory learning binds neurons into a cross-modal memory engram. Nature 2023; 617:777-784. [PMID: 37100911 PMCID: PMC10208976 DOI: 10.1038/s41586-023-06013-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 03/24/2023] [Indexed: 04/28/2023]
Abstract
Associating multiple sensory cues with objects and experience is a fundamental brain process that improves object recognition and memory performance. However, neural mechanisms that bind sensory features during learning and augment memory expression are unknown. Here we demonstrate multisensory appetitive and aversive memory in Drosophila. Combining colours and odours improved memory performance, even when each sensory modality was tested alone. Temporal control of neuronal function revealed visually selective mushroom body Kenyon cells (KCs) to be required for enhancement of both visual and olfactory memory after multisensory training. Voltage imaging in head-fixed flies showed that multisensory learning binds activity between streams of modality-specific KCs so that unimodal sensory input generates a multimodal neuronal response. Binding occurs between regions of the olfactory and visual KC axons, which receive valence-relevant dopaminergic reinforcement, and is propagated downstream. Dopamine locally releases GABAergic inhibition to permit specific microcircuits within KC-spanning serotonergic neurons to function as an excitatory bridge between the previously 'modality-selective' KC streams. Cross-modal binding thereby expands the KCs representing the memory engram for each modality into those representing the other. This broadening of the engram improves memory performance after multisensory learning and permits a single sensory feature to retrieve the memory of the multimodal experience.
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Affiliation(s)
- Zeynep Okray
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK.
| | - Pedro F Jacob
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK
| | - Ciara Stern
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK
| | - Kieran Desmond
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK
| | - Nils Otto
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK
- Institute of Anatomy and Molecular Neurobiology, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Clifford B Talbot
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK
| | | | - Scott Waddell
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK.
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27
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Fabian B, Sachse S. Experience-dependent plasticity in the olfactory system of Drosophila melanogaster and other insects. Front Cell Neurosci 2023; 17:1130091. [PMID: 36923450 PMCID: PMC10010147 DOI: 10.3389/fncel.2023.1130091] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/07/2023] [Indexed: 02/24/2023] Open
Abstract
It is long known that the nervous system of vertebrates can be shaped by internal and external factors. On the other hand, the nervous system of insects was long assumed to be stereotypic, although evidence for plasticity effects accumulated for several decades. To cover the topic comprehensively, this review recapitulates the establishment of the term "plasticity" in neuroscience and introduces its original meaning. We describe the basic composition of the insect olfactory system using Drosophila melanogaster as a representative example and outline experience-dependent plasticity effects observed in this part of the brain in a variety of insects, including hymenopterans, lepidopterans, locusts, and flies. In particular, we highlight recent advances in the study of experience-dependent plasticity effects in the olfactory system of D. melanogaster, as it is the most accessible olfactory system of all insect species due to the genetic tools available. The partly contradictory results demonstrate that morphological, physiological and behavioral changes in response to long-term olfactory stimulation are more complex than previously thought. Different molecular mechanisms leading to these changes were unveiled in the past and are likely responsible for this complexity. We discuss common problems in the study of experience-dependent plasticity, ways to overcome them, and future directions in this area of research. In addition, we critically examine the transferability of laboratory data to natural systems to address the topic as holistically as possible. As a mechanism that allows organisms to adapt to new environmental conditions, experience-dependent plasticity contributes to an animal's resilience and is therefore a crucial topic for future research, especially in an era of rapid environmental changes.
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Affiliation(s)
| | - Silke Sachse
- Research Group Olfactory Coding, Max Planck Institute for Chemical Ecology, Jena, Germany
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28
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Rössler W, Grob R, Fleischmann PN. The role of learning-walk related multisensory experience in rewiring visual circuits in the desert ant brain. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2022:10.1007/s00359-022-01600-y. [DOI: 10.1007/s00359-022-01600-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/21/2022] [Accepted: 12/02/2022] [Indexed: 12/13/2022]
Abstract
AbstractEfficient spatial orientation in the natural environment is crucial for the survival of most animal species. Cataglyphis desert ants possess excellent navigational skills. After far-ranging foraging excursions, the ants return to their inconspicuous nest entrance using celestial and panoramic cues. This review focuses on the question about how naïve ants acquire the necessary spatial information and adjust their visual compass systems. Naïve ants perform structured learning walks during their transition from the dark nest interior to foraging under bright sunlight. During initial learning walks, the ants perform rotational movements with nest-directed views using the earth’s magnetic field as an earthbound compass reference. Experimental manipulations demonstrate that specific sky compass cues trigger structural neuronal plasticity in visual circuits to integration centers in the central complex and mushroom bodies. During learning walks, rotation of the sky-polarization pattern is required for an increase in volume and synaptic complexes in both integration centers. In contrast, passive light exposure triggers light-spectrum (especially UV light) dependent changes in synaptic complexes upstream of the central complex. We discuss a multisensory circuit model in the ant brain for pathways mediating structural neuroplasticity at different levels following passive light exposure and multisensory experience during the performance of learning walks.
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Damulewicz M, Tyszka A, Pyza E. Light exposure during development affects physiology of adults in Drosophila melanogaster. Front Physiol 2022; 13:1008154. [PMID: 36505068 PMCID: PMC9732085 DOI: 10.3389/fphys.2022.1008154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 11/14/2022] [Indexed: 11/27/2022] Open
Abstract
Light is one of most important factors synchronizing organisms to day/night cycles in the environment. In Drosophila it is received through compound eyes, Hofbauer-Buchner eyelet, ocelli, using phospholipase C-dependent phototransduction and by deep brain photoreceptors, like Cryptochrome. Even a single light pulse during early life induces larval-time memory, which synchronizes the circadian clock and maintains daily rhythms in adult flies. In this study we investigated several processes in adult flies after maintaining their embryos, larvae and pupae in constant darkness (DD) until eclosion. We found that the lack of external light during development affects sleep time, by reduction of night sleep, and in effect shift to the daytime. However, disruption of internal CRY- dependent photoreception annuls this effect. We also observed changes in the expression of genes encoding neurotransmitters and their receptors between flies kept in different light regime. In addition, the lack of light during development results in decreasing size of mushroom bodies, involved in sleep regulation. Taking together, our results show that presence of light during early life plays a key role in brain development and affects adult behavior.
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Ikeda K, Kataoka M, Tanaka NK. Nonsynaptic Transmission Mediates Light Context-Dependent Odor Responses in Drosophila melanogaster. J Neurosci 2022; 42:8621-8628. [PMID: 36180227 PMCID: PMC9671575 DOI: 10.1523/jneurosci.1106-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 11/21/2022] Open
Abstract
Recent connectome analyses of the entire synaptic circuit in the nervous system have provided tremendous insights into how neural processing occurs through the synaptic relay of neural information. Conversely, the extent to which ephaptic transmission which does not depend on the synapses contributes to the relay of neural information, especially beyond a distance between adjacent neurons and to neural processing remains unclear. We show that ephaptic transmission mediated by extracellular potential changes in female Drosophila melanogaster can reach >200 µm, equivalent to the depth of its brain. Furthermore, ephaptic transmission driven by retinal photoreceptor cells mediates light-evoked firing rate increases in olfactory sensory neurons. These results indicate that ephaptic transmission contributes to sensory responses that can change momentarily in a context-dependent manner.SIGNIFICANCE STATEMENT Although extracellular field potential activities are commonly observed in many nervous systems, this activity has been generally considered as a side effect of synchronized spiking of neurons. This study, however, shows that field potential changes in retinae evoked by a sensory stimulus can control the excitability of distant neurons in vivo and mediates multimodal sensory integration in Drosophila melanogaster As such ephaptic transmission is more effective at a short distance, the ephaptic transmission from the retinae may contribute significantly to firing rate changes in downstream neurons of the photoreceptor cells in the optic lobe.
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Affiliation(s)
- Kazuaki Ikeda
- Division of Biology, Department of Biological Sciences, School of Science, Hokkaido University, Sapporo, 060-0810, Japan
- Graduate School of Life Sciences, Hokkaido University, Sapporo, 060-0810, Japan
| | - Masaki Kataoka
- Division of Biology, Department of Biological Sciences, School of Science, Hokkaido University, Sapporo, 060-0810, Japan
- Graduate School of Life Sciences, Hokkaido University, Sapporo, 060-0810, Japan
| | - Nobuaki K Tanaka
- Division of Biology, Department of Biological Sciences, School of Science, Hokkaido University, Sapporo, 060-0810, Japan
- Graduate School of Life Sciences, Hokkaido University, Sapporo, 060-0810, Japan
- Faculty of Science, Hokkaido University, Sapporo, 060-0810, Japan
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31
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Yilmaz A, Hempel de Ibarra N, Kelber A. High diversity of arthropod colour vision: from genes to ecology. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210273. [PMID: 36058249 PMCID: PMC9441235 DOI: 10.1098/rstb.2021.0273] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 05/17/2022] [Indexed: 11/16/2022] Open
Abstract
Colour vision allows animals to use the information contained in the spectrum of light to control important behavioural decisions such as selection of habitats, food or mates. Among arthropods, the largest animal phylum, we find completely colour-blind species as well as species with up to 40 different opsin genes or more than 10 spectral types of photoreceptors, we find a large diversity of optical methods shaping spectral sensitivity, we find eyes with different colour vision systems looking into the dorsal and ventral hemisphere, and species in which males and females see the world in different colours. The behavioural use of colour vision shows an equally astonishing diversity. Only the neural mechanisms underlying this sensory ability seems surprisingly conserved-not only within the phylum, but even between arthropods and the other well-studied phylum, chordates. The papers in this special issue allow a glimpse into the colourful world of arthropod colour vision, and besides giving an overview this introduction highlights how much more research is needed to fill in the many missing pieces of this large puzzle. This article is part of the theme issue 'Understanding colour vision: molecular, physiological, neuronal and behavioural studies in arthropods'.
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Affiliation(s)
- Ayse Yilmaz
- Department of Biology - Functional Zoology, Lund University, Lund 22362, Sweden
| | | | - Almut Kelber
- Department of Biology - Functional Zoology, Lund University, Lund 22362, Sweden
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Abstract
Ants are ecologically one of the most important groups of insects and exhibit impressive capabilities for visual learning and orientation. Studies on numerous ant species demonstrate that ants can learn to discriminate between different colours irrespective of light intensity and modify their behaviour accordingly. However, the findings across species are variable and inconsistent, suggesting that our understanding of colour vision in ants and what roles ecological and phylogenetic factors play is at an early stage. This review provides a brief synopsis of the critical findings of the past century of research by compiling studies that address molecular, physiological and behavioural aspects of ant colour vision. With this, we aim to improve our understanding of colour vision and to gain deeper insights into the mysterious and colourful world of ants. This article is part of the theme issue ‘Understanding colour vision: molecular, physiological, neuronal and behavioural studies in arthropods’.
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Affiliation(s)
- Ayse Yilmaz
- Department of Biology, Lund Vision Group, University of Lund, 223 62 Lund, Sweden
| | - Johannes Spaethe
- Behavioral Physiology and Sociobiology (Zoology II), Biocenter, University of Würzburg, Sanderring 2, 97070 Würzburg, Germany
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33
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Zhuravlev AV, Ivanova PN, Makaveeva KA, Zakharov GA, Nikitina EA, Savvateeva-Popova EV. cd1 Mutation in Drosophila Affects Phenoxazinone Synthase Catalytic Site and Impairs Long-Term Memory. Int J Mol Sci 2022; 23:ijms232012356. [PMID: 36293213 PMCID: PMC9604555 DOI: 10.3390/ijms232012356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2022] Open
Abstract
Being involved in development of Huntington's, Parkinson's and Alzheimer's diseases, kynurenine pathway (KP) of tryptophan metabolism plays a significant role in modulation of neuropathology. Accumulation of a prooxidant 3-hydroxykynurenine (3-HOK) leads to oxidative stress and neuronal cell apoptosis. Drosophila mutant cardinal (cd1) with 3-HOK excess shows age-dependent neurodegeneration and short-term memory impairments, thereby presenting a model for senile dementia. Although cd gene for phenoxazinone synthase (PHS) catalyzing 3-HOK dimerization has been presumed to harbor the cd1 mutation, its molecular nature remained obscure. Using next generation sequencing, we have shown that the cd gene in cd1 carries a long deletion leading to PHS active site destruction. Contrary to the wild type Canton-S (CS), cd1 males showed defective long-term memory (LTM) in conditioned courtship suppression paradigm (CCSP) at days 5-29 after eclosion. The number of dopaminergic neurons (DAN) regulating fly locomotor activity showed an age-dependent tendency to decrease in cd1 relative to CS. Thus, in accordance with the concept "from the gene to behavior" proclaimed by S. Benzer, we have shown that the aberrant PHS sequence in cd1 provokes drastic LTM impairments and DAN alterations.
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Affiliation(s)
- Aleksandr V. Zhuravlev
- Pavlov Institute of Physiology, Russian Academy of Sciences, 199034 Saint Petersburg, Russia
- Correspondence:
| | - Polina N. Ivanova
- Pavlov Institute of Physiology, Russian Academy of Sciences, 199034 Saint Petersburg, Russia
| | - Ksenia A. Makaveeva
- Faculty of Biology, Herzen State Pedagogical University of Russia, 191186 Saint Petersburg, Russia
| | | | - Ekaterina A. Nikitina
- Pavlov Institute of Physiology, Russian Academy of Sciences, 199034 Saint Petersburg, Russia
- Faculty of Biology, Herzen State Pedagogical University of Russia, 191186 Saint Petersburg, Russia
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Poppinga H, Çoban B, Meltzer H, Mayseless O, Widmann A, Schuldiner O, Fiala A. Pruning deficits of the developing Drosophila mushroom body result in mild impairment in associative odour learning and cause hyperactivity. Open Biol 2022; 12:220096. [PMID: 36128716 PMCID: PMC9490343 DOI: 10.1098/rsob.220096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The principles of how brain circuits establish themselves during development are largely conserved across animal species. Connections made during embryonic development that are appropriate for an early life stage are frequently remodelled later in ontogeny via pruning and subsequent regrowth to generate adult-specific connectivity. The mushroom body of the fruit fly Drosophila melanogaster is a well-established model circuit for examining the cellular mechanisms underlying neurite remodelling. This central brain circuit integrates sensory information with learned and innate valences to adaptively instruct behavioural decisions. Thereby, the mushroom body organizes adaptive behaviour, such as associative learning. However, little is known about the specific aspects of behaviour that require mushroom body remodelling. Here, we used genetic interventions to prevent the intrinsic neurons of the larval mushroom body (γ-type Kenyon cells) from remodelling. We asked to what degree remodelling deficits resulted in impaired behaviour. We found that deficits caused hyperactivity and mild impairment in differential aversive olfactory learning, but not appetitive learning. Maintenance of circadian rhythm and sleep were not affected. We conclude that neurite pruning and regrowth of γ-type Kenyon cells is not required for the establishment of circuits that mediate associative odour learning per se, but it does improve distinct learning tasks.
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Affiliation(s)
- Haiko Poppinga
- Department of Molecular Neurobiology of Behaviour, University of Göttingen, Julia-Lermontowa-Weg 3, 37077 Göttingen, Germany
| | - Büşra Çoban
- Department of Molecular Neurobiology of Behaviour, University of Göttingen, Julia-Lermontowa-Weg 3, 37077 Göttingen, Germany
| | - Hagar Meltzer
- Departments for Molecular Cell Biology and Molecular Neuroscience, Weizmann Institute of Science, Ullmann Building of Life Sciences, Rehovot 7610001, Israel
| | - Oded Mayseless
- Departments for Molecular Cell Biology and Molecular Neuroscience, Weizmann Institute of Science, Ullmann Building of Life Sciences, Rehovot 7610001, Israel
| | - Annekathrin Widmann
- Department of Molecular Neurobiology of Behaviour, University of Göttingen, Julia-Lermontowa-Weg 3, 37077 Göttingen, Germany
| | - Oren Schuldiner
- Departments for Molecular Cell Biology and Molecular Neuroscience, Weizmann Institute of Science, Ullmann Building of Life Sciences, Rehovot 7610001, Israel
| | - André Fiala
- Department of Molecular Neurobiology of Behaviour, University of Göttingen, Julia-Lermontowa-Weg 3, 37077 Göttingen, Germany
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Nöbel S, Monier M, Fargeot L, Lespagnol G, Danchin E, Isabel G. Female fruit flies copy the acceptance, but not the rejection, of a mate. Behav Ecol 2022. [DOI: 10.1093/beheco/arac071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Acceptance and avoidance can be socially transmitted, especially in the case of mate choice. When a Drosophila melanogaster female observes a conspecific female (called demonstrator female) choosing to mate with one of two males, the former female (called observer female) can memorize and copy the latter female’s choice. Traditionally in mate-copying experiments, demonstrations provide two types of information to observer females, namely, the acceptance (positive) of one male and the rejection of the other male (negative). To disentangle the respective roles of positive and negative information in Drosophila mate copying, we performed experiments in which demonstrations provided only one type of information at a time. We found that positive information alone is sufficient to trigger mate copying. Observer females preferred males of phenotype A after watching a female mating with a male of phenotype A in the absence of any other male. Contrastingly, negative information alone (provided by a demonstrator female actively rejecting a male of phenotype B) did not affect future observer females’ mate choice. These results suggest that the informative part of demonstrations in Drosophila mate-copying experiments lies mainly, if not exclusively, in the positive information provided by the copulation with a given male. We discuss the reasons for such a result and suggest that Drosophila females learn to prefer the successful males, implying that the underlying learning mechanisms may be shared with those of appetitive memory in non-social associative learning.
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Affiliation(s)
- Sabine Nöbel
- Université Toulouse 1 Capitole and Institute for Advanced Study in Toulouse (IAST) , Toulouse , France
- Laboratoire Évolution & Diversité Biologique (EDB), UMR5174, CNRS, IRD, Université Toulouse III Paul Sabatier , 118 route de Narbonne, F-31062 Toulouse Cedex 9 , France
| | - Magdalena Monier
- Laboratoire Évolution & Diversité Biologique (EDB), UMR5174, CNRS, IRD, Université Toulouse III Paul Sabatier , 118 route de Narbonne, F-31062 Toulouse Cedex 9 , France
| | - Laura Fargeot
- Centre de Recherches sur la Cognition Animale (CRCA) , Centre de Biologie Intégrative (CBI), CNRS UMR 5169, Toulouse , France
| | - Guillaume Lespagnol
- Laboratoire Évolution & Diversité Biologique (EDB), UMR5174, CNRS, IRD, Université Toulouse III Paul Sabatier , 118 route de Narbonne, F-31062 Toulouse Cedex 9 , France
| | - Etienne Danchin
- Laboratoire Évolution & Diversité Biologique (EDB), UMR5174, CNRS, IRD, Université Toulouse III Paul Sabatier , 118 route de Narbonne, F-31062 Toulouse Cedex 9 , France
| | - Guillaume Isabel
- Centre de Recherches sur la Cognition Animale (CRCA) , Centre de Biologie Intégrative (CBI), CNRS UMR 5169, Toulouse , France
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Ryu L, Kim SY, Kim AJ. From Photons to Behaviors: Neural Implementations of Visual Behaviors in Drosophila. Front Neurosci 2022; 16:883640. [PMID: 35600623 PMCID: PMC9115102 DOI: 10.3389/fnins.2022.883640] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/28/2022] [Indexed: 11/17/2022] Open
Abstract
Neural implementations of visual behaviors in Drosophila have been dissected intensively in the past couple of decades. The availability of premiere genetic toolkits, behavioral assays in tethered or freely moving conditions, and advances in connectomics have permitted the understanding of the physiological and anatomical details of the nervous system underlying complex visual behaviors. In this review, we describe recent advances on how various features of a visual scene are detected by the Drosophila visual system and how the neural circuits process these signals and elicit an appropriate behavioral response. Special emphasis was laid on the neural circuits that detect visual features such as brightness, color, local motion, optic flow, and translating or approaching visual objects, which would be important for behaviors such as phototaxis, optomotor response, attraction (or aversion) to moving objects, navigation, and visual learning. This review offers an integrative framework for how the fly brain detects visual features and orchestrates an appropriate behavioral response.
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Affiliation(s)
- Leesun Ryu
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Sung Yong Kim
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Anmo J. Kim
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
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37
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Recurrent circadian circuitry regulates central brain activity to maintain sleep. Neuron 2022; 110:2139-2154.e5. [PMID: 35525241 DOI: 10.1016/j.neuron.2022.04.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/14/2022] [Accepted: 04/07/2022] [Indexed: 12/19/2022]
Abstract
Animal brains have discrete circadian neurons, but little is known about how they are coordinated to influence and maintain sleep. Here, through a systematic optogenetic screening, we identified a subtype of uncharacterized circadian DN3 neurons that is strongly sleep promoting in Drosophila. These anterior-projecting DN3s (APDN3s) receive signals from DN1 circadian neurons and then output to newly identified noncircadian "claw" neurons (CLs). CLs have a daily Ca2+ cycle, which peaks at night and correlates with DN1 and DN3 Ca2+ cycles. The CLs feedback onto a subset of DN1s to form a positive recurrent loop that maintains sleep. Using trans-synaptic photoactivatable green fluorescent protein (PA-GFP) tracing and functional in vivo imaging, we demonstrated that the CLs drive sleep by interacting with and releasing acetylcholine onto the mushroom body γ lobe. Taken together, the data identify a novel self-reinforcing loop within the circadian network and a new sleep-promoting neuropile that are both essential for maintaining normal sleep.
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38
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The best of both worlds: Dual systems of reasoning in animals and AI. Cognition 2022; 225:105118. [PMID: 35453083 DOI: 10.1016/j.cognition.2022.105118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/29/2022] [Accepted: 04/01/2022] [Indexed: 11/20/2022]
Abstract
Much of human cognition involves two different types of reasoning that operate together. Type 1 reasoning systems are intuitive and fast, whereas Type 2 reasoning systems are reflective and slow. Why has our cognition evolved with these features? Both systems are coherent and in most ecological circumstances either alone is capable of coming up with the right answer most of the time. Neural tissue is costly, and thus far evolutionary models have struggled to identify a benefit of operating two systems of reasoning. To explore this issue we take a broad comparative perspective. We discuss how dual processes of cognition have enabled the emergence of selective attention in insects, transforming the learning capacities of these animals. Modern AIs using dual systems of learning are able to learn how their vast world works and how best to interact with it, allowing them to exceed human levels of performance in strategy games. We propose that the core benefits of dual processes of reasoning are to narrow down a problem space in order to focus cognitive resources most effectively.
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Multimodal Information Processing and Associative Learning in the Insect Brain. INSECTS 2022; 13:insects13040332. [PMID: 35447774 PMCID: PMC9033018 DOI: 10.3390/insects13040332] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/23/2022] [Accepted: 03/25/2022] [Indexed: 02/04/2023]
Abstract
Simple Summary Insect behaviors are a great indicator of evolution and provide useful information about the complexity of organisms. The realistic sensory scene of an environment is complex and replete with multisensory inputs, making the study of sensory integration that leads to behavior highly relevant. We summarize the recent findings on multimodal sensory integration and the behaviors that originate from them in our review. Abstract The study of sensory systems in insects has a long-spanning history of almost an entire century. Olfaction, vision, and gustation are thoroughly researched in several robust insect models and new discoveries are made every day on the more elusive thermo- and mechano-sensory systems. Few specialized senses such as hygro- and magneto-reception are also identified in some insects. In light of recent advancements in the scientific investigation of insect behavior, it is not only important to study sensory modalities individually, but also as a combination of multimodal inputs. This is of particular significance, as a combinatorial approach to study sensory behaviors mimics the real-time environment of an insect with a wide spectrum of information available to it. As a fascinating field that is recently gaining new insight, multimodal integration in insects serves as a fundamental basis to understand complex insect behaviors including, but not limited to navigation, foraging, learning, and memory. In this review, we have summarized various studies that investigated sensory integration across modalities, with emphasis on three insect models (honeybees, ants and flies), their behaviors, and the corresponding neuronal underpinnings.
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40
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Devineni AV, Scaplen KM. Neural Circuits Underlying Behavioral Flexibility: Insights From Drosophila. Front Behav Neurosci 2022; 15:821680. [PMID: 35069145 PMCID: PMC8770416 DOI: 10.3389/fnbeh.2021.821680] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 12/14/2021] [Indexed: 11/16/2022] Open
Abstract
Behavioral flexibility is critical to survival. Animals must adapt their behavioral responses based on changes in the environmental context, internal state, or experience. Studies in Drosophila melanogaster have provided insight into the neural circuit mechanisms underlying behavioral flexibility. Here we discuss how Drosophila behavior is modulated by internal and behavioral state, environmental context, and learning. We describe general principles of neural circuit organization and modulation that underlie behavioral flexibility, principles that are likely to extend to other species.
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Affiliation(s)
- Anita V. Devineni
- Department of Biology, Emory University, Atlanta, GA, United States
- Zuckerman Mind Brain Institute, Columbia University, New York, NY, United States
| | - Kristin M. Scaplen
- Department of Psychology, Bryant University, Smithfield, RI, United States
- Center for Health and Behavioral Studies, Bryant University, Smithfield, RI, United States
- Department of Neuroscience, Brown University, Providence, RI, United States
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41
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Li Q, Jang H, Lim KY, Lessing A, Stavropoulos N. insomniac links the development and function of a sleep-regulatory circuit. eLife 2021; 10:65437. [PMID: 34908527 PMCID: PMC8758140 DOI: 10.7554/elife.65437] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 10/15/2021] [Indexed: 11/17/2022] Open
Abstract
Although many genes are known to influence sleep, when and how they impact sleep-regulatory circuits remain ill-defined. Here, we show that insomniac (inc), a conserved adaptor for the autism-associated Cul3 ubiquitin ligase, acts in a restricted period of neuronal development to impact sleep in adult Drosophila. The loss of inc causes structural and functional alterations within the mushroom body (MB), a center for sensory integration, associative learning, and sleep regulation. In inc mutants, MB neurons are produced in excess, develop anatomical defects that impede circuit assembly, and are unable to promote sleep when activated in adulthood. Our findings link neurogenesis and postmitotic development of sleep-regulatory neurons to their adult function and suggest that developmental perturbations of circuits that couple sensory inputs and sleep may underlie sleep dysfunction in neurodevelopmental disorders.
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Affiliation(s)
- Qiuling Li
- Neuroscience Institute, Department of Neuroscience and Physiology, New York University School of MedicineNew YorkUnited States
| | - Hyunsoo Jang
- Neuroscience Institute, Department of Neuroscience and Physiology, New York University School of MedicineNew YorkUnited States
| | - Kayla Y Lim
- Neuroscience Institute, Department of Neuroscience and Physiology, New York University School of MedicineNew YorkUnited States
| | - Alexie Lessing
- Neuroscience Institute, Department of Neuroscience and Physiology, New York University School of MedicineNew YorkUnited States
| | - Nicholas Stavropoulos
- Neuroscience Institute, Department of Neuroscience and Physiology, New York University School of MedicineNew YorkUnited States
- Waksman Institute, Rutgers UniversityPiscatawayUnited States
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42
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Finke V, Baracchi D, Giurfa M, Scheiner R, Avarguès-Weber A. Evidence of cognitive specialization in an insect: proficiency is maintained across elemental and higher-order visual learning but not between sensory modalities in honey bees. J Exp Biol 2021; 224:273769. [PMID: 34664669 DOI: 10.1242/jeb.242470] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 10/14/2021] [Indexed: 11/20/2022]
Abstract
Individuals differing in their cognitive abilities and foraging strategies may confer a valuable benefit to their social groups as variability may help responding flexibly in scenarios with different resource availability. Individual learning proficiency may either be absolute or vary with the complexity or the nature of the problem considered. Determining if learning abilities correlate between tasks of different complexity or between sensory modalities has a high interest for research on brain modularity and task-dependent specialisation of neural circuits. The honeybee Apis mellifera constitutes an attractive model to address this question due to its capacity to successfully learn a large range of tasks in various sensory domains. Here we studied whether the performance of individual bees in a simple visual discrimination task (a discrimination between two visual shapes) is stable over time and correlates with their capacity to solve either a higher-order visual task (a conceptual discrimination based on spatial relations between objects) or an elemental olfactory task (a discrimination between two odorants). We found that individual learning proficiency within a given task was maintained over time and that some individuals performed consistently better than others within the visual modality, thus showing consistent aptitude across visual tasks of different complexity. By contrast, performance in the elemental visual-learning task did not predict performance in the equivalent elemental olfactory task. Overall, our results suggest the existence of cognitive specialisation within the hive, which may contribute to ecological social success.
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Affiliation(s)
- Valerie Finke
- Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse; CNRS, UPS, 118 Route de Narbonne, 31062 Toulouse, France.,Biozentrum, Universität Würzburg, Am Hubland, 97074 Würzburg, Germany
| | - David Baracchi
- Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse; CNRS, UPS, 118 Route de Narbonne, 31062 Toulouse, France.,Department of Biology, University of Florence, Via Madonna del Piano 6, 50019 Sesto Fiorentino, Italy
| | - Martin Giurfa
- Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse; CNRS, UPS, 118 Route de Narbonne, 31062 Toulouse, France.,Institut Universitaire de France, Paris, France
| | - Ricarda Scheiner
- Biozentrum, Universität Würzburg, Am Hubland, 97074 Würzburg, Germany
| | - Aurore Avarguès-Weber
- Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse; CNRS, UPS, 118 Route de Narbonne, 31062 Toulouse, France
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43
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Machado Almeida P, Lago Solis B, Stickley L, Feidler A, Nagoshi E. Neurofibromin 1 in mushroom body neurons mediates circadian wake drive through activating cAMP-PKA signaling. Nat Commun 2021; 12:5758. [PMID: 34599173 PMCID: PMC8486785 DOI: 10.1038/s41467-021-26031-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 09/15/2021] [Indexed: 02/08/2023] Open
Abstract
Various behavioral and cognitive states exhibit circadian variations in animals across phyla including Drosophila melanogaster, in which only ~0.1% of the brain's neurons contain circadian clocks. Clock neurons transmit the timing information to a plethora of non-clock neurons via poorly understood mechanisms. Here, we address the molecular underpinning of this phenomenon by profiling circadian gene expression in non-clock neurons that constitute the mushroom body, the center of associative learning and sleep regulation. We show that circadian clocks drive rhythmic expression of hundreds of genes in mushroom body neurons, including the Neurofibromin 1 (Nf1) tumor suppressor gene and Pka-C1. Circadian clocks also drive calcium rhythms in mushroom body neurons via NF1-cAMP/PKA-C1 signaling, eliciting higher mushroom body activity during the day than at night, thereby promoting daytime wakefulness. These findings reveal the pervasive, non-cell-autonomous circadian regulation of gene expression in the brain and its role in sleep.
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Affiliation(s)
- Pedro Machado Almeida
- grid.8591.50000 0001 2322 4988Department of Genetics and Evolution, Sciences III, University of Geneva, 30 Quai Ernest-Ansermet, Geneva, 4, CH-1211 Switzerland
| | - Blanca Lago Solis
- grid.8591.50000 0001 2322 4988Department of Genetics and Evolution, Sciences III, University of Geneva, 30 Quai Ernest-Ansermet, Geneva, 4, CH-1211 Switzerland
| | - Luca Stickley
- grid.8591.50000 0001 2322 4988Department of Genetics and Evolution, Sciences III, University of Geneva, 30 Quai Ernest-Ansermet, Geneva, 4, CH-1211 Switzerland
| | - Alexis Feidler
- grid.8591.50000 0001 2322 4988Department of Genetics and Evolution, Sciences III, University of Geneva, 30 Quai Ernest-Ansermet, Geneva, 4, CH-1211 Switzerland ,grid.412750.50000 0004 1936 9166Present Address: University of Rochester School of Medicine and Dentistry, Rochester, NY USA
| | - Emi Nagoshi
- grid.8591.50000 0001 2322 4988Department of Genetics and Evolution, Sciences III, University of Geneva, 30 Quai Ernest-Ansermet, Geneva, 4, CH-1211 Switzerland
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44
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Abstract
Every aspect of vision, from the opsin proteins to the eyes and the ways that they serve animal behavior, is incredibly diverse. It is only with an evolutionary perspective that this diversity can be understood and fully appreciated. In this review, I describe and explain the diversity at each level and try to convey an understanding of how the origin of the first opsin some 800 million years ago could initiate the avalanche that produced the astonishing diversity of eyes and vision that we see today. Despite the diversity, many types of photoreceptors, eyes, and visual roles have evolved multiple times independently in different animals, revealing a pattern of eye evolution strictly guided by functional constraints and driven by the evolution of gradually more demanding behaviors. I conclude the review by introducing a novel distinction between active and passive vision that points to uncharted territories in vision research. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Dan-E Nilsson
- Lund Vision Group, Department of Biology, Lund University, 22362 Lund, Sweden;
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45
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Ikarashi M, Tanimoto H. Drosophila acquires seconds-scale rhythmic behavior. J Exp Biol 2021; 224:238112. [PMID: 33795422 DOI: 10.1242/jeb.242443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 03/22/2021] [Indexed: 11/20/2022]
Abstract
Detection of the temporal structure of stimuli is crucial for prediction. While perception of interval timing is relevant for immediate behavioral adaptations, it has scarcely been investigated, especially in invertebrates. Here, we examined whether the fruit fly, Drosophila melanogaster, can acquire rhythmic behavior in the range of seconds. To this end, we developed a novel temporal conditioning paradigm utilizing repeated electric shocks. Combined automatic behavioral annotation and time-frequency analysis revealed that behavioral rhythms continued after cessation of the shocks. Furthermore, we found that aging impaired interval timing. This study thus not only demonstrates the ability of insects to acquire behavioral rhythms of a few seconds, but highlights a life-course decline of temporal coordination, which is also common in mammals.
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Affiliation(s)
- Masayoshi Ikarashi
- Graduate School of Life Sciences, Tohoku University, 2-1-1 Katahira, Aoba-Ku, Sendai, 980-8577, Japan
| | - Hiromu Tanimoto
- Graduate School of Life Sciences, Tohoku University, 2-1-1 Katahira, Aoba-Ku, Sendai, 980-8577, Japan
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46
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Scaplen KM, Talay M, Fisher JD, Cohn R, Sorkaç A, Aso Y, Barnea G, Kaun KR. Transsynaptic mapping of Drosophila mushroom body output neurons. eLife 2021; 10:e63379. [PMID: 33570489 PMCID: PMC7877909 DOI: 10.7554/elife.63379] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 01/26/2021] [Indexed: 11/13/2022] Open
Abstract
The mushroom body (MB) is a well-characterized associative memory structure within the Drosophila brain. Analyzing MB connectivity using multiple approaches is critical for understanding the functional implications of this structure. Using the genetic anterograde transsynaptic tracing tool, trans-Tango, we identified divergent projections across the brain and convergent downstream targets of the MB output neurons (MBONs). Our analysis revealed at least three separate targets that receive convergent input from MBONs: other MBONs, the fan-shaped body (FSB), and the lateral accessory lobe (LAL). We describe, both anatomically and functionally, a multilayer circuit in which inhibitory and excitatory MBONs converge on the same genetic subset of FSB and LAL neurons. This circuit architecture enables the brain to update and integrate information with previous experience before executing appropriate behavioral responses. Our use of trans-Tango provides a genetically accessible anatomical framework for investigating the functional relevance of components within these complex and interconnected circuits.
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Affiliation(s)
- Kristin M Scaplen
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Department of Psychology, Bryant UniversitySmithfieldUnited States
- Center for Health and Behavioral Sciences, Bryant UniversitySmithfieldUnited States
| | - Mustafa Talay
- Department of Neuroscience, Brown UniversityProvidenceUnited States
| | - John D Fisher
- Department of Neuroscience, Brown UniversityProvidenceUnited States
| | - Raphael Cohn
- Laboratory of Neurophysiology and Behavior, The Rockefeller UniversityNew YorkUnited States
| | - Altar Sorkaç
- Department of Neuroscience, Brown UniversityProvidenceUnited States
| | - Yoshi Aso
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gilad Barnea
- Department of Neuroscience, Brown UniversityProvidenceUnited States
| | - Karla R Kaun
- Department of Neuroscience, Brown UniversityProvidenceUnited States
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47
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Strausfeld N, Sayre ME. Shore crabs reveal novel evolutionary attributes of the mushroom body. eLife 2021; 10:65167. [PMID: 33559601 PMCID: PMC7872517 DOI: 10.7554/elife.65167] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 01/14/2021] [Indexed: 11/13/2022] Open
Abstract
Neural organization of mushroom bodies is largely consistent across insects, whereas the ancestral ground pattern diverges broadly across crustacean lineages resulting in successive loss of columns and the acquisition of domed centers retaining ancestral Hebbian-like networks and aminergic connections. We demonstrate here a major departure from this evolutionary trend in Brachyura, the most recent malacostracan lineage. In the shore crab Hemigrapsus nudus, instead of occupying the rostral surface of the lateral protocerebrum, mushroom body calyces are buried deep within it with their columns extending outwards to an expansive system of gyri on the brain’s surface. The organization amongst mushroom body neurons reaches extreme elaboration throughout its constituent neuropils. The calyces, columns, and especially the gyri show DC0 immunoreactivity, an indicator of extensive circuits involved in learning and memory.
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Affiliation(s)
| | - Marcel E Sayre
- Lund Vision Group, Department of Biology, Lund University, Lund, Sweden.,Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia
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48
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Laursen WJ, Garrity PA. A divalent boost from magnesium. eLife 2021; 10:65359. [PMID: 33504427 PMCID: PMC7843128 DOI: 10.7554/elife.65359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 01/20/2021] [Indexed: 11/13/2022] Open
Abstract
Enhanced levels of dietary magnesium improve long-term memory in fruit flies.
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Affiliation(s)
- Willem J Laursen
- Department of Biology and Volen Center for Complex Systems, Brandeis University, Waltham, United States
| | - Paul A Garrity
- Department of Biology and Volen Center for Complex Systems, Brandeis University, Waltham, United States
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49
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van der Kooi CJ, Stavenga DG, Arikawa K, Belušič G, Kelber A. Evolution of Insect Color Vision: From Spectral Sensitivity to Visual Ecology. ANNUAL REVIEW OF ENTOMOLOGY 2021; 66:435-461. [PMID: 32966103 DOI: 10.1146/annurev-ento-061720-071644] [Citation(s) in RCA: 120] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Color vision is widespread among insects but varies among species, depending on the spectral sensitivities and interplay of the participating photoreceptors. The spectral sensitivity of a photoreceptor is principally determined by the absorption spectrum of the expressed visual pigment, but it can be modified by various optical and electrophysiological factors. For example, screening and filtering pigments, rhabdom waveguide properties, retinal structure, and neural processing all influence the perceived color signal. We review the diversity in compound eye structure, visual pigments, photoreceptor physiology, and visual ecology of insects. Based on an overview of the current information about the spectral sensitivities of insect photoreceptors, covering 221 species in 13 insect orders, we discuss the evolution of color vision and highlight present knowledge gaps and promising future research directions in the field.
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Affiliation(s)
- Casper J van der Kooi
- Faculty of Science and Engineering, University of Groningen, 9700 AK Groningen, The Netherlands;
| | - Doekele G Stavenga
- Faculty of Science and Engineering, University of Groningen, 9700 AK Groningen, The Netherlands;
| | - Kentaro Arikawa
- Department of Evolutionary Studies of Biosystems, SOKENDAI Graduate University for Advanced Studies, Kanagawa 240-0193, Japan;
| | - Gregor Belušič
- Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Almut Kelber
- Lund Vision Group, Department of Biology, University of Lund, 22362 Lund, Sweden;
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50
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Siju KP, De Backer JF, Grunwald Kadow IC. Dopamine modulation of sensory processing and adaptive behavior in flies. Cell Tissue Res 2021; 383:207-225. [PMID: 33515291 PMCID: PMC7873103 DOI: 10.1007/s00441-020-03371-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 11/26/2020] [Indexed: 12/31/2022]
Abstract
Behavioral flexibility for appropriate action selection is an advantage when animals are faced with decisions that will determine their survival or death. In order to arrive at the right decision, animals evaluate information from their external environment, internal state, and past experiences. How these different signals are integrated and modulated in the brain, and how context- and state-dependent behavioral decisions are controlled are poorly understood questions. Studying the molecules that help convey and integrate such information in neural circuits is an important way to approach these questions. Many years of work in different model organisms have shown that dopamine is a critical neuromodulator for (reward based) associative learning. However, recent findings in vertebrates and invertebrates have demonstrated the complexity and heterogeneity of dopaminergic neuron populations and their functional implications in many adaptive behaviors important for survival. For example, dopaminergic neurons can integrate external sensory information, internal and behavioral states, and learned experience in the decision making circuitry. Several recent advances in methodologies and the availability of a synaptic level connectome of the whole-brain circuitry of Drosophila melanogaster make the fly an attractive system to study the roles of dopamine in decision making and state-dependent behavior. In particular, a learning and memory center-the mushroom body-is richly innervated by dopaminergic neurons that enable it to integrate multi-modal information according to state and context, and to modulate decision-making and behavior.
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Affiliation(s)
- K. P. Siju
- School of Life Sciences, Department of Molecular Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Jean-Francois De Backer
- School of Life Sciences, Department of Molecular Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Ilona C. Grunwald Kadow
- School of Life Sciences, Department of Molecular Life Sciences, Technical University of Munich, 85354 Freising, Germany
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