1
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Venkadesh S, Santarelli A, Boesen T, Dong HW, Ascoli GA. Combinatorial quantification of distinct neural projections from retrograde tracing. Nat Commun 2023; 14:7271. [PMID: 37949860 PMCID: PMC10638408 DOI: 10.1038/s41467-023-43124-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
Comprehensive quantification of neuronal architectures underlying anatomical brain connectivity remains challenging. We introduce a method to identify distinct axonal projection patterns from a source to a set of target regions and the count of neurons with each pattern. A source region projecting to n targets could have 2n-1 theoretically possible projection types, although only a subset of these types typically exists. By injecting uniquely labeled retrograde tracers in k target regions (k < n), one can experimentally count the cells expressing different color combinations in the source region. The neuronal counts for different color combinations from n-choose-k experiments provide constraints for a model that is robustly solvable using evolutionary algorithms. Here, we demonstrate this method's reliability for 4 targets using simulated triple injection experiments. Furthermore, we illustrate the experimental application of this framework by quantifying the projections of male mouse primary motor cortex to the primary and secondary somatosensory and motor cortices.
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Affiliation(s)
- Siva Venkadesh
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA, 22030, USA
- Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA, 22030, USA
| | - Anthony Santarelli
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90089, USA
| | - Tyler Boesen
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90089, USA
| | - Hong-Wei Dong
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90089, USA
| | - Giorgio A Ascoli
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA, 22030, USA.
- Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA, 22030, USA.
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2
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Venkadesh S, Santarelli A, Boesen T, Dong H, Ascoli GA. Combinatorial quantification of distinct neural projections from retrograde tracing. Res Sq 2023:rs.3.rs-2454289. [PMID: 36711802 PMCID: PMC9882684 DOI: 10.21203/rs.3.rs-2454289/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Comprehensive quantification of neuronal architectures underlying anatomical brain connectivity remains challenging. We introduce a method to identify the distinct axonal projection patterns from a source to a set of target regions and the count of neurons with each pattern. For a source region projecting to n targets, there are 2n - 1 theoretically possible projection types, although only a subset of these types typically exists. By injecting uniquely labeled retrograde tracers in k regions (k < n), one can experimentally count the cells expressing different combinations of colors in the source region1,2. Such an experiment can be performed for n choose k combinations. The counts of cells with different color combinations from all experiments provide constraints for a system of equations that include 2n - 1 unknown variables, each corresponding to the count of neurons for a projection pattern. Evolutionary algorithms prove to be effective at solving the resultant system of equations, thus allowing the determination of the counts of neurons with each of the possible projection patterns. Numerical analysis of simulated 4 choose 3 retrograde injection experiments using surrogate data demonstrates reliable and precise count estimates for all projection neuron types. We illustrate the experimental application of this framework by quantifying the projections of mouse primary motor cortex to four prominent targets: the primary and secondary somatosensory and motor cortices.
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Affiliation(s)
- Siva Venkadesh
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, Virginia 22030, USA
- Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, Virginia 22030, USA
| | - Anthony Santarelli
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90089, USA
| | - Tyler Boesen
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90089, USA
| | - Hongwei Dong
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90089, USA
| | - Giorgio A. Ascoli
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, Virginia 22030, USA
- Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, Virginia 22030, USA
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3
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Hahn JD, Gao L, Boesen T, Gou L, Hintiryan H, Dong HW. Macroscale connections of the mouse lateral preoptic area and anterior lateral hypothalamic area. J Comp Neurol 2022; 530:2254-2285. [PMID: 35579973 PMCID: PMC9283274 DOI: 10.1002/cne.25331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 04/05/2022] [Accepted: 04/07/2022] [Indexed: 11/25/2022]
Abstract
The macroscale neuronal connections of the lateral preoptic area (LPO) and the caudally adjacent lateral hypothalamic area anterior region (LHAa) were investigated in mice by anterograde and retrograde axonal tracing. Both hypothalamic regions are highly and diversely connected, with connections to >200 gray matter regions spanning the forebrain, midbrain, and rhombicbrain. Intrahypothalamic connections predominate, followed by connections with the cerebral cortex and cerebral nuclei. A similar overall pattern of LPO and LHAa connections contrasts with substantial differences between their input and output connections. Strongest connections include outputs to the lateral habenula, medial septal and diagonal band nuclei, and inputs from rostral and caudal lateral septal nuclei; however, numerous additional robust connections were also observed. The results are discussed in relation to a current model for the mammalian forebrain network that associates LPO and LHAa with a range of functional roles, including reward prediction, innate survival behaviors (including integrated somatomotor and physiological control), and affect. The present data suggest a broad and intricate role for LPO and LHAa in behavioral control, similar in that regard to previously investigated LHA regions, contributing to the finely tuned sensory‐motor integration that is necessary for behavioral guidance supporting survival and reproduction.
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Affiliation(s)
- Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, California, USA
| | - Lei Gao
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Tyler Boesen
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Lin Gou
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Houri Hintiryan
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Hong-Wei Dong
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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4
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Foster NN, Barry J, Korobkova L, Garcia L, Gao L, Becerra M, Sherafat Y, Peng B, Li X, Choi JH, Gou L, Zingg B, Azam S, Lo D, Khanjani N, Zhang B, Stanis J, Bowman I, Cotter K, Cao C, Yamashita S, Tugangui A, Li A, Jiang T, Jia X, Feng Z, Aquino S, Mun HS, Zhu M, Santarelli A, Benavidez NL, Song M, Dan G, Fayzullina M, Ustrell S, Boesen T, Johnson DL, Xu H, Bienkowski MS, Yang XW, Gong H, Levine MS, Wickersham I, Luo Q, Hahn JD, Lim BK, Zhang LI, Cepeda C, Hintiryan H, Dong HW. The mouse cortico-basal ganglia-thalamic network. Nature 2021; 598:188-194. [PMID: 34616074 PMCID: PMC8494639 DOI: 10.1038/s41586-021-03993-3] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/03/2021] [Indexed: 12/05/2022]
Abstract
The cortico–basal ganglia–thalamo–cortical loop is one of the fundamental network motifs in the brain. Revealing its structural and functional organization is critical to understanding cognition, sensorimotor behaviour, and the natural history of many neurological and neuropsychiatric disorders. Classically, this network is conceptualized to contain three information channels: motor, limbic and associative1–4. Yet this three-channel view cannot explain the myriad functions of the basal ganglia. We previously subdivided the dorsal striatum into 29 functional domains on the basis of the topography of inputs from the entire cortex5. Here we map the multi-synaptic output pathways of these striatal domains through the globus pallidus external part (GPe), substantia nigra reticular part (SNr), thalamic nuclei and cortex. Accordingly, we identify 14 SNr and 36 GPe domains and a direct cortico-SNr projection. The striatonigral direct pathway displays a greater convergence of striatal inputs than the more parallel striatopallidal indirect pathway, although direct and indirect pathways originating from the same striatal domain ultimately converge onto the same postsynaptic SNr neurons. Following the SNr outputs, we delineate six domains in the parafascicular and ventromedial thalamic nuclei. Subsequently, we identify six parallel cortico–basal ganglia–thalamic subnetworks that sequentially transduce specific subsets of cortical information through every elemental node of the cortico–basal ganglia–thalamic loop. Thalamic domains relay this output back to the originating corticostriatal neurons of each subnetwork in a bona fide closed loop. Mesoscale connectomic mapping of the cortico–basal ganglia–thalamic network reveals key architectural and information processing features.
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Affiliation(s)
- Nicholas N Foster
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. .,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Joshua Barry
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Laura Korobkova
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Luis Garcia
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lei Gao
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marlene Becerra
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yasmine Sherafat
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bo Peng
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Jun-Hyeok Choi
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Lin Gou
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Brian Zingg
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sana Azam
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Darrick Lo
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Neda Khanjani
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bin Zhang
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jim Stanis
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ian Bowman
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kaelan Cotter
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chunru Cao
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Seita Yamashita
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Amanda Tugangui
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China
| | - Tao Jiang
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Xueyan Jia
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Zhao Feng
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Sarvia Aquino
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hyun-Seung Mun
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Muye Zhu
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Anthony Santarelli
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nora L Benavidez
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Monica Song
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Gordon Dan
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marina Fayzullina
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah Ustrell
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tyler Boesen
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David L Johnson
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hanpeng Xu
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Michael S Bienkowski
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - X William Yang
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience, Los Angeles, CA, USA
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China
| | - Michael S Levine
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Ian Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.,School of Biomedical Engineering, Hainan University, Haikou, China
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Byung Kook Lim
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Li I Zhang
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Carlos Cepeda
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Houri Hintiryan
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hong-Wei Dong
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. .,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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5
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Benavidez NL, Bienkowski MS, Zhu M, Garcia LH, Fayzullina M, Gao L, Bowman I, Gou L, Khanjani N, Cotter KR, Korobkova L, Becerra M, Cao C, Song MY, Zhang B, Yamashita S, Tugangui AJ, Zingg B, Rose K, Lo D, Foster NN, Boesen T, Mun HS, Aquino S, Wickersham IR, Ascoli GA, Hintiryan H, Dong HW. Organization of the inputs and outputs of the mouse superior colliculus. Nat Commun 2021; 12:4004. [PMID: 34183678 PMCID: PMC8239028 DOI: 10.1038/s41467-021-24241-2] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/02/2021] [Indexed: 11/16/2022] Open
Abstract
The superior colliculus (SC) receives diverse and robust cortical inputs to drive a range of cognitive and sensorimotor behaviors. However, it remains unclear how descending cortical input arising from higher-order associative areas coordinate with SC sensorimotor networks to influence its outputs. Here, we construct a comprehensive map of all cortico-tectal projections and identify four collicular zones with differential cortical inputs: medial (SC.m), centromedial (SC.cm), centrolateral (SC.cl) and lateral (SC.l). Further, we delineate the distinctive brain-wide input/output organization of each collicular zone, assemble multiple parallel cortico-tecto-thalamic subnetworks, and identify the somatotopic map in the SC that displays distinguishable spatial properties from the somatotopic maps in the neocortex and basal ganglia. Finally, we characterize interactions between those cortico-tecto-thalamic and cortico-basal ganglia-thalamic subnetworks. This study provides a structural basis for understanding how SC is involved in integrating different sensory modalities, translating sensory information to motor command, and coordinating different actions in goal-directed behaviors.
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Affiliation(s)
- Nora L Benavidez
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael S Bienkowski
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Muye Zhu
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Luis H Garcia
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Marina Fayzullina
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Lei Gao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Ian Bowman
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Lin Gou
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Neda Khanjani
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kaelan R Cotter
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Laura Korobkova
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marlene Becerra
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chunru Cao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Monica Y Song
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Bin Zhang
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Seita Yamashita
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Amanda J Tugangui
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Brian Zingg
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Kasey Rose
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Darrick Lo
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Nicholas N Foster
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Tyler Boesen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Hyun-Seung Mun
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Sarvia Aquino
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ian R Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Houri Hintiryan
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Hong-Wei Dong
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
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6
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Hintiryan H, Bowman I, Johnson DL, Korobkova L, Zhu M, Khanjani N, Gou L, Gao L, Yamashita S, Bienkowski MS, Garcia L, Foster NN, Benavidez NL, Song MY, Lo D, Cotter KR, Becerra M, Aquino S, Cao C, Cabeen RP, Stanis J, Fayzullina M, Ustrell SA, Boesen T, Tugangui AJ, Zhang ZG, Peng B, Fanselow MS, Golshani P, Hahn JD, Wickersham IR, Ascoli GA, Zhang LI, Dong HW. Connectivity characterization of the mouse basolateral amygdalar complex. Nat Commun 2021; 12:2859. [PMID: 34001873 PMCID: PMC8129205 DOI: 10.1038/s41467-021-22915-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 03/25/2021] [Indexed: 11/08/2022] Open
Abstract
The basolateral amygdalar complex (BLA) is implicated in behaviors ranging from fear acquisition to addiction. Optogenetic methods have enabled the association of circuit-specific functions to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. Here, we apply machine-learning based computational and informatics analysis techniques to the results of circuit-tracing experiments to create a foundational, comprehensive BLA connectivity map. The analyses identify three distinct domains within the anterior BLA (BLAa) that house target-specific projection neurons with distinguishable morphological features. We identify brain-wide targets of projection neurons in the three BLAa domains, as well as in the posterior BLA, ventral BLA, posterior basomedial, and lateral amygdalar nuclei. Inputs to each nucleus also are identified via retrograde tracing. The data suggests that connectionally unique, domain-specific BLAa neurons are associated with distinct behavior networks.
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Affiliation(s)
- Houri Hintiryan
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
| | - Ian Bowman
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - David L Johnson
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Laura Korobkova
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Muye Zhu
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Neda Khanjani
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lin Gou
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Lei Gao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Seita Yamashita
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Michael S Bienkowski
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Luis Garcia
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Nicholas N Foster
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Nora L Benavidez
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Monica Y Song
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Darrick Lo
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kaelan R Cotter
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Marlene Becerra
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarvia Aquino
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chunru Cao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Ryan P Cabeen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jim Stanis
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marina Fayzullina
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah A Ustrell
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tyler Boesen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Amanda J Tugangui
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Zheng-Gang Zhang
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Physiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Bo Peng
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Michael S Fanselow
- Brain Research Institute, Department of Psychology, University of California, Los Angeles, CA, USA
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- West Los Angeles Veterans Administration Medical Center, Los Angeles, CA, USA
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Ian R Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Li I Zhang
- Center for Neural Circuitry & Sensory Processing Disorders, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hong-Wei Dong
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
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7
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Arnold E, Bell N, Boesen T, Boldt N, Borden A, Bro J, Douglas M, Fuller J, Furumo Q, Gummin C, Keuler A, Kim D, Martinez A, Moldenhauer D, Mullooly I, Nelsen‐Freund R, Ogunkunle D, Ortega L, Palmersheim S, Sabatino T, Schwabe B, Sung R, Trzcinski K, Ulschmid C, Klestinski K, Kaiser C, Vogt D, Cunningham CW. One Indole Ring to Rule Them All: How Modeling of Naltrindole Bound to the Delta Opioid Receptor Can Aid the Development of Novel Analgesics. FASEB J 2013. [DOI: 10.1096/fasebj.27.1_supplement.lb171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- E. Arnold
- Marquette University High SchoolMilwaukeeWI
| | - N. Bell
- Marquette University High SchoolMilwaukeeWI
| | - T. Boesen
- Marquette University High SchoolMilwaukeeWI
| | - N. Boldt
- Marquette University High SchoolMilwaukeeWI
| | - A. Borden
- Marquette University High SchoolMilwaukeeWI
| | - J. Bro
- Marquette University High SchoolMilwaukeeWI
| | - M. Douglas
- Marquette University High SchoolMilwaukeeWI
| | - J. Fuller
- Marquette University High SchoolMilwaukeeWI
| | - Q. Furumo
- Marquette University High SchoolMilwaukeeWI
| | - C. Gummin
- Marquette University High SchoolMilwaukeeWI
| | - A. Keuler
- Marquette University High SchoolMilwaukeeWI
| | - D. Kim
- Marquette University High SchoolMilwaukeeWI
| | | | | | | | | | | | - L. Ortega
- Marquette University High SchoolMilwaukeeWI
| | | | | | - B. Schwabe
- Marquette University High SchoolMilwaukeeWI
| | - R. Sung
- Marquette University High SchoolMilwaukeeWI
| | | | | | | | - C. Kaiser
- Marquette University High SchoolMilwaukeeWI
| | - D. Vogt
- Marquette University High SchoolMilwaukeeWI
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8
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Abstract
Mycoplasma hominis has been previously described as a heterogeneous species, and in the present study intraspecies diversity of 20 M. hominis isolates from different individuals was analyzed using parts of the unlinked gyrase B (gyrB), elongation factor Tu (tuf), SRalpha homolog (ftsY), hitB-hitL, excinuclease ABC subunit A (uvrA) and glyceraldehyde-3-phosphate dehydrogenase (gap) genes. The level of variability of these M. hominis genes was low compared with the housekeeping genes from Helicobacter pylori and Neisseria meningitidis, but only few M. hominis isolates had identical sequences in all genes indicating the presence of recombination. In order to test for intergenic recombination, phylogenetic trees were reconstructed for each of the genes but no well-supported bifurcating phylogenetic trees could be obtained. The genes were tested for intragenic recombination using the correlation between linkage disequilibrium and distance between the segregating sites, by the homoplasy ratio (H ratio), and by compatibility matrices. The gap gene showed well-supported evidence for high levels of recombination, whereas recombination was less frequent and not significant within the other genes. The analysis revealed intergenic and intragenic recombination in M. hominis and this may explain the high intraspecies variability. The results obtained in the present study may be of importance for future population studies of Mycoplasma species.
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Affiliation(s)
- I Z Søgaard
- Department of Genetics and Ecology, Bioinformatics Research Center (BIRC), University of Aarhus, Ny Munkegade, Building 540, DK-8000 Aarhus C, Denmark
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9
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Abstract
The variable adherence-associated (Vaa) adhesin of the opportunistic human pathogen Mycoplasma hominis is a surface-exposed, membrane-associated protein involved in the attachment of the bacterium to host cells. The molecular masses of recombinant 1 and 2 cassette forms of the protein determined by a light-scattering (LS) method were 23.9 kD and 36.5 kD, respectively, and corresponded to their monomeric forms. Circular dichroism (CD) spectroscopy of the full-length forms indicated that the Vaa protein has an alpha-helical content of approximately 80%. Sequence analysis indicates the presence of coiled-coil domains in both the conserved N-terminal and antigenic variable C-terminal part of the Vaa adhesin. Experimental results obtained with recombinant proteins corresponding to the N- or C-terminal parts of the shortest one-cassette form of the protein were consistent with the hypothesis of two distinct coiled-coil regions. The one-cassette Vaa monomer appears to be an elongated protein with a axial shape ratio of 1:10. Analysis of a two-cassette Vaa type reveals a similar axial shape ratio. The results are interpreted in terms of the topological organization of the Vaa protein indicating the localization of the adherence-mediating structure.
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Affiliation(s)
- T Boesen
- Department of Medical Microbiology and Immunology, University of Aarhus, DK-8000 Aarhus C, Denmark.
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10
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Püschl A, Boesen T, Zuccarello G, Dahl O, Pitsch S, Nielsen PE. Synthesis of pyrrolidinone PNA: a novel conformationally restricted PNA analogue. J Org Chem 2001; 66:707-12. [PMID: 11430086 DOI: 10.1021/jo001000k] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
To preorganize PNA for duplex formation, a new cyclic pyrrolidinone PNA analogue has been designed. In this analogue the aminoethylglycine backbone and the methylenecarbonyl linker are connected, introducing two chiral centers compared to PNA. The four stereoisomers of the adenine analogue were synthesized, and the hybridization properties of PNA decamers containing one analogue were measured against complementary DNA, RNA, and PNA strands. The (3S,5R) isomer was shown to have the highest affinity toward RNA, and to recognize RNA and PNA better than DNA. The (3S,5R) isomer was used to prepare a fully modified decamer which bound to rU10 with only a small decrease in Tm (delta Tm/mod = 1 degree C) relative to aminoethylglycine PNA.
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Affiliation(s)
- A Püschl
- Center for Biomolecular Recognition, Department for Biochemistry and Genetics, Biochemistry Laboratory B, Panum Institute, Blegdamsvej 3C, DK-2200, Copenhagen, Denmark
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11
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Mygind T, Zeuthen Søgaard I, Melkova R, Boesen T, Birkelund S, Christiansen G. Cloning, sequencing and variability analysis of the gap gene from Mycoplasma hominis. FEMS Microbiol Lett 2000; 183:15-21. [PMID: 10650196 DOI: 10.1111/j.1574-6968.2000.tb08927.x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
The gap gene encodes the glycolytic enzyme glyceraldehyde 3-phosphate dehydrogenase (GAPDH). The gene was cloned and sequenced from the Mycoplasma hominis type strain PG21(T). The intraspecies variability was investigated by inspection of restriction fragment length polymorphism (RFLP) patterns after polymerase chain reaction (PCR) amplification of the gap gene from 15 strains and furthermore by sequencing of part of the gene in eight strains. The M. hominis gap gene was found to vary more than the Escherichia coli counterpart, but the variation at nucleotide level gave rise to only a few amino acid substitutions. To verify that the gene was expressed in M. hominis, a polyclonal antibody was produced and tested against whole cell protein from 15 strains. The enzyme was expressed in all strains investigated as a 36-kDa protein. All strains except type strain PG21(T) showed reaction to a 104-kDa band in addition to the expected 36-kDa band. The protein reacting at 104 kDa is a M. hominis protein with either an epitope similar to one on GAPDH, or it is an immunoglobulin binding protein.
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Affiliation(s)
- T Mygind
- Department of Medical Microbiology and Immunology, The Bartholin Building, University of Aarhus, DK-8000, Aarhus C, Denmark
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12
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Christiansen G, Boesen T, Hjerno K, Daugaard L, Mygind P, Madsen AS, Knudsen K, Falk E, Birkelund S. Molecular biology of Chlamydia pneumoniae surface proteins and their role in immunopathogenicity. Am Heart J 1999; 138:S491-5. [PMID: 10539856 DOI: 10.1016/s0002-8703(99)70283-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The association of Chlamydia pneumoniae with the development of atherosclerosis is based on serology and on detection of C pneumoniae-specific DNA by polymerase chain reaction in the atheromas. METHODS AND RESULTS Because the humoral immune response frequently recognizes epitopes present on the surface of the bacteria, we analyzed what components are present on the C pneumoniae surface. We identified a family of proteins, the GGAI or Omp4-15 proteins, of which at least 3 are present on the surface of C pneumoniae. We immunized rabbits with recombinant GGAI proteins and used these antibodies in immunofluorescence microscopy of experimentally infected mice. In lung sections, a massive infiltration with polymorph nuclear neutrophil cells was observed. In the bronchial epithelial cells, C pneumoniae inclusions were seen. Evidence was found of differential expression of the GGAI proteins. CONCLUSIONS On the basis of surface localization, differential expression, and the fact that the proteins are recognized by the human humoral immune response, we speculate whether these proteins, in addition to the lipopolysaccharides, are of importance for the immunopathogenesis of C pneumoniae.
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Affiliation(s)
- G Christiansen
- Department of Medical Microbiology, Department of Molecular and Structural Biology, University of Aarhus, Denmark
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13
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Abstract
Mycoplasma hominis contains a variable adherence-associated (vaa) gene. To classify variants of the vaa genes, we examined 42 M. hominis isolated by PCR, DNA sequencing and immunoblotting. This uncovered the existence of five gene categories. Comparison of the gene types revealed a modular composition of the Vaa proteins. The proteins constituted a conserved N-terminal part followed by a varying number of interchangeable cassettes encoding approximately 110 amino acids with conserved sequences boxes flanking the cassettes. The interchangeable cassettes showed a high mutual homology and a conserved leucine zipper motif. The smallest product contained only one cassette and the largest five. Additionally, two types of stop mutations caused by substitutions resulting in the expression of truncated Vaa proteins were observed. Our results expand the known potential of the Vaa system in generating antigen variation.
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Affiliation(s)
- T Boesen
- Department of Medical Microbiology and Immunology, University of Aarhus, Denmark
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14
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Christiansen G, Jensen LT, Boesen T, Emmersen J, Ladefoged SA, Schiøtz LK, Birkelund S. Molecular biology of Mycoplasma. Wien Klin Wochenschr 1997; 109:557-61. [PMID: 9286059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Mycoplasmas are the smallest free living microorganisms with the smallest genome. The G+C content is in general low (25-33%) and the coding capacity is about 600 proteins. Mycoplasma species are phylogenetically related, they use the genetic codon UGA for tryptophan, and show rapid evolution, with a high rate of divergence. The genomes of Mycoplasma genitalium and Mycoplasma pneumoniae have been fully sequenced. Striking features of the M. genitalium sequencing project are the presence of a high number of membrane proteins with no resemblance to previously sequenced genes and the presence of repeated fragments of the gene encoding the tip-localized 140 kDa adhesin (MgPa). Many Mycoplasma species display a high frequency of antigenic variation, both as phase and size variation of individual antigens. Mycoplasma hominis isolates are known to be antigenic heterogeneous, as reflected in the reactivity with monoclonal antibodies (MAbs). The genetics of the antigenic variation has been studied for three different surface exposed antigens: P120, Lmp, and P50/Vaa. The gene encoding P120 had a hyper-variable region in the N-terminal region. In addition, a second gene with homology to p120 was identified. The gene encoding Lmp, a 135 kDa protein is repeated and both genes are translated and both contain internal repeated sequences. Deletion mutants in the lmp gene were obtained by cultivation of M. hominis PG21 with MAb 552 specific for the repeated part of Lmp. One of the lmp genes had deletions of from four to eight repeats. The other gene was left unaltered. The genes encoding P50/Vaa show a different form of variability where domains of the genes seem to be exchangeable. The genomic maps of five M. hominis strains showed that even though the size of the genomes varied the position of the different genes were in general conserved.
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Affiliation(s)
- G Christiansen
- Department of Medical Microbiology and Immunology, University of Aarhus, Denmark
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15
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Duus BR, Boesen T, Kruse KV, Nielsen KB. [Minor head injuries. Prognostic factors in the evaluation of patients]. Ugeskr Laeger 1994; 156:5510-3. [PMID: 7941085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
In order to identify signs to be used in the decision whether or not to admit patients after minor head injuries (MHI), the records of 713 females and 1163 males were reviewed. Skull X-ray was not obtained routinely, all patients were able to talk and walk when they reached medical contact. Nine patients developed an intracranial complication, three had an operation and one died. The risk of developing an ICC was 16.7% when the patient was agitated, 3.4% in the presence of impaired consciousness and 2.1% when positive neurological signs were observed at the time of examination. Based on the medical history, amnesia for more than five minutes and vomiting were associated with a risk of 3.3% and 1.2% respectively. The risk increased considerably in the presence of two of the above mentioned signs and was 60% if the patient was agitated and had amnesia for more than five minutes. Consequently, we recommend that all patients with one or more of the above symptoms or alcohol intoxication after a MHI should be admitted for observation. This policy may hold the possibility of considerable economic savings.
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Affiliation(s)
- B R Duus
- Ortopaedkirurgisk afdeling, Amtssygehuset i Glostrup
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16
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Abstract
To assess signs that might be used in the decision whether or not to admit a patient with minor head injury, the records of 713 female and 1163 male patients were reviewed. Skull radiographs were not obtained routinely; all patients were able to walk and talk when they reached medical contact. Nine patients developed an intracranial complication. The risk of developing such a complication was 16.7 per cent when the patient was agitated, 3.4 per cent in the presence of impaired consciousness and 2.1 per cent when positive neurological signs were observed at the time of examination. Based on the medical history, amnesia for > 5 min and vomiting were associated with a risk of 3.3 and 1.2 per cent respectively; the risk increased considerably in the presence of both. It is recommended that all patients presenting themselves with one or more of the above symptoms or signs, or with alcohol intoxication, after a minor head injury be admitted for observation. If these guidelines had been used, all patients with an intracranial complication would have been detected, and 44.5 per cent of the bed-days used would have been saved.
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Affiliation(s)
- B R Duus
- Department of Orthopaedic Surgery, University Hospital, Sygehus, Denmark
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17
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Abstract
Infection around the tonsillar region does not always mean the presence of a peritonsillar abscess although the condition of peritonsillitis without abscess formation may clinically present similarly. It is, however, of therapeutic importance to distinguish between the two conditions. Treatment for abscess is surgical: aspiration, incision and drainage or immediate tonsillectomy. In contrast, phlegmonous peritonsillitis only requires antibiotics. In order to evaluate the diagnostic implications of preoperative ultrasonography in patients referred for treatment of peritonsillar abscess, 27 consecutive patients were subjected to bilateral ultrasound examination to visualize the tonsillar region. The transducer used was placed just below the mandibular angle, pointing posteriorly and cranially. The results of this study showed that it was possible to verify the presence of an abscess in approximately 90% of the cases. We suggest that this examination be performed whenever the normal clinical examination is insufficient due to trismus, lack of patient cooperation, etc.
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Affiliation(s)
- T Boesen
- Department of Otolaryngology and Head-Neck Surgery, Rigshospitalet, University of Copenhagen, Denmark
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18
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Duus BR, Kruse KV, Nielsen KB, Boesen T. [Minor head injuries in a Copenhagen district. 1. Epidemiology]. Ugeskr Laeger 1991; 153:2111-3. [PMID: 1866813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The records of 1,218 males and 743 females admitted to Copenhagen County Hospital in Glostrup between January 1 1985 and December 31 1986 were studied. The mean age was 28.2 years (SD = 20.3 years). The incidence rate was 360 per 100,000 inhabitants per year and was between 30 and 130% larger for males than females (p less than 0.001), the incidence rate was 26% higher in this study as compared to the rate in Denmark as a whole. Patients between 0 and 14 years were most frequently admitted between noon and the early evening hours and those between 15 and 64 years most frequently from late in the afternoon until the early night. The distribution was uniform throughout the week. There was a tendency towards more frequent admissions in the April and July trimesters. 74% of the patients were admitted at hours when the staff was minimal. This category comprised 4.9% of all patients admitted as emergencies to the hospital. The mean hospitalization was 1.6 days (range 1-14 days). Females were hospitalized longer than males (p less than 0.001). Furthermore, hospitalization was significantly longer, the older the patients were (p = 0.005). Patients with minor head trauma constitute a considerable work load in the hospital, especially outside normal working-hours.
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Affiliation(s)
- B R Duus
- Københavns Amts Sygehus i Glostrup
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19
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Duus BR, Nielsen KB, Kruse KV, Boesen T. [Minor head injuries in a Copenhagen district. 2. Causes, simultaneous lesions, alcohol and economic aspects]. Ugeskr Laeger 1991; 153:2114-6. [PMID: 1866814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
In order to elucidate causes, simultaneous lesions, alcohol intoxication and economic aspects of admission of patients with minor head traumas, the records of 1,961 patients admitted in 1985 and 1986 were reviewed. 35% were admitted after traffic accidents, 18% after assaults and 17% because of home accidents. In the age group 30-39 years, 40% of the males were admitted after assaults. 7% sustained fractures of the extremities, 4% facial fractures and 3% and 1% thoracic and abdominal lesions respectively. At least 28% of all patients, more than 50% of the patients admitted during evening- and night hours and 51% of the males between 14 and 65 years were clinically alcohol intoxicated. 1,876 patients were admitted solely for observation, nine developed an intracranial complication, three were operated upon and one died. The bed day expenditure was 2.7 mill. Dkr. (pounds 225,000), and for the entire nation 64.8 mill. Dkr (pounds 5,400,000). The costs for diagnosing one case of intracranial lesion in Denmark were 925.000 Dkr. (approximately pounds 80,000) and 2.1 mill. Dkr. (pounds 180,000) to find the cases requiring treatment (1985 level).
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Affiliation(s)
- B R Duus
- Københavns Amts Sygehus i Glostrup
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Abstract
During the 2-year period 1985-86 a total of 1,876 patients were admitted to our hospital after milder head trauma including cerebral concussion. Two hundred and eighty four patients who had a skull X-ray were not selected from guidelines. In 1,592 patients without a skull X-ray, signs of an intracranial complication developed in six cases and were verified by CT. In the 284 patients with skull X-ray a fracture was demonstrated in 25, and of these 25 patients only one patient disclosed a cerebral contusion. In the 259 patients with skull X-ray, but without demonstration of fracture, there were subsequently seen one subdural haematoma and one cerebral contusion. The incidence of intracranial complications in patients without and with skull X-ray with or without fracture does not differ significantly. In these circumstances we do not find any justification for routine skull X-ray after milder head trauma.
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Affiliation(s)
- J Rosenørn
- University Clinic of Neurosurgery, Copenhagen County Hospital, Glostrup, Denmark
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Boesen T, Fiellau-Nikolajsen M. [The value of routine preoperative radiography of the nasal sinuses in rhinosurgery. A retrospective controlled study]. Ugeskr Laeger 1987; 149:1937-9. [PMID: 3433411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Boesen T. [In vivo staining of the outer eye]. Ugeskr Laeger 1981; 143:2343-5. [PMID: 7303250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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Gluud BS, Boesen T, Norn M. [The effect of a fatty vehicle on the cornea and conjunctiva. In vivo staining following instilling of oil]. Ugeskr Laeger 1981; 143:2345-7. [PMID: 7303251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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