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Liu M, Wang C, Huo L, Cao J, Mao X, He Z, Hu C, Sun H, Deng W, He W, Chen Y, Gu M, Liao J, Guo N, He X, Wu Q, Chen J, Zhang L, Wang X, Shang C, Dong J. Complexin-1 enhances ultrasound neurotransmission in the mammalian auditory pathway. Nat Genet 2024; 56:1503-1515. [PMID: 38834904 DOI: 10.1038/s41588-024-01781-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 04/25/2024] [Indexed: 06/06/2024]
Abstract
Unlike megabats, which rely on well-developed vision, microbats use ultrasonic echolocation to navigate and locate prey. To study ultrasound perception, here we compared the auditory cortices of microbats and megabats by constructing reference genomes and single-nucleus atlases for four species. We found that parvalbumin (PV)+ neurons exhibited evident cross-species differences and could respond to ultrasound signals, whereas their silencing severely affected ultrasound perception in the mouse auditory cortex. Moreover, megabat PV+ neurons expressed low levels of complexins (CPLX1-CPLX4), which can facilitate neurotransmitter release, while microbat PV+ neurons highly expressed CPLX1, which improves neurotransmission efficiency. Further perturbation of Cplx1 in PV+ neurons impaired ultrasound perception in the mouse auditory cortex. In addition, CPLX1 functioned in other parts of the auditory pathway in microbats but not megabats and exhibited convergent evolution between echolocating microbats and whales. Altogether, we conclude that CPLX1 expression throughout the entire auditory pathway can enhance mammalian ultrasound neurotransmission.
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Affiliation(s)
- Meiling Liu
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Changliang Wang
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Lifang Huo
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Jie Cao
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Xiuguang Mao
- School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China
| | - Ziqing He
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Chuanxia Hu
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Haijian Sun
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Wenjun Deng
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Weiya He
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Yifu Chen
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Meifeng Gu
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Jiayu Liao
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Ning Guo
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Xiangyang He
- Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Guangdong Public Laboratory of Wild Animal Conservation and Utilization, Institute of Zoology, Guangdong Academy of Sciences, Guangzhou, China
| | - Qian Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jiekai Chen
- CAS Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Libiao Zhang
- Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Guangdong Public Laboratory of Wild Animal Conservation and Utilization, Institute of Zoology, Guangdong Academy of Sciences, Guangzhou, China.
| | - Xiaoqun Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Congping Shang
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China.
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China.
| | - Ji Dong
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China.
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China.
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Smotherman MS, Croft T, Macias S. Biosonar discrimination of fine surface textures by echolocating free-tailed bats. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.969350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Echolocating bats are able to discriminate between different surface textures based on the spectral properties of returning echoes. This capability is likely to be important for recognizing prey and for finding suitably perching sites along smooth cave walls. Previous studies showed that bats may exploit echo spectral interference patterns in returning echoes to classify surface textures, but a systematic assessment of the limits of their discrimination performance is lacking and may provide important clues about the neural mechanisms by which bats reconstruct target features based on echo acoustic cues. We trained three Mexican free-tailed bats (Tadarida brasiliensis) on a Y-maze to discriminate between the surfaces of 10 different sheets of aluminum-oxide abrasive sandpapers differing in standardized grit sizes ranging from 40 grit (coarse, 425 μm mean particle diameter) to 240 grit (fine, 54 μm mean particle diameter). Bats were rewarded for choosing the coarsest of two choices. All three bats easily discriminated all abrasive surfaces from a smooth plexiglass control and between all sandpaper comparisons except the two with the smallest absolute difference in mean particle sizes, the 150 vs. 180 grit (92 vs. 82 μm) and the 220 vs. 240 grit (68 vs. 54 μm) surfaces. These results indicate that echolocating free-tailed bats can use slight variations in the echo spectral envelope to remotely classify very fine surface textures with an acuity of at least 23 μm, which rivals direct tactile discrimination performance of the human hand.
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Macias S, Bakshi K, Troyer T, Smotherman M. The prefrontal cortex of the Mexican free-tailed bat is more selective to communication calls than primary auditory cortex. J Neurophysiol 2022; 128:634-648. [PMID: 35975923 PMCID: PMC9448334 DOI: 10.1152/jn.00436.2021] [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: 09/29/2021] [Revised: 07/20/2022] [Accepted: 08/05/2022] [Indexed: 11/22/2022] Open
Abstract
In this study, we examined the auditory responses of a prefrontal area, the frontal auditory field (FAF), of an echolocating bat (Tadarida brasiliensis) and presented a comparative analysis of the neuronal response properties between the FAF and the primary auditory cortex (A1). We compared single-unit responses from the A1 and the FAF elicited by pure tones, downward frequency-modulated sweeps (dFMs), and species-specific vocalizations. Unlike the A1, FAFs were not frequency tuned. However, progressive increases in dFM sweep rate elicited a systematic increase of response precision, a phenomenon that does not take place in the A1. Call selectivity was higher in the FAF versus A1. We calculated the neuronal spectrotemporal receptive fields (STRFs) and spike-triggered averages (STAs) to predict responses to the communication calls and provide an explanation for the differences in call selectivity between the FAF and A1. In the A1, we found a high correlation between predicted and evoked responses. However, we did not generate reasonable STRFs in the FAF, and the prediction based on the STAs showed lower correlation coefficient than that of the A1. This suggests nonlinear response properties in the FAF that are stronger than the linear response properties in the A1. Stimulating with a call sequence increased call selectivity in the A1, but it remained unchanged in the FAF. These data are consistent with a role for the FAF in assessing distinctive acoustic features downstream of A1, similar to the role proposed for primate ventrolateral prefrontal cortex.NEW & NOTEWORTHY In this study, we examined the neuronal responses of a frontal cortical area in an echolocating bat to behaviorally relevant acoustic stimuli and compared them with those in the primary auditory cortex (A1). In contrast to the A1, neurons in the bat frontal auditory field are not frequency tuned but showed a higher selectivity for social signals such as communication calls. The results presented here indicate that the frontal auditory field may represent an additional processing center for behaviorally relevant sounds.
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Affiliation(s)
- Silvio Macias
- Department of Biology, Texas A&M University, College Station, Texas
| | - Kushal Bakshi
- Institute for Neuroscience, Texas A&M University, College Station, Texas
| | - Todd Troyer
- Department of Neuroscience, Developmental and Regenerative Biology, University of Texas at San Antonio, San Antonio, Texas
| | - Michael Smotherman
- Department of Biology, Texas A&M University, College Station, Texas
- Institute for Neuroscience, Texas A&M University, College Station, Texas
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Beetz MJ, Hechavarría JC. Neural Processing of Naturalistic Echolocation Signals in Bats. Front Neural Circuits 2022; 16:899370. [PMID: 35664459 PMCID: PMC9157489 DOI: 10.3389/fncir.2022.899370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 04/21/2022] [Indexed: 11/18/2022] Open
Abstract
Echolocation behavior, a navigation strategy based on acoustic signals, allows scientists to explore neural processing of behaviorally relevant stimuli. For the purpose of orientation, bats broadcast echolocation calls and extract spatial information from the echoes. Because bats control call emission and thus the availability of spatial information, the behavioral relevance of these signals is undiscussable. While most neurophysiological studies, conducted in the past, used synthesized acoustic stimuli that mimic portions of the echolocation signals, recent progress has been made to understand how naturalistic echolocation signals are encoded in the bat brain. Here, we review how does stimulus history affect neural processing, how spatial information from multiple objects and how echolocation signals embedded in a naturalistic, noisy environment are processed in the bat brain. We end our review by discussing the huge potential that state-of-the-art recording techniques provide to gain a more complete picture on the neuroethology of echolocation behavior.
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Affiliation(s)
- M. Jerome Beetz
- Zoology II, Biocenter, University of Würzburg, Würzburg, Germany
| | - Julio C. Hechavarría
- Institute of Cell Biology and Neuroscience, Goethe University Frankfurt, Frankfurt, Germany
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Macias S, Bakshi K, Garcia-Rosales F, Hechavarria JC, Smotherman M. Temporal coding of echo spectral shape in the bat auditory cortex. PLoS Biol 2020; 18:e3000831. [PMID: 33170833 PMCID: PMC7678962 DOI: 10.1371/journal.pbio.3000831] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 11/20/2020] [Accepted: 10/01/2020] [Indexed: 01/26/2023] Open
Abstract
Echolocating bats rely upon spectral interference patterns in echoes to reconstruct fine details of a reflecting object’s shape. However, the acoustic modulations required to do this are extremely brief, raising questions about how their auditory cortex encodes and processes such rapid and fine spectrotemporal details. Here, we tested the hypothesis that biosonar target shape representation in the primary auditory cortex (A1) is more reliably encoded by changes in spike timing (latency) than spike rates and that latency is sufficiently precise to support a synchronization-based ensemble representation of this critical auditory object feature space. To test this, we measured how the spatiotemporal activation patterns of A1 changed when naturalistic spectral notches were inserted into echo mimic stimuli. Neurons tuned to notch frequencies were predicted to exhibit longer latencies and lower mean firing rates due to lower signal amplitudes at their preferred frequencies, and both were found to occur. Comparative analyses confirmed that significantly more information was recoverable from changes in spike times relative to concurrent changes in spike rates. With this data, we reconstructed spatiotemporal activation maps of A1 and estimated the level of emerging neuronal spike synchrony between cortical neurons tuned to different frequencies. The results support existing computational models, indicating that spectral interference patterns may be efficiently encoded by a cascading tonotopic sequence of neural synchronization patterns within an ensemble of network activity that relates to the physical features of the reflecting object surface. Echolocating bats rely upon spectral interference patterns in echoes to reconstruct fine details of a reflecting object’s shape. This study shows that the latency shifts induced by spectral notch patterns can provide the foundation for an avalanche of neuronal synchrony that is sufficient to support encoding of auditory object shape features during active biosonar.
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Affiliation(s)
- Silvio Macias
- Department of Biology, Texas A&M University, College Station, Texas, United States of America
- * E-mail:
| | - Kushal Bakshi
- Department of Biology, Texas A&M University, College Station, Texas, United States of America
| | | | - Julio C. Hechavarria
- Institut für Zellbiologie und Neurowissenschaft, Goethe-Universität, Frankfurt/M., Germany
| | - Michael Smotherman
- Department of Biology, Texas A&M University, College Station, Texas, United States of America
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