Dimorphic neural network architecture prioritizes sexual-related behaviors in male <i>Caenorhabditis elegans</i>.
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Overview
- Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, China
- International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, China
- School of Systems Science, Beijing Normal University, Beijing, China
- Huitong College, Beijing Normal University, Zhuhai, China
- Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, China
- Department of Neuroscience, City University of Hong Kong, Hong Kong, China
Abstract
Neural network architecture determines its functional output. However, the detailed mechanisms are not well characterized. In this study, we focused on the neural network architectures of male and hermaphrodite Caenorhabditis elegans and the association with sexually dimorphic behaviors. We applied graph theory and computational neuroscience methods to systematically discern the features of these two neural networks. Our findings revealed that a small percentage of sexual-specific neurons exerted dominance throughout the entire male neural network, suggesting males prioritized sexual-related behavior outputs. Based on the structural and dynamical characteristics of two complete neural networks, sub-networks containing sex-specific neurons and their immediate neighbors, or sub-networks exclusively comprising sex-shared neurons, we predicted dimorphic behavioral outcomes for males and hermaphrodites. To verify the prediction, we performed behavioral and calcium imaging experiments and dissected a circuit that is specific for the increased spontaneous local search in males for mate-searching. Our research sheds light on the neural circuits that underlie sexually dimorphic behaviors in C. elegans and provides significant insights into the interconnected relationship between network architecture and functional outcomes at the whole-brain level.
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supp:PMC13095209/elife-102309-code1.zip
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- eLife-VOR-RA-2024-102309
/ — Python, 175 lines, not shown hereSource code 1.py
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Data
No dataset and no data link were found in the paper.
Data availability
All data supporting the findings of this study are available within the article and its Supplementary Information files.
The following previously published dataset was used:
Cook SJ, Jarrell TA, Brittin CA, Wang Y, Bloniarz AE, Yakovlev MA, Nguyen KCQ, Tang LTH, Bayer EA, Duerr JS, Büow HZ, Hobert O, Hall DH, Emmons SW. 2019. Adult hermaphrodite and adult male Data. WormWiring. emmonslab
Reproduced under the paper's license (CC BY), from the paper cited above.
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Version 1, 29 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 15 authors, 1 keyword, 6 MeSH terms, 1 funder, 89 references.
Cite
This paper
Wang, X., Liu, H., Yang, W., Yang, J., Sun, X., Liu, Q., Zhu, Y., Sun, Y., Liu, C., Shi, G., Liu, Q., Zhang, K., Di, Z., Yang, W., & Liu, H. (2026). Dimorphic neural network architecture prioritizes sexual-related behaviors in male &
BibTeX
@article{wang2026dimorph
author = {Wang, Xuebin and Liu, Hanzhang and Yang, Wenjing and Yang, Jingxuan and Sun, Xuehong and Liu, Qiuhan and Zhu, Ying and Sun, Yinghao and Liu, Chunxiuzi and Shi, Guiyuan and Liu, Qiang and Zhang, Ke and Di, Zengru and Yang, Wenxing and Liu, He},
title = {{Dimorphic neural network architecture prioritizes sexual-related behaviors in male \&
journal = {eLife},
year = {2026},
month = apr,
volume = {14},
pages = {RP102309},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/
url = {https://
pmid = {42007675},
pmcid = {PMC13095209}
}
RIS
TY - JOUR
AU - Wang, Xuebin
AU - Liu, Hanzhang
AU - Yang, Wenjing
AU - Yang, Jingxuan
AU - Sun, Xuehong
AU - Liu, Qiuhan
AU - Zhu, Ying
AU - Sun, Yinghao
AU - Liu, Chunxiuzi
AU - Shi, Guiyuan
AU - Liu, Qiang
AU - Zhang, Ke
AU - Di, Zengru
AU - Yang, Wenxing
AU - Liu, He
TI - Dimorphic neural network architecture prioritizes sexual-related behaviors in male &
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/
VL - 14
SP - RP102309
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/
UR - https://
LA - en
ER -
CSL-JSON
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