Evidence map›Paper›PMID 41703147›Full record

ArticleScientific reports2026

Excitatory GABA receptors shape locomotor circuit organization in C. elegans.

Xingran Wang, Kenji Mizuguchi, Kosuke Hashimoto

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Frontiers in aging neuroscience · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xingran WangLaboratory for Computational Biology, Institute for Protein Research, The University of Osaka, 3-2 Yamadaoka, Suita-shi, Osaka, 565-0871, Japan.
Kenji MizuguchiLaboratory for Computational Biology, Institute for Protein Research, The University of Osaka, 3-2 Yamadaoka, Suita-shi, Osaka, 565-0871, Japan.
Kosuke HashimotoLaboratory for Computational Biology, Institute for Protein Research, The University of Osaka, 3-2 Yamadaoka, Suita-shi, Osaka, 565-0871, Japan. kosuke.hashimoto@protein.osaka-u.ac.jp.

Funding

Japan Society for the Promotion of Science JP24K09423
6 · The paper itself

Abstract

Caenorhabditis elegans encodes 102 cys-loop ligand-gated ion channels (LGICs) via the lgc gene family, including excitatory γ-aminobutyric acid (GABA) receptors not found in vertebrates. Although GABA is classically inhibitory, in C. elegans it can also elicit excitation. However, how those excitatory GABA receptors are organized within motor circuits remains poorly understood. Using publicly available single-cell transcriptomic datasets, we found that lgc genes are broadly enriched in locomotion motor neurons, largely driven by GABA receptor–encoding members. Among these, LGC-35 and EXP-1—both excitatory GABA receptors—exhibit subtype-specific and spatially biased expression patterns. A-type motor neurons, which mediate backward locomotion, display striking posterior enrichment of lgc-35 and exp-1, whose largely non-overlapping distributions suggest distinct functional roles. Connectomic analysis, which reconstructs synaptic connections from C. elegans whole-animal electron microscopy data, reveals direct GABAergic input from D-type to A-type motor neurons, suggesting that GABA from D-type neurons excites A-type neurons to regulate backward locomotion. These findings revise classical inhibitory-centric models of GABAergic locomotor control and highlight the role of excitatory GABA signaling in directionally precise motor output.

Indexed as

Caenorhabditis elegansCaenorhabditis elegans ProteinsLocomotionMotor NeuronsReceptors, GABAAnimalsgamma-Aminobutyric AcidCaenorhabditis elegans Proteinsgamma-Aminobutyric AcidReceptors, GABA

Identifiers

PMID41703147
PMCPMC13003095

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.