Biology subjects

Kohsaka, H.

Publications and source records attributed to Kohsaka, H..

13 recordsLinked to original sources

bioRxiv2026

A canonical excitatory-inhibitory motif embedded in the connectome underlies rhythmic locomotion in Drosophila larvae

Rhythmic locomotion is generated by central pattern generators, yet how defined neural circuits generate rhythmic motor waves remains unclear. Theoretical models, including Wilson-Cowan-type excitatory-inhibitory networks, have long proposed that reciprocal interactions between excitatory and inhibitory neuronal populations can generate oscillatory activity and travelling waves, but whether such motifs are physically embedded within biological connectomes is unknown. Here we show that Drosophila larval forward locomotion is generated by a segmentally repeated excitatory-inhibitory network centred on the excitatory A18f and inhibitory A02j neurons. Connectomic analysis revealed that A18f and A02j form reciprocally connected loops coupled across segments, receive input from forward command systems and project to premotor modules controlling muscle contraction and relaxation. Connectome-constrained firing-rate simulations, in which synaptic weights were determined by reconstructed synapse numbers, reproduced rhythmic forward-propagating motor waves with minimal parameter tuning. Physiological recordings revealed activity dynamics and phase relationships consistent with model predictions. Acute and chronic perturbations showed that A18f is required for forward locomotion, whereas optogenetic activation of posterior A18f neurons initiated forward motor waves. Perturbation of inhibitory input to A18f further supported a role for A02j-mediated inhibition. These findings identify a canonical excitatory-inhibitory motif embedded within the connectome as a biological implementation of a rhythm-generating network.

neuroscience↗

bioRxiv2026

The membrane-tethered cis-ligand Belly roll elicits GPCR signaling thereby enabling adaptive avoidance behaviors

Animals adapt their behavioral responses to sensory stimuli through neuromodulatory circuits. Increasing fluid osmolality drives water-seeking behaviors via neuromodulation. However, the neural circuits and molecular mechanisms linking internal osmotic state to behavioral adaptation remain unclear. Here, we show that desiccation stress increases hemolymph osmolality and enhances avoidance of dry substrates in Drosophila larvae, enabling larvae to seek humid environments. We identify the abdominal leucokinin-producing (ABLK) neurons as a key circuit node that mediates this adaptive response. We further show that the GPI-anchored protein Belly roll (Bero), a member of the lymphocyte antigen-6/urokinase-type plasminogen activator receptor (LU) superfamily, acts as a non-canonical endogenous cis-ligand for the neuropeptide G protein-coupled receptor Allatostatin C receptor 2 (AstC-R2). Genetic, biochemical, and imaging analyses revealed that Bero activates AstC-R2 to induce phospholipase C {beta} (PLC{beta})-dependent signaling, thereby suppressing sensory transmission in ABLK neurons. Desiccation-induced increase in hemolymph osmolality activates VMA-AstC neurons to trigger Allatostatin C (AstC) release, which antagonizes Bero-mediated AstC-R2 signaling and promotes dry substrate avoidance. Together, our findings uncover a neural circuit and a molecular mechanism by which changes in internal physiological state dynamically switch GPCR signaling through the opposing actions of membrane-tethered and secreted ligands, thereby reshaping adaptive behavioral responses.

neuroscience↗

bioRxiv2026

Tonically active interneurons gate motor output in Drosophila larvae

Tonic inhibition and its transient removal are fundamental neural mechanisms for gating activity, suppressing out-of-phase excitation, and enhancing the temporal precision of behavior. However, their roles within locomotor central pattern generators (CPGs) remain poorly understood. Here, we identify A02l neurons--segmentally repeated excitatory interneurons in the Drosophila larval ventral nerve cord (VNC)--that regulate crawling behavior through tonic inhibition. A02l neurons exhibit high baseline activity and are transiently suppressed during motor output. They provide excitatory input to inhibitory premotor networks, enabling widespread silencing of motor neurons to maintain muscle relaxation. Optogenetic activation of A02l neurons reduced crawling frequency, whereas their inhibition induced transient whole-body contractions. Together, these findings reveal a cellular-resolution mechanism for tonic inhibition within the nerve cord and highlight its importance in gating motor output and maintaining motor quiescence.

neuroscience↗

bioRxiv2024

Modular organization of synapses within a neuromere for distinct axial locomotion in Drosophila larvae

The ability to generate diverse patterns of behavior is advantageous for animal survival. However, it is still unclear how interneurons in a single nervous system are organized to exhibit distinct motions by coordinating the same set of motor neurons. In this study, we analyze the populational dynamics of synaptic activity when fly larvae exhibit two distinct fictive locomotion, forward and backward waves. Based on neurotransmitter phenotypes, the hemi-neuromere is demarcated into ten domains. Calcium imaging analysis shows that one pair of the domains exhibits a consistent recruitment order in synaptic activity in forward and backward waves, while most other domains show the opposite orders in the distinct fictive locomotion. Connectomics-based mapping indicates that these two domains contain pre- and post-synaptic terminals of interneurons involved in motor control. These results suggest that the identified domains serve as a convergence region of forward and backward crawling programs.

neuroscience↗

bioRxiv2022

Synchronous multi-segmental activity between metachronal waves controls locomotion speed in Drosophila larvae

The ability to adjust the speed of locomotion is essential for survival. In limbed animals, the frequency of locomotion is modulated primarily by changing the duration of the stance phase. The underlying neural mechanisms of this selective modulation remain an open question. Here, we report a neural circuit controlling a similarly selective adjustment of locomotion frequency in Drosophila larvae. Drosophila larvae crawl using peristaltic waves of muscle contractions. We find that larvae adjust the frequency of locomotion mostly by varying the time between consecutive contraction waves, reminiscent of limbed locomotion. A specific set of muscles, the lateral transverse (LT) muscles, co-contract in all segments during this phase, the duration of which sets the duration of the interwave phase. We identify two types of GABAergic interneurons in the LT neural network, premotor neuron A26f and its presynaptic partner A31c, which exhibit segmentally synchronized activity and control locomotor frequency by setting the amplitude and duration of LT muscle contractions. Altogether, our results reveal an inhibitory central circuit that sets the frequency of locomotion by controlling the duration of the period in between peristaltic waves. Further analysis of the descending inputs onto this circuit will help understand the higher control of this selective modulation.

neuroscience↗

bioRxiv2022

A Drosophila larvae-inspired vacuum-actuated soft robot

Peristalsis is one of the most common locomotion patterns in limbless animals. This motion is generated by propagating muscular contraction and relaxation along the body axis. While the kinematics of peristalsis has been examined intensively, the kinetics and mechanical control of peristalsis remain unclear, partially due to the lack of suitable physical models to analyse the force and temporal control in soft-bodied animals locomotion. Here, based on a soft-bodied animal, Drosophila larvae, we proposed a vacuum-actuated soft robot replicating their crawling behaviour. The soft structure, made with hyperelastic silicon rubber, was designed to mimic the larval hydrostatic structure. To estimate the adequate range of pressures and time scales for control of the soft robots, a numerical simulation by the finite element method was conducted. Pulse-Width-Modulation (PWM) was used to generate time-series signals to control the vacuum pressure in each segment. Based on this control system, the soft robots could exhibit the peristaltic pattern resembling fly larval crawling. The soft robots reproduced two previous experimental results on fly larvae: slower crawling speed in backward crawling than in forward crawling, and the involvement of segmental contraction duration and intersegmental delay in crawling speed. Furthermore, the soft robot provided a novel prediction that the larger the contraction force, the faster the crawling speed. These observations indicate that the use of soft robots could serve to examine the kinetics and mechanical regulation of crawling behaviour in soft-bodied animals.

biophysics↗

bioRxiv2021

Extraction of bouton-like structures from neuropil calcium imaging data

The neuropil, the plexus of axons and dendrites, plays a critical role in operating the circuit processing of the nervous system. Revealing the spatiotemporal activity pattern within the neuropil would clarify how the information flows throughout the nervous system. However, calcium imaging to examine the circuit dynamics has mainly focused on the soma population due to their discrete distribution. The development of a methodology to analyze the calcium imaging data of a densely packed neuropil would provide us with new insights into the circuit dynamics. Here, we propose a new method to decompose calcium imaging data of the neuropil into populations of bouton-like synaptic structures with a standard desktop computer. To extract bouton-like structures from calcium imaging data, we introduced a new type of modularity, a widely used quality measure in graph theory, and optimized the clustering configuration by a simulated annealing algorithm, which is established in statistical physics. To assess this methods performance, we conducted calcium imaging of the neuropil of Drosophila larvae. Based on the obtained data, we established artificial neuropil imaging datasets. We applied the decomposition procedure to the artificial and experimental calcium imaging data and extracted individual bouton-like structures successfully. Based on the extracted spatiotemporal data, we analyzed the network structure of the central nervous system of fly larvae and found it was scale-free. These results demonstrate that neuropil calcium imaging and its decomposition could provide new insight into our understanding of neural processing.

neuroscience↗

bioRxiv2021

Regulation of coordinated muscular relaxation by a pattern-generating intersegmental circuit

Typical patterned movements in animals are achieved through combinations of contraction and delayed relaxation of groups of muscles. However, how intersegmentally coordinated patterns of muscular relaxation are regulated by the neural circuits remain poorly understood. Here, we identify Canon, a class of higher-order premotor interneurons, that regulates muscular relaxation during backward locomotion of Drosophila larvae. Canon neurons are cholinergic interneurons present in each abdominal neuromere and show wave-like activity during fictive backward locomotion. Optogenetic activation of Canon neurons induces relaxation of body wall muscles, whereas inhibition of these neurons disrupts timely muscle relaxation. Canon neurons provide excitatory outputs to inhibitory premotor interneurons. Canon neurons also connect with each other to form an intersegmental circuit and regulate their own wave-like activities. Thus, our results demonstrate how coordinated muscle relaxation can be realized by an intersegmental circuit that regulates its own patterned activity and sequentially terminates motor activities along the anterior-posterior axis.

neuroscience↗

bioRxiv2020

Interspecies variation of larval locomotion kinematics in the genus Drosophila and its relation to habitat temperature

Speed and trajectory of locomotion are characteristic traits of individual species. During evolution, locomotion kinematics is likely to have been tuned for survival in the habitats of each species. Although kinematics of locomotion is thought to be influenced by habitats, the quantitative relation between the kinematics and environmental factors has not been fully revealed. Here, we performed comparative analyses of larval locomotion in 11 Drosophila species. We found that larval locomotion kinematics are divergent among the species. The diversity is not correlated to the body length but is correlated instead to the habitat temperature of the species. Phylogenetic analyses using Bayesian inference suggest that the evolutionary rate of the kinematics is diverse among phylogenetic trees. The results of this study imply that the kinematics of larval locomotion has diverged in the evolutionary history of the genus Drosophila and evolved under the effects of the ambient temperature of habitats.

neuroscience↗

bioRxiv2020

A neuromechanical model and kinematic analyses for Drosophila larval crawling based on physical measurements

Animal locomotion requires dynamic interactions between neural circuits, muscles, and surrounding environments. In contrast to intensive studies on neural circuits, the neuromechanical basis for animal behaviour remains unclear due to the lack of information on the physical properties of animals. Here, we proposed an integrated neuromechanical model based on physical measurements by taking Drosophila larvae as a model of soft- bodied animals. The biomechanical parameters of fly larvae were measured by the stress- relaxation test. By optimizing parameters in the neural circuit, our neuromechanical model succeeded in quantitatively reproducing the kinematics of larval locomotion that were obtained experimentally. This model could reproduce the observation of optogenetic studies reported previously. The model predicted that peristaltic locomotion could be exhibited in a low friction condition. Analysis of floating larvae provided results consistent with this prediction. Furthermore, the model predicted a significant contribution of intersegmental connections in the central nervous system, which contrasts with a previous study. This hypothesis allowed us to make a testable prediction for the variability in intersegmental connection in sister species of the genus Drosophila. Our model based on physical measurement provides a new foundation to study locomotion in soft-bodied animals and soft robot engineering.

neuroscience↗

bioRxiv2020

Electrophysiological validation of premotor interneurons monosynaptically connected to the aCC motoneuron in the Drosophila larval CNS.

Mapping the wired connectivity of a nervous system is a prerequisite for full understanding of function. In this respect, such endeavours can be likened to genome sequencing projects. These projects similarly produce impressive amounts of data which, whilst a technical tour-de-force, remain under-utilised without validation. Validation of neuron synaptic connectivity requires electrophysiology which has the necessary temporal and spatial resolution to map synaptic connectivity. However, this technique is not common and requires extensive equipment and training to master, particularly when applied to the small CNS of the Drosophila larva. Thus, validation of connectivity in this CNS has been more reliant on behavioural analyses and, in particular, activity imaging using the calcium-sensor GCaMP. Whilst both techniques are powerful, they each have significant limitations for this purpose. Here we use electrophysiology to validate an array of driver lines reported to label specific premotor interneurons that the Drosophila connectome project suggests are monosynaptically connected to an identified motoneuron termed the anterior corner cell (aCC). Our results validate this proposition for four selected lines. Thus, in addition to validating the connectome with respect to these four premotor interneurons, our study highlights the need to functionally validate driver lines prior to use.

neuroscience↗

bioRxiv2018

System level analyses of motor-related neural activities in larval Drosophila

The way in which the central nervous system (CNS) governs animal movement is complex and difficult to solve solely by the analyses of muscle movement patterns. We tackle this problem by observing the activity of a large population of neurons in the CNS of larval Drosophila. We focused on two major behaviors of the larvae, forward and backward locomotion, and analyzed the neuronal activity related to these behaviors during fictive locomotion that spontaneously occurs in the isolated CNS. We expressed genetically-encoded calcium indicator, GCaMP, and a nuclear marker in all neurons and used digital scanned light-sheet microscopy to record neural activities in the entire ventral nerve cord at a fast frame rate. We developed image processing tools that automatically detect the cell position based on the nuclear staining and allocate the activity signals to each detected cell. We also applied a machine learning-based method that we developed recently to assign motor status in each time frame. Based on these methods, we find cells whose activity is biased to forward versus backward locomotion and vice versa. In particular, we identified a group of neurons near the boundary of subesophageal zone (SEZ) and thoracic neuromeres, which are strongly active during an early phase of backward but not forward fictive locomotion. Our experimental procedure and computational pipeline enable systematic identification of neurons to show characteristic motor activities in larval Drosophila.

neuroscience↗

bioRxiv2018

Data-driven analysis of motor activity implicated 5-HT2A neurons in backward locomotion of larval Drosophila

Rhythmic animal behaviors are regulated in part by neural circuits called the central pattern generators (CPGs). Classifying neural population activities correlated with body movements and identifying the associated component neurons are critical steps in understanding CPGs. Previous methods that classify neural dynamics obtained by dimension reduction algorithms often require manual optimization which could be laborious and preparation-specific. Here, we present a simpler and more flexible method that is based on the pre-trained convolutional neural network model VGG-16 and unsupervised learning, and successfully classifies the fictive motor patterns in Drosophila larvae under various imaging conditions. We also used voxel-wise correlation mapping to identify neurons associated with motor patterns. By applying these methods to neurons targeted by 5-HT2A-GAL4, which we generated by the CRISPR/Cas9-system, we identified two classes of interneurons, termed Seta and Leta, which are specifically active during backward but not forward fictive locomotion. Optogenetic activation of Seta and Leta neurons increased backward locomotion. Conversely, thermogenetic inhibition of 5-HT2A-GAL4 neurons or application of a 5-HT2 antagonist decreased backward locomotion induced by noxious light stimuli. This study establishes an accelerated pipeline for activity profiling and cell identification in larval Drosophila and implicates the serotonergic system in the modulation of backward locomotion.

neuroscience↗