Sander Liessem

Neural circuits in the brain control high-level parameters of movement, including the initiation, speed, and direction of locomotion. How specific cell types are organized into circuits to compute these control signals and enable context-appropriate behavior remains unclear. Here, we identify central brain neurons in Drosophila that can initiate walking and exert graded control over walking speed. Connectome analyses position the neurons on top of a layered brain circuit, which recruits a specific population of descending neurons to control forward walking independently of turning and other parameters. As predicted by the connectome, the circuit enforces straight walking when activated unilaterally, and the activity of the central brain neurons represents a high-level walking drive. This drive is suppressed during flight but flexibly integrated with other control signals to enable complex movement sequences during obstacle negotiation. Together, our findings elucidate a dedicated brain circuit that enables efficient computation, selection, and integration of forward-walking signals for context-appropriate motor control.

Nervous systems can process information in serial or in parallel, trading off efficiency for flexibility and speed. How these network architectures are implemented across sensorimotor pathways to control behavior is unclear. We investigate this tradeoff directly in Drosophila by comparing neuronal circuits underlying landing and takeoff, behaviors transforming similar visual cues to whole-body motor output. Using a whole-CNS connectome, electrophysiology, and behavioral analysis, we reconstruct the complete feedforward pathway for landing, including visual feature detectors, a dedicated ensemble of descending neurons (DNs), and a core premotor circuit in the nerve cord. Comparison to the takeoff pathway reveals that, despite encoding the same sensory feature and engaging similar muscle groups, neuronal circuits controlling the two behaviors are separated at every sensorimotor level. Extending this analysis to the complete DN population reveals a blueprint for descending motor control: DNs across the behavioral space utilized by the fly are organized as a set of parallel, loosely-overlapping ensembles that form a continuum from command-like control, with individual DNs determining behavioral output, to population coding, with multiple DNs controlling behavior synergistically. Distinct combinations of sensory feature detectors differentially recruit DN ensembles to enable flexible, context-dependent behavioral control.

Summary: Animals need to fine-control the speed and direction of locomotion to navigate complex and dynamic environments. To achieve this, they integrate multimodal sensory inputs with their internal drive to constantly adjust their motor output. This integration involves the interplay of neuronal populations across different hierarchical levels along the sensorimotor axis – from sensory, central, and modulatory neurons in the brain to descending neurons and motor networks in the nerve cord. Here, we characterize two populations of neurons that control distinct aspects of walking on different hierarchical levels in Drosophila . First, we use in-vivo electrophysiological recordings to demonstrate that moonwalker descending neurons (MDN) integrate antennal touch to drive changes in walking direction from forward to backward. Second, we establish DopaMeander as an important component in the control of forward walking through a combination of optogenetic activation, silencing, connectomics, and in-vivo recordings. These dopaminergic modulatory neurons drive forward walking with increased turning, and the activity of individual neurons is correlated with ipsiversive turning. Hence, MDN and DopaMeander control opposite regimes of walking on different hierarchical levels. Computational models reveal that their activity predicts key parameters of spontaneous walking. Moreover, we find that both MDN and DopaMeander are gated out during flight. This suggests that neuronal populations across levels of control are modulated by the behavioral state to minimize cross-talk between motor programs.

Insulin plays a critical role in maintaining metabolic homeostasis. Since metabolic demands are highly dynamic, insulin release needs to be constantly adjusted. These adjustments are mediated by different pathways, most prominently the blood glucose level, but also by feedforward signals from motor circuits and different neuromodulatory systems. Here, we analyze how neuromodulatory inputs control the activity of the main source of insulin in Drosophila – a population of Insulin-Producing Cells (IPCs) located in the brain. IPCs are functionally analogous to mammalian pancreatic beta cells, but their location makes them accessible for in vivo recordings in intact animals. We characterized functional inputs to IPCs using single-nucleus RNA sequencing analysis, anatomical receptor expression mapping, connectomics, and an optogenetics-based ‘intrinsic pharmacology’ approach. Our results show that the IPC population expresses a variety of receptors for neuromodulators and classical neurotransmitters. Interestingly, IPCs exhibit heterogeneous receptor profiles, suggesting that the IPC population can be modulated differentially. This is supported by electrophysiological recordings from IPCs, which we performed while activating different populations of modulatory neurons. Our analysis revealed that some modulatory inputs have heterogeneous effects on the IPC activity, such that they inhibit one subset of IPCs, while exciting another. Monitoring calcium activity across the IPC population uncovered that these heterogeneous responses occur simultaneously. Certain neuromodulatory populations shifted the IPC population activity towards an excited state, while others shifted it towards inhibition. Taken together, we provide a comprehensive, multi-level analysis of neuromodulation in the insulinergic system of Drosophila.

Insulin plays a critical role in maintaining metabolic homeostasis. Since metabolic demands are highly dynamic, insulin release needs to be constantly adjusted. These adjustments are mediated by different pathways, most prominently the blood glucose level, but also by feedforward signals from motor circuits and different neuromodulatory systems. Here, we analyze how neuromodulatory inputs control the activity of the main source of insulin in Drosophila – a population of Insulin-Producing Cells (IPCs) located in the brain. IPCs are functionally analogous to mammalian pancreatic beta cells, but their location makes them accessible for in vivo recordings in intact animals. We characterized functional inputs to IPCs using single-nucleus RNA sequencing analysis, anatomical receptor expression mapping, connectomics, and an optogenetics-based ‘intrinsic pharmacology’ approach. Our results show that the IPC population expresses a variety of receptors for neuromodulators and classical neurotransmitters. Interestingly, IPCs exhibit heterogeneous receptor profiles, suggesting that the IPC population can be modulated differentially. This is supported by electrophysiological recordings from IPCs, which we performed while activating different populations of modulatory neurons. Our analysis revealed that some modulatory inputs have heterogeneous effects on the IPC activity, such that they inhibit one subset of IPCs, while exciting another. Monitoring calcium activity across the IPC population uncovered that these heterogeneous responses occur simultaneously. Certain neuromodulatory populations shifted the IPC population activity towards an excited state, while others shifted it towards inhibition. Taken together, we provide a comprehensive, multi-level analysis of neuromodulation in the insulinergic system of Drosophila .

Behavioral flexibility is an important cornerstone for the ecological success of animals. Social Cataglyphis nodus ants with their age‐related polyethism characterized by age‐related behavioral phenotypes represent a prime example for behavioral flexibility. We propose neuropeptides as powerful candidates for the flexible modulation of age‐related behavioral transitions in individual ants. As the neuropeptidome of C. nodus was unknown, we collected a comprehensive peptidomic data set obtained by transcriptome analysis of the ants’ central nervous system combined with brain extract analysis by Q‐Exactive Orbitrap mass spectrometry (MS) and direct tissue profiling of different regions of the brain by matrix‐assisted laser desorption/ionization time‐of‐flight (MALDI‐TOF) MS. In total, we identified 71 peptides with likely bioactive function, encoded on 49 neuropeptide‐, neuropeptide‐like, and protein hormone prepropeptide genes, including a novel neuropeptide‐like gene (fliktin). We next characterized the spatial distribution of a subset of peptides encoded on 16 precursor proteins with high resolution by MALDI MS imaging (MALDI MSI) on 14 µm brain sections. The accuracy of our MSI data were confirmed by matching the immunostaining patterns for tachykinins with MSI ion images from consecutive brain sections. Our data provide a solid framework for future research into spatially resolved qualitative and quantitative peptidomic changes associated with stage‐specific behavioral transitions and the functional role of neuropeptides in Cataglyphis ants. image

In the best studied cases (Aplysiafeeding, crustacean stomatogastric system), peptidergic modulation is mediated by large numbers of peptides. Furthermore, inAplysia, excitatory motor neurons release the peptides, obligatorily coupling target activation and modulator release. Vertebrate nervous systems typically contain about a hundred peptide modulators. These data have created a belief that modulation is, in general, complex. The stick insect leg is a well-studied locomotory model system, and the complete stick insect neuropeptide inventory was recently described. We used multiple techniques to comprehensively examine stick insect leg peptidergic modulation. Single-cell mass spectrometry (MS) and immunohistochemistry showed that myoinhibitory peptide (MIP) is the only neuronal (as opposed to hemolymph-borne) peptide modulator of all leg muscles. Leg muscle excitatory motor neurons contained no neuropeptides. Only the common inhibitor (CI) and dorsal unpaired median (DUM) neuron groups, each neuron of which innervates a group of functionally-related leg muscles, contained MIP. We described MIP transport to, and receptor presence in, one leg muscle, the extensor tibiae (ExtTi). MIP application reduced ExtTi slow fiber force and shortening by about half, increasing the muscle's ability to contract and relax rapidly. These data show neuromodulation does not need to be complex. Excitation and modulation do not need to be obligatorily coupled (Aplysiafeeding). Modulation does not need to involve large numbers of peptides, with the attendant possibility of combinatorial explosion (stomatogastric system). Modulation can be simple, mediated by dedicated regulatory neurons, each innervating a single group of functionally-related targets, and all using the same neuropeptide.SIGNIFICANCE STATEMENTVertebrate and invertebrate nervous systems contain large numbers (around a hundred in human brain) of peptide neurotransmitters. In prior work, neuropeptide modulation has been complex, either obligatorily coupling postsynaptic excitation and modulation, or large numbers of peptides modulating individual neural networks. The complete stick insect neuropeptide inventory was recently described. We comprehensively describe here peptidergic modulation in the stick insect leg. Surprisingly, out of the large number of potential peptide transmitters, only myoinhibitory peptide (MIP) was present in neurons innervating leg muscles. Furthermore, the peptide was present only in dedicated regulatory neurons, not in leg excitatory motor neurons. Peptidergic modulation can thus be simple, neither obligatorily coupling target activation and modulation nor involving so many peptides that combinatorial explosion can occur.

Mass spectrometry imaging (MSI) of neuropeptides has become a well-established method with the ability to combine spatially resolved information of immunohistochemistry with peptidomics information of mass spectrometric analysis. Several studies have conducted MSI of insect neural tissues, however; these studies did not detect a neuropeptide complement comparable to that obtained with conventional peptidomics. The aim of our study was to improve sample preparation so that MSI provides comprehensive and reproducible neuropeptidomics information. Using the cockroach retrocerebral complex, the presented protocol produces enhanced coverage of neuropeptides at 15 µm spatial resolution, confirmed by parallel analysis of tissue extracts using electrospray ionization MS. Altogether, more than 100 peptide signals from 15 neuropeptide precursor genes could be traced with high spatial resolution. In addition, MSI spectra confirmed differential prohormone processing and distinct neuropeptide-based compartmentalization of the retrocerebral complex. We believe that our workflow facilitates incorporation of MSI in neuroscience-related topics, including study of complex neuropeptide interactions within the CNS.