{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/379461"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/379461","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Building and characterising the neuropeptidergic connectome of Caenorhabditis elegans","abstract":"Nervous systems consist of synaptically-wired neuronal circuits, each with distinct roles and patterns of activity. At the level of the entire organism, these circuits integrate into a broader architecture, which is crucial for proper function. This synaptic circuitry is further modulated by monoamines and neuropeptides, which primarily act through extrasynaptic volume transmission. Neuromodulation is critical to nervous system function in all species, yet little is known about the structure and function of extrasynaptic signalling networks at a whole-organism level. Here I present data and analysis for a whole-organism level map of neuromodulatory connections in C. elegans and the tools I developed to build and characterise it. Specifically, I focused on the abundant and complex but mostly unstudied neuropeptidergic connections. To build the map of neuropeptidergic connections I combined anatomical reconstructions of the C. elegans nervous system along with single neuron gene expression and biochemical data for ligand-receptor interactions. I then used graph theory and machine learning methods to characterise the resulting network. This network is defined by a high density of connections, a clear mesoscale structure, and a large presence of autocrine self-loops and signalling cascades. These features are novel findings for any network of its kind in any species. Furthermore, I also identified a group of previously little-studied neurons as specialised neuropeptidergic neurons and characterised two of them using microscopy techniques and behavioural experiments. I then put this network in the overall context of the nervous system structure and attempted to understand from a computational perspective how neuropeptides interact with other neuromodulatory connections and with the synapticallywired circuits. Finally, I evaluated how the neuropeptide network changes during development and across evolution in closely related species. This map of neuropeptidergic connections and its characterisation serve as a prototype for understanding how neuromodulatory signalling is organised and influences behaviour, while also offering insights into its evolution and developmental changes.","abstract_html":"Nervous systems consist of synaptically-wired neuronal circuits, each with distinct roles and patterns of activity. At the level of the entire organism, these circuits integrate into a broader architecture, which is crucial for proper function. This synaptic circuitry is further modulated by monoamines and neuropeptides, which primarily act through extrasynaptic volume transmission. Neuromodulation is critical to nervous system function in all species, yet little is known about the structure and function of extrasynaptic signalling networks at a whole-organism level. Here I present data and analysis for a whole-organism level map of neuromodulatory connections in C. elegans and the tools I developed to build and characterise it. Specifically, I focused on the abundant and complex but mostly unstudied neuropeptidergic connections. To build the map of neuropeptidergic connections I combined anatomical reconstructions of the C. elegans nervous system along with single neuron gene expression and biochemical data for ligand-receptor interactions. I then used graph theory and machine learning methods to characterise the resulting network. This network is defined by a high density of connections, a clear mesoscale structure, and a large presence of autocrine self-loops and signalling cascades. These features are novel findings for any network of its kind in any species. Furthermore, I also identified a group of previously little-studied neurons as specialised neuropeptidergic neurons and characterised two of them using microscopy techniques and behavioural experiments. I then put this network in the overall context of the nervous system structure and attempted to understand from a computational perspective how neuropeptides interact with other neuromodulatory connections and with the synapticallywired circuits. Finally, I evaluated how the neuropeptide network changes during development and across evolution in closely related species. 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