Neuronal circuits and behavior of Hydra in the context of the metaorganism

Neuronal circuits and behavior of Hydra in the context of the metaorganism Dissertation in fulfilment of the requirements for the degree Doctor rerum naturalium of the Faculty of Mathematics and Natural Sciences at the University of Kiel Submitted by Christoph Giez Department of Celland Developmental Biology Zoological Institute, Kiel University Kiel, 2023 1 First referee: Prof. Dr. Thomas Bosch Second referee: Prof. Dr. Hinrich Schulenburg Date of oral examination: December 15th, 2023 Signature: _________________________________________________ 2 3 Declaration I, Christoph Giez, declare that: Apart from my supervisor’s guidance the content and design of the thesis is all my own work. Specific aspects of my thesis were supported by colleagues; their contribution is specified in detail in the following section “Contribution of authors”. The thesis has not already been submitted neither partially nor wholly as part of a doctoral degree to another examining body. Apart from the included published papers no other part of the thesis has been published nor submitted for publishing; Furthermore, I declare that I have not yet attempted a doctoral degree. The thesis has been prepared subject to the Rules of Good Scientific Practice of the German Research Foundation (DFG). Signature: ______________________________________________________ 4 5 Acknowledgements Over the last four years, my work on the interaction of the nervous system of Hydra and the associated microbes have been a time I am very grateful for and very much enjoyed. However, it would have been not nearly as much fun without a large group of people supporting me along the way. Firstly, and most importantly, I want to express my gratitude to you, Thomas Bosch, for the guidance, support, trust, and freedom you provided as a supervisor. You always inspired me to go further and to stay curious. I also cannot thank you enough for the many opportunities you made possible during my development. I would like to express my gratitude to Karen Guillemin and Hinrich Schulenburg for the time they devoted to overseeing my progress and providing invaluable suggestions that significantly enhanced my work. Thank you to Tim Lachnit and Alexander Klimovich, for smoothing the way for me by providing the needed expertise and fruitful discussions. Alexander Klimovich for the help to develop the required tools, Tim Lachnit for the dedication to identify the microbial molecule. I also want to thank Andreas Tholey, Jan Leipert, Christian Treitz, Christoph Kaleta, Georgios Marinos, Karlis Moors, Jan Taubenheim, Jenny Uhl, Urska Repnik, and Marc Bramkamp for their help, expertise, and the different points of view on my project, leading to stimulating discussions. I would also like to thank all former and current members of the Bosch lab: Jay Bathia, Johana Fajardo Castro, Hanna Domin, Maria Franck, Yan Giencke, Jinru He, Mirjam Hecht, Eva-Maria Herbst, Anika Hintz, Alexander Klimovich, Tim Lachnit, Janina Lange, Benedikt Mortzfeld, Ornina Merza, Christopher Noack, Dijana Pavleska, Denis Pinkle, Kai Rathje, Ehsan Sakib, Benedict Staack, Jan Taubenheim, Laura Ulrich, Doris Willoweit-Ohl and Jörg Wittlieb for helping me along the way and making my journey more joyful. 6 Jörg Wittlieb without whom the project would have not been possible; EvaMaria Herbst for helping and supporting me since my start in the lab and always spreading joy. I also want to thank my bachelor and master students Denis Pinkle, Yan Giencke and Ehsan Sakib who I was lucky to supervise and support. Thank you for your trust in me and for helping me to decipher the interaction between neuronal populations and microbes. Thank you to Ute Jülly for helping me during challenging times, helping me to reflect and giving me support. Without the unwavering support and encouragement of my family, my parents, and my friends I would not have been able to accomplish this chapter of my life. Special thanks to you, Lena Peters, for taking the same journey, being an inspiration, and support. Thank you also to Janna Wülbern for taking your time and improving my thesis at the end. Finally, I would like to thank my wife Tabea for her emotional support, motivation, being immensely patient, making challenges appear simple and always believing in me. Without you I would have lost myself, you keep me grounded. 7 14 15 Summary How do spontaneous body wall contractions, eating behavior, the evolution of the nervous system and the microbiota fit together? In my thesis I show that all four are connected and form a feedback loop that is omnipresent in the animal kingdom and might shed new light on the evolution of the metaorganism. My major findings were: 1. A puzzling behavior within Hydras’ repertoire is the spontaneous contractions of its body walls. We combined experimental fluid dynamics analysis and mathematical modeling to shed light on this phenomenon. It turns out that these spontaneous body wall contractions play a functional role by enhancing the transport of chemical compounds to and from the tissue surfaces where symbiotic bacteria reside. Interestingly, reducing the frequency of these contractions is associated with alterations in the composition of the colonizing microbiota. 2. Further, we delved into the eating behavior of Hydra, investigating the interplay between neuronal circuits and the symbiotic microbiota. Multiple neuronal subpopulations work together to control eating behavior and integrate a microbial metabolite. This observation underscores how microbes can affect neuronal circuits and eating behavior in a communitydependent manner. 3. Additionally, hunger and satiety have been found to influence behavior. Our work revealed that two distinct neuronal populations, N3 and N4, are responsible for feeding-dependent behavioral changes in Hydra. The endodermal N4 population is essential for food intake and digestion, like the enteric nervous system, while the ectodermal N3 population influences and inhibits other motor-related behaviors, similar to the central nervous system. These intriguing findings highlight the evolution and complexity of even the simplest nervous systems. 16 In summary, research on Hydra illuminates the importance of spontaneous body wall contractions, eating behavior and microbes, and neuronal activity in response to feeding. Our data highlight that one major function of the nervous system is to control eating behavior and maintain a stable and beneficial microbiota which are in the end linked to each other via feedback loops. These discoveries provide valuable insights into the dynamics of host-microbe interactions and the functionality of rudimentary nervous systems. 17 Zusammenfassung Wie passen spontane Kontraktionen, Essverhalten, die Evolution des Nervensystems und Mikroben zusammen? In meiner Arbeit zeige ich, dass alle diese Elemente miteinander verbunden sind und diese Verbindung in allen Tieren zu finden ist und womit diese Erkenntnis möglicherweise ein neues Licht auf die Evolution des Metaorganismus und des Nervensystems wirft. Hier eine kurze Zusammenfassung der wichtigsten Erkenntnisse aus meiner Arbeit: 1. Um das Phänomen der Kontraktionen in Hydra zu verstehen, kombinierten wir experimentelle Fluiddynamikanalysen und mathematische Modellierungen. Dadurch konnten wir feststellen, dass diese spontanen Kontraktionen eine wichtige Rolle spielen im Transport von Stoffen zu und von der Körperoberfläche von Hydra. Interessanterweise sind Veränderungen in der Häufigkeit dieser Kontraktionen mit einer Veränderung in der Zusammensetzung der assoziierten Mikrobiota verbunden. 2. Des Weiteren haben wir das Essverhalten der Hydra untersucht und das Zusammenspiel zwischen neuronalen Schaltkreisen und der Mikrobiota erforscht. Wir stellten fest, dass mehrere neuronale Zellpopulationen zusammenarbeiten, um das Essverhalten zu steuern. Gleichzeitig wird in diesen Schaltkreis ein mikrobielles Molekül integriert. Die Integration ist abhängig von der Zusammensetzung der mikrobiellen Gemeinschaft, was zu Veränderungen im Verhalten führen kann. Diese Beobachtung unterstreicht, wie Mikroben neuronale Schaltkreise und Essverhalten in einer gemeinschaftsabhängigen Weise beeinflussen können. 3. Darüber hinaus haben wir festgestellt, dass zwei unterschiedliche neuronale Populationen, N3 und N4, für sättigungsabhängige Verhaltensänderungen in der Hydra verantwortlich sind. Die endodermale N4-Population ist entscheidend für die Nahrungsaufnahme und endodermalen Kontraktionen während der Verdauung, ähnlich dem enterischen Nervensystem, während die ektodermale N3-Population andere motorische Verhaltensweisen in einer sättigungsabhängigen 18 Weise moduliert, ähnlich dem zentralen Nervensystem. Diese faszinierende Entdeckung verdeutlicht die Evolution und Komplexität selbst einfacher Nervensysteme. Meine Arbeit zeigt, dass eine Hauptfunktion des Nervensystems darin besteht, das Essverhalten zu kontrollieren und eine stabile und nützliche Mikrobiota aufrechtzuerhalten, wobei beides über Rückkopplungsschleifen miteinander verbunden ist. Diese Erkenntnisse bieten wertvolle Einblicke in die Dynamik von Wechselwirkungen zwischen Wirt und Mikrobe sowie die Funktionalität und Evolution von Nervensystemen. 19 20 21 Introduction Animals evolved in a microbial world: The concept of a metaorganism Let me take you on a trip back to the origin of life on our planet earth. Picture a time when everything was just about chemical reactions. In this soup, suddenly, the basis of all life emerged in a beautiful spontaneous reaction (Harold Urey, 1952). One intriguing theory speculates that this pivotal moment was driven by the miraculous birth of self-replicating macromolecules. These humble beginnings set the stage for the rise of RNA in the so-called "RNA world" and eventually paving the way for the DNA, the genetic code as we know it (Cooper, 2000; Robertson and Joyce, 2012). Now let's fast forward to about 3.8 billion years ago, when the young planet earth was on the brink of turning mere billion years old. During this epoch, prokaryotes evolved as the first life form on earth (Cooper, 2000). From now on, these prokaryotes shaped the earth, thriving in the harshest of environments. Yet, it was not until another 1.8 billion years had passed that the first eukaryotic cell evolved in this prokaryotic world. In a mesmerizing twist, it has been proposed that the eukaryotic cell is an outcome of a bacterium living in an archaeon (Sagan, 1967). Another 1.2 billion years come and go, ushering in an era around 600 million years ago, when the first multicellular organism is belived to have emerged (Erwin et al., 2011; Grosberg and Strathmann, 2007; Zhu et al., 2016). Let us now reflect upon this magnificent story of life, where the first multicellular organisms evolved in a world solely shaped by microbial organisms and their molecules. It is a remarkable testament to the unbreakable connection that exists among every living being and the everlasting heritage of our earliest days. The whole trajectory of the metazoan evolution happened in the context of a microbial world (McFall-Ngai et al., 2013). There was never a time and environment without microbes or microbial products which caused microbes to have a crucial impact on the evolution of eukaryotes. It is highly probable that the organelles mitochondria and chloroplasts evolved as a result of an uptake of a microbial cell (Dyall et al., 2004; McFadden and Van Dooren, 2004). Further, in the emergence of the first form of multicellularity, bacterial metabolites are thought 22 to be the inducers of the transition from a unicellular to a multicellular lifestyle (Woznica et al., 2016). Continuing along the trajectory of the evolution of complex lifeforms, one can find a multitude of different degrees of symbiotic interactions (McFall-Ngai et al., 2013). This highlights that there is no organism without an associated microbial community and every organism can be seen as a metaorganism (Fig. 1) (Bosch and McFall-Ngai, 2011). Over the last two decades, the interactions within the metaorganism were investigated on a wide variety of model systems ranging from unicellular organisms, (such as choanoflagellates), over early branching metazoan (sponges and cnidarian) and invertebrates (Caenorhabditis elegans, Drosophila melanogaster) to vertebrates (zebrafish, mice, humans) highlighting the manifold impact of microbes on animals’ development, physiology and behavior (Bosch and McFall-Ngai, 2021; Carrier and Bosch, 2022; Fraune and Bosch, 2010). Figure 1. The metaorganism concept. Multicellular organisms are hosts to prokaryotes, eukaryotes, and viruses thereby forming a metaorganism which is embedded in a certain environment. (Modified from (Bosch and McFall-Ngai, 2011)) 23 Microbes and host development In invertebrates, microbial symbionts can have a major impact on developmental transitions, development per se, life history, and fitness. Many marine invertebrates use bacterial cues to decide where to settle and undergo metamorphosis such as the tubeworm Hydroides elegans (Hadfield, 2010; Shikuma et al., 2016). In a different instance, in the bobtail squid Euprymna scolopes, the colonization of the crypts by Vibrio fischeri leads to light-organ morphogenesis (Nyholm and McFall-Ngai, 2021). This includes cellular changes, epithelial swelling, and an increase in microvillar density. In Drosophila melanogaster the microbiota influences stem cell proliferation, tissue homeostasis, mating preferences and life span (Erkosar et al., 2013). Interestingly, in invertebrates, one can see the most intimate interactions and codependency between microbes and hosts. One such exceptional case can be observed in the aphid Acyrthosiphon pisum where a specialized cell, the bacteriocytes, houses the obligative endosymbiont Buchnera. Buchnera provides essential amino acids that compensate for the essential amino acid deficient diet enabling the host to grow and reproduce (Akman Gündüz and Douglas, 2008; Shigenobu et al., 2000). Such intimate and essential symbiosis occurs frequently in insects and can get as fascinating as the fungus-growing ants (Currie, 2003). All those examples highlight the importance of microbes and their manifold impact on invertebrate development and physiology. In vertebrates, microbial symbionts are required for the development and maturation of the intestinal epithelium, as well as for the immune and nervous system (John F Cryan et al., 2019; Sommer and Bäckhed, 2013). In zebrafish the gut microbiota promotes intestinal epithelia proliferation (Cheesman et al., 2011), maturation and enteroendocrine and goblet cell differentiation (Bates et al., 2006). Here, bacteria seem to interfere with the developmental pathway Notch thereby changing cell fates. The phenotypes and transcriptional response to the microbiota were found to be similar between germ-free (GF) zebrafish and mice which suggests a conserved response to the microbiota in vertebrates (Hooper et al., 2001; Rawls et al., 2004; Sharma and Schumacher, 1995; Uribe et al., 2009). Looking into immune system maturation in mammals, the impact of the microbiota has been shown in multiple studies and it already starts during early life (Gensollen et al., 2016; Hooper et al., 2012; Lee and Mazmanian, 2010). Another aspect of vertebrate physiology where microbes can play a crucial role 30 Host repertoire of receptors Discussing the ways of behavioral changes and the production of neuroactive molecules, leaves the question open of how the host is detecting the microbial signals. Interestingly, most hosts have developed a manifold of receptors that can sense and get activated by microbial metabolites. Those channels can be divided into ion channels, such as TRPVA1 (Dohnalová et al., 2022), and G-protein coupled receptors (GPCR) (Cohen et al., 2017; Colosimo et al., 2019). Within the GPCRs, many have not been annotated and are taxonomically restricted (Chen et al., 2019). In mammals there have been multiple in vitro screenings of GPCRs highlighting the ability to sense microbial metabolites in a host specific manner (Chen et al., 2019; Cohen et al., 2017; Colosimo et al., 2019). In Drosophila, a GPCR is proposed to sense the uracil of opportunistic pathobionts and provoke Figure 2. The gut microbiota influences similar behaviors across multiple model organisms. The gut microbiota produces metabolites which are detected over multiple signaling pathways. In the end they all reach the brain and affect the behavior of the host. Hyperactivity, social behavior, and eating behavior are modulated by microbes across multiple model systems. 31 chronic inflammation (Ha et al., 2009; Lee et al., 2013) and in addition different ion channel receptors can detect microbial metabolites (Depetris-Chauvin et al., 2017). In C. elegans, an ion channel from the family of acid sensing ion channels detects microbes in the gut (Rhoades et al., 2019). However, a systematic approach to identify GPCR and ion channels that can respond to microbial products is missing for most hosts. The identification and analysis of the potential to sense microbial products and their location might help to close the gap in knowledge about the communication between gut and brain. Bacterial effects on the nervous system The impact of microbes on the central nervous system seems to be manifold besides the impact on behavior: it can affect neuronal activity and neurogenesis across multiple hosts. In mice and zebrafish, the absence of microbes leads to changes in the anatomy of the nervous system. Such as a decrease in axon growth and a decrease in arborization of neurons (Bruckner et al., 2022; Vuong et al., 2020). These effects abrogated due to an early-life recolonization or exposure to microbial products. Bacterial metabolites can change the neuronal activity of the enteric nervous system as well as the central nervous system. In mice, the gut microbiota is necessary for the normal excitability of the gut sensory neurons (Mcvey Neufeld et al., 2013). Similarly, the presence of distinct bacteria and metabolites can activate the enteric nervous system in zebrafish (Ye et al., 2021). Bacterial cell membrane components, muropeptides, can reach the brain and reduce neuronal activity in mice (Gabanyi et al., 2022). The presence of bacterial metabolites is also able to activate the dorsal root ganglion and thereby induces neurochemical changes in the brain (Dohnalová et al., 2022). Interestingly, not much is known about the influence of bacteria on neuronal activity and anatomy in invertebrates although there are behavioral changes (Nagpal and Cryan, 2021). In Drosophila peptidoglycan is inhibiting the activity of a subset of octopaminergic neurons in the brain which control egg laying (Masuzzo et al., 2019). Nevertheless, given the different and prominent effects on behavior in invertebrates, changes in neuronal activity and neurogenesis are to be expected and worth pursuing further. This can give a better understanding of conserved principles across systems and elucidating new mechanisms. Going through the exciting findings about the importance of microbes on animal behavior, only in a few cases a deep mechanistic understanding has been 32 reached as highlighted above. Challenging enough, results tackling the impact of microbes on animal behavior and even work solely focusing on behavior showed a variety of inconsistencies between different working groups and over different model systems (Nagpal and Cryan, 2021; Richter et al., 2009). This shows that so far not all relevant factors are known or discovered. To increase complexity, the question that often remains unanswered is: why do microbes have an impact on behavior or why has the host adapted to bacterial signals in a certain way? Since the decoding of the nervous system is a tremendous challenge in itself, the inclusion of the effect of an associated microbial community seems unsolvable. The proposal outlined at the start suggests that finding synergies across the phylogenetic tree and new model organisms, involving simpler organisms with genetic accessibility, can clarify the complex interplay. Open questions: • How do animals integrate bacterial/microbial signals in neuronal circuits and respond almost immediately? (Chapter III) • How are microbial metabolites sensed and in which way is the information translated and integrated in distal tissues such as the brain? (Chapter III) • Are there universal mechanisms for how hosts detect and integrate bacterial metabolites? (Chapter III) • Why do microbial metabolites influence host behavior? (Chapter II) 33 Evolution of the nervous system The nervous system is one of the most diverse structures in the animal kingdom, both in its anatomy and in cell type diversity (Arendt, 2018; Bucher and Anderson, 2015; Martín-Durán and Hejnol, 2021; Squire et al., 2012). It comprises diffuse nerve nets (Cnidarian) to complex brains (Vertebrates) as well as nervous systems with just 302 neurons (C. elegans) and ones with billions of neuronal cells (Squire et al., 2012). The evolution of such diverse and complex structures is still an enigma and multiple works draw different scenarios of how neurons and complex structures emerged (Arendt, 2020, 2018; Bucher and Anderson, 2015; Martín-Durán and Hejnol, 2021). Before starting this chapter, we need to ask: what defines a neuron as a neuron? The features which one would assign first are that (1) a neuron possesses long processes (axons, dendrites, neurites), (2) it can generate and/or transmit action potentials, (3) it has an input (from sensory neuron or other stimuli), and (4) an output via synaptic structures to another cell (neuron, muscle etc.) (Kristan, 2016). However, not every neuron has all those features. There are neurons which have no processes or do not use action potentials (Kristan, 2016). Thus, the key characteristics all neurons have in common are that they can communicate via specialized synaptic connections (Bucher and Anderson, 2015; Hobert et al., 2010). The evolution of the cell type neuron is still discussed, and it is not clear if neurons evolved only once or multiple times independently (Moroz et al., 2014; Moroz and Kohn, 2016; Ryan et al., 2013). However, since the phylogeny of the cell type neuron is not clear, here, the focus will be on the origin of some of the molecular neuronal features instead of elucidating the phylogenetic reconstruction. If we take the three common associated characteristics with neurons – action potential, neurotransmitter, and synaptic molecules, – and elaborate on their origin, one finds that those characteristics are not exclusively neuron and metazoan-specific but can be already found in unicellular organisms and prokaryotes. Action potentials are one of the key features of a neuron, but it is not restricted to neurons even in metazoans. In Cnidarian, epithelia cells can transmit and generate action potentials which allows for example nerve-free Hydra to still contract due to mechanical stimuli (Bosch et al., 2017; Tran et al., 2017). In sponges, certain species can use electrical signaling without the presence of a neuron (Leys and Anderson, 2015; Leys and Mackie, 1997). However, the first organisms to use electrical signaling to communicate are prokaryotes (Bavaharan and Skilbeck, 2022; Benarroch and Asally, 2020; Leys and Anderson, 2015; 34 Shimomura et al., 2020; Vien and DeCaen, 2016). They already possessed voltage-gated ion channels and use electrical signaling at a single cell and biofilm level (Leys and Anderson, 2015; Leys and Mackie, 1997). Classical neurotransmitters such as glutamate and GABA can be found in microbial communities as well as their receptors to detect those molecules (Dagorn et al., 2013; Kuner et al., 2003; Roshchina, 2016). For example, in Agrobacterium tumefaciens GABA may modulate the level of quorum sensing signal (Chevrot et al., 2006). Last, classical synaptic molecules such as Homer and SNAREs can be already found forming fine cytoplasmatic bridges in their colonial life stages in choanoflagellates (Burkhardt and Sprecher, 2017; Fairclough et al., 2013). In conclusion, many components of the neuronal molecular characteristics were invented by microbes to communicate between conspecifics. Reassembly and cell type specialization may have led to the evolution of the cell type neuron, suggesting that fundamental structures are still working in a similar way as in prokaryotes. This might have implications for understanding the prominent crosstalk between the nervous system and the microbiota. From diffuse nerve net to complex brain Since there are multiple theories on how the nervous system evolved, we will focus here on one which also translates to our findings in Chapter IV. The evolution of complex structures as the brain is still an unresolved question. The answer to this question would greatly improve our understanding of how the brain works, which would have far-reaching implications. One potential scenario is proposed by Arendt et al. 2015 who propose that the evolution of complex nervous systems started with the integration of distinct integration centers which are already found in the Cnidarian phylum (Arendt et al., 2015). The predictions are mainly drawn from a comparative developmental biological point of view but are also partially supported by functional data. The authors indicated that the apical nervous system (ANS) and blastoporal nervous system (BNS) underwent evolutionary changes to emerge as centers of integration situated on opposite sides of the body. The ANS is responsible for controlling the overall physiology of the organism, settlement, and locomotion, while the BNS coordinates feeding movements. The integration centers are defined by their location in the gastrulashaped ancestor (Arendt et al., 2015). In Bilaterian, the two centers merged and then formed the complex structures of the brain and the nerve chords (Fig. 3A). However, how exactly the transition from a rather diffuse structure in Cnidarian to 35 the Bilaterian brain happened is still highly speculative. To resolve this enigma, one needs to get a better understanding of the nervous systems prior to cephalization as they already can perform similar functions as the complex central nervous system in Bilaterian (Bosch et al., 2017). In Chapter IV, we approach this by deciphering the sub-functionalization of a simple nervous system. Hydra – a model system for neurobiology The freshwater Cnidarian Hydra vulgaris is a unique model system that provides the prospect to address all the questions raised in the previous sections and gain a mechanistic understanding. First, because of its phylogenetic position, the common ancestor of Cnidarian and Bilaterian is supposed to have evolved one of the first nervous systems by using the same building blocks which makes it suitable for investigating evolutionarily conserved traits (Fig. 3A) (Jékely et al., 2015; Pisani et al., 2015). Second, it has a simple body plan consisting of two epithelia layers – the ectoand the endoderm – which are separated by a matrix called mesoglea (Fig. 3B-C). Together they form the body column with a head and a basal foot (Fig. 3B). The lumen of the body column where the food is digested is also known as the gastric cavity. Third, it is colonized with a simple microbiota that sits on the outside in a mucus layer similar to the mucus structures in the gut of vertebrates (Fig. 3C) (Fraune and Bosch, 2007; Schröder and Bosch, 2016a). Fourth and last, the nervous system is rather simple compared to other model systems but still able to control complex behaviors (Fig. 3D-J) (Han et al., 2018; Siebert et al., 2019). In this chapter, the model system Hydra vulgaris will be reviewed and the open question highlighted. Neuronal cell types The first accepted confirmation of neurons in Hydra was made in 1964 which led to several investigations on the structure of the nervous system in Hydra (Fig. 3D) (Burnett and Diehl, 1964). The nervous system of Hydra was commonly assumed to be a diffuse and primitive nervous system but already in 1978 eleven different morphological neurons were described (Epp and Tardent, 1978; Tardent and Weber, 1976). Forty-two years later, this cell-type diversity could be confirmed based on molecular signatures (Siebert et al., 2019). The first attempt to describe the diversity of neurons was solely based on the morphology of dissociated cells using maceration (David, 1973; Epp and Tardent, 1978; Tardent 36 and Weber, 1976). Based on the observations, two different multipolar cells (M1, M2), two symmetrical bipolars (B1, B2) and two unipolar cells (U1, U2) were found in the ectoand endoderm. Asymmetrical bipolars (B3-B7) were only found in the endoderm. The distribution of the neurons follows a U-shape where the highest densities were found in the head (including tentacles) and the foot (Bode et al., 1973). Overall, two main neuronal cell types were described: sensory neurons (bipolar and unipolar, Fig. 3D) and ganglion neurons (multipolar, Fig. 3D) (Tardent and Weber, 1976). Recently the diversity of neuronal cell types was revisited by investigating the molecular signatures of the different cell types in Hydra (Klimovich et al., 2020; Siebert et al., 2019). Based on this approach, nine distinct main neuronal populations were identified, while some could be divided further, which leads to twelve neuronal populations (Fig. 3E). From those populations, three were identified to be in the endoderm and the other nine populations were in the ectoderm. The distribution of neuronal cell types between ectoand endoderm is not in consistency with the work from 1978. However, both works highlight that on the morphological and molecular level the nervous system seems to be more complex than just a diffuse nerve net. Chemical and electrical synaptic structures The identification of different neuronal populations (either based on morphology or molecular data) raised the question of how they are connected or to epitheliomuscular cells. With the first ultrastructural evidence of neurons, multiple other studies followed investigating the synaptic structures (Lentz and Barrnett, 1965) as well as conducting molecular and genomic analysis (Chapman et al., 2010). On the molecular level, multiple gap junctions, innexins, were identified allowing electrical coupling between neurons and epithelia cells (Chapman et al., 2010). With the single cell data sets, neuronal populations were identified which can or are electrically coupled and which are not. All populations express innexins except for population N6 (Ec4A/B) and N7 (Ec2) while there is no such clear distinction seen with genes associated with chemical synapsis (Klimovich et al., 2020). However, electron microscopy revealed both chemical and electrical synapsis decades earlier, but here assigning a neuron to one of the different neuronal populations is highly speculative. Therefore, the body of work on the ultrastructure of Hydras nervous system is a highlight of all potential structures that can be found in neurons of Hydra but not a systematic approach to link molecular and structural data. 37 Investigation of the nervous system of Hydra on an ultrastructural level defined various modes of synaptic connections. One of the first studies identified polarized chemical synaptic structures in neurons that contain dense core vesicles filled with neuropeptides (Koizumi et al., 1989; Westfall et al., 1971). Those chemical connections can be found between neurons (sensory-sensory, sensory-ganglion, ganglion-ganglion), neuro-epitheliomuscular cells and neuronematocytes (Westfall and Kinnamon, 1984). The synaptic structures can be reciprocal synapsis (between sensory cells, sensory-ganglion and ganglionganglion) or two-way chemical synapsis (only between ganglion-ganglion cells and ganglion-sensory cells). Based on serial sectioning and tracing there were also either two-cell pathways, which are formed by direct sensory-nematocyte/- epitheliomuscular cells, and three-cell pathways where a ganglion cell is interposed between sensory and epitheliomuscular cells (Westfall and Kinnamon, 1984). In addition, ganglion cells can form axo-axo-epitheliomuscular synapsis. Interestingly, a high percentage of neurons (ganglion cells in particular) in the head and foot have stereo ciliary complexes (between 84% and 96%)(Kinnamon and Westfall, 1981; Westfall and Epp, 1985). Further, in the head ganglion cells are often found in groups since 64 neuronal clusters with more than three neurons were identified (5 neurons on average per cluster, maximum of eleven)(Kinnamon and Westfall, 1981). In summary synaptic connections in Hydra are characterized by dense core vesicles which often form reciprocal structures. Interestingly, while investigating the ultrastructural anatomy of Hydras neurons, multifunctional properties were identified based on cell structures (Westfall, 1973; Westfall and Kinnamon, 1978a). Sensory and ganglion cells seem to be multifunctional and were described as sensory-motor-interneuron. The ganglion neurons have a cilium, as a characteristic of a sensory cell, they both have contact with epitheliomuscular cells, a characteristic of a motor neuron, and they both have synaptic connections to other neurons, a characteristic of an interneuron (Kinnamon and Westfall, 1981; Westfall et al., 1991; Westfall and Kinnamon, 1978b). The sensory cells can have complicated synaptic connections with each other and the surrounding epitheliomuscular cells as they can reach a ratio of one epithelia cell to up to four sensory cells in the apical region of the head/hypostome (Westfall and Kinnamon, 1984). Most of the work mentioned focused on chemical synapsis, but adjacent electrical and chemical synapses were observed as well by the same neuron to an epitheliomuscular cells, multiple gap-junctions between neurons, and even neurons which have both chemical and 38 electrical synapsis (Westfall et al., 1980). All those features make the nervous system of Hydra an interesting neurobiology model system. To summarize, Hydra's neurons seem to be multifunctional since they have characteristics of sensory, inter-, and motor neurons in one neuronal cell and can have both electrical and chemical synapses in the same neuron, suggesting a highly flexible nervous system where a neuron can overtake multiple roles at once. However, the functional significance of the observed structures and arrangements is still not clear. The behavioral pattern in Hydra Even though Hydra has a rather simple body plan and no central nervous system structure, it can already perform complex behavioral patterns (Fig. 3J). The behavior of Hydra can be divided into spontaneous and stimulus-triggered movements which were initially documented by Trembley (1744) (Trembley, 1744). Spontaneous actions encompass contraction (Passano and McCullough, 1964) as well as locomotion including somersaulting and inchworming (Mackie, 2013). However, both behaviors can be triggered by light and mechanical stimuli such as pinching (L. M. Passano and McCullough, 1964; L M Passano and McCullough, 1964; Passano and McCullough, 1965, 1963, 1962). Contraction behavior can be triggered by light such as that in the dark/night the contraction frequency decreases and increases during light/day (Kanaya et al., 2019; Passano and McCullough, 1964). The starvation state as well as other behaviors (feeding behavior) can affect or inhibit contractions (Rushforth, 1965). In a state dependent manner, Hydra is moving via locomotion towards a light-source which contains the blue spectrum (400-450nm)(Kim and Robinson, 2023; Tardent et al., 1976). On the other hand, the classical stimulus evoked behavior is the feeding behavior in Hydra (Lenhoff, 1961; Loomis, 1955). Food-related stimuli elicit a stereotypical feeding behavior that consists of three distinctive stages: tentacle writhing, tentacle ball formation and mouth opening (Koizumi et al., 1983; Lenhoff, 1961; Loomis, 1955). This behavior is crucial to the survival of Hydra. Furthermore, feeding behavior can be robustly induced by small molecules such as glutathione and S-methyl-glutathione (GSH, more later) (Loomis, 1955). Apart from the relatively complex actions, Hydra also exhibits fewer complex movements in various body parts and magnitudes, such as flexion, autonomous tentacle motion, as well as radial and longitudinal contractions (Han et al., 2018). To show that a neuronal control underlies all those behaviors, nerve free Hydra 39 were used (i-cell lineage free). Work on nerve free Hydra revealed that all complex behaviors (contractions, locomotion, feeding) are controlled by the interstitial cell lineage and most likely by neurons (Campbell et al., 1976; Tran et al., 2017). In summary, Hydra exhibits an already complex repertoire of movements with different degrees of complexity despite the simple nervous system. Figure 3. Hydra as a model system for neuroscience. A. Phylogenetic tree which highlights the evolution of the nervous system and transition from a nerve net to a centralized nervous system (modified from (Bosch et al. 2017)). B. Hydra's body plan consisting of a head with tentacles, body column, and a foot. C. Schematic of the tissue organization and localization of the microbiota. D. Drawings of neurons isolated from Hydra by A. Burnett 1964, separating into sensory and ganglion morphotypes (Burnett and Diehl, 1964). E. Schematic drawing of all major neuronal cell populations identified by single-cell analysis (modified from (Klimovich et al., 2020)). F-I. Immunohistochemistry of Hydra visualizing different neuronal population at different body locations. J. The major behavioral pattern of Hydra. 46 (Hufnagel et al., 1985; Venturini, 1987). In addition, glutathione was binding to membrane fractions in a specific and non-replaceable manner (Grosvenor et al., 1992). However, even though there is accumulating evidence of a specific receptor, the location and molecular identity have not yet been identified. The main neurotransmitters involved in the feeding response are GABA and glutamate which have opposite effects. GABA prolongs the feeding behavior, in particular the mouth opening duration, by 25% via ionotropic GABAA receptors (Concas et al., 1998; Pierobon et al., 2004b, 1995). The response time to glutathione was not affected and animals responded as fast as the control. In addition, glycine, taurine, and β-alanine had a similar effect, but glycine rather works via glycine (GlyR) and NMDA receptors (Pierobon et al., 2001). In contrast, glutamate inhibits the mouth opening in a glutathione-to-glutamate ratio dependent way (Lenhoff, 1961). The same effect was also seen with AMPA and kainate whereas NMDA, a glutamate receptor agonist, only reduced the response duration and did not inhibit the behavior (Pierobon et al., 2004b, 2004a). Further examination was conducted to investigate classical neurotransmitters and their involvement in the regulation of feeding behavior. Based on their observation, dopamine increases the duration of the mouth opening whereas endocannabinoid, anandamide, accelerates the mouth closing (De Petrocellis et al., 1999; Venturini and Carolei, 1992). Nevertheless, the identification of the specific cellular subtype that detects and reacts to the neurotransmitter remains elusive in all of the aforementioned investigations. In summary, the feeding response is an essential behavior for the survival of organisms involving intricate movements instigated by both prey and glutathione, originating from endogenous or exogenous sources. The feeding response depends on the interstitial cell line, namely neurons, however, the underlying neural circuitry responsible for this phenomenon remains elusive, as does the receptor that recognizes glutathione. The modulation and inhibition of the feeding response can be attributed to classical neurotransmitters such as GABA and glutamate. 47 Hydra – a simple metaorganism The microbiota of Hydra consists of a rather simple community with a few dominant bacterial species which sits on the outside in a mucus-like structure, glycocalyx (Bosch, 2014, 2013; Fraune and Bosch, 2007). Those features make it a great system for investigating host-microbe interactions. On one side because of the simplicity of the bacterial community. On the other side because of the easy accessibility and possibility to manipulate. The microbiota of Hydra is shaped predominantly by environmental factors and to a lesser extent by host factors. Nevertheless, there are still similarities between laboratory animals and wild-caught animals as well as differences between different Hydra species. Therefore, Hydra vulgaris and Hydra oligactis have a distinct microbial composition which is mainly based on the different abundance of β-proteobacteria and α-proteobacteria (Fraune and Bosch, 2007). Hydra vulgaris’ main class of bacteria are β-proteobacteria (dominant family: Burkholderiaceae) whereas Hydra oligactis’ main class of bacteria are αproteobacteria (dominant order: Rickettsiales). Expanding the number of Hydra species highlighted that the differences still hold partially true (Franzenburg et al., 2013b). Interestingly, Hydra vulgaris AEP rather clusters together with H. carnea and H. magnipapillata instead of Hydra vulgaris. However, during the analysis of the bacterial communities, reads of a Spirochaetia (Turneriella parva) were removed from Hydra vulgaris AEP which potentially introduced a bias in the analysis and interpretation (Franzenburg et al., 2013b). Further, in later studies the dominant order Rickettsiales of H. oligactis was lost, suggesting a more flexible community composition than initially suggested (Mortzfeld et al., 2018). In a more recent study, environmental factors were shown to drive the diversity of the host associated communities more strongly than host factors (Taubenheim et al., 2022). Here, different Hydra populations in the wild were investigated under different environmental conditions (Taubenheim et al., 2022). In conclusion, similar bacteria can be detected in association with Hydra vulgaris such as Curvibacter but the abundance and the diversity of the community can vary depending on the environmental situation. Hydra controls the associated microbial community via neurons and by using the conserved Toll-like receptor and MyD88 signaling pathway as well as antimicrobial peptides and potentially other innate immune pathways (Bosch, 2014; Klimovich and Bosch, 2018). Single-cell analysis revealed that neurons of 48 Hydra express many genes associated with the innate immune system which suggests a role in detection and interaction with the microbiota (Klimovich et al., 2020). Among the many genes were TLR/MyD88 associated genes as well different antimicrobial peptides expressed in neurons. Interestingly, many of those observations have already been experimentally proven before the availability of the single cell atlas. For MyD88, it has been shown that Toll-like receptors (TLR) and MyD88 play a mild role in regulating a species-specific recolonization (Franzenburg et al., 2012). In MyD88 knock-down animals many taxonomic restricted genes are (Hydra specific without annotation) differentially expressed and the recolonization by bacteria is delayed (Franzenburg et al., 2012). Furthermore, the role of neurons was elegantly shown by following interstitial stem cell lineage free (nerve free) animals over time and analyzing the microbial community (Fraune et al., 2009). The interstitial stem cell lineage was removed by a heat shock which allows the ablation of the interstitial stem cell in the naturally occurring mutant H. magnipapillata sf1. In the absence of neurons and gland cells, the microbial community changed significantly by a reduced abundance of β-Proteobacteria (Rhodoferax) and an increased abundance of Bacteriodetes (Fraune et al., 2009). Further evidence for the role of neurons has been given by showing that a neuropeptide had antimicrobial properties against the main colonizer Curvibacter (Augustin et al., 2017). The antimicrobial peptide, NDA-1, was expressed specifically in neurons in the head, body, and foot while missing in the tentacles. Interestingly, the abundance of Curvibacter in the tentacles is found to be 10-fold higher in comparison to the other regions of the body, thereby raising a question as to whether neurons are accountable for the spatial distribution of bacteria (Augustin et al., 2017). In NDA-1 knock-down animals the abundance of Curvibacter increased 2-fold in the body and foot region. To summarize, the neural regulation and utilization of Toll-like receptor and MyD88 signaling pathway, along with antimicrobial peptides and other innate immune pathways, play a crucial role in governing the microbial community associated with Hydra. Community assembly in Hydra is based on host factors, such as quorum quenching and antimicrobial peptides which lead to frequency-dependent interactions. Starting from the embryogenesis over to hatchling till the adulthood of Hydra the colonization pattern highlighted an interplay between host-derived factors, the environment, and frequency dependent interactions (Franzenburg et al., 2013a). Among the host factors, it has been shown that Hydra can modulate 49 the behavior of its bacterial symbionts by modifying quorum sensing molecules using an oxidoreductase leading to increased colonization (Pietschke et al., 2017). Furthermore, antimicrobial peptides such as arminins or other antimicrobial peptides controlled by the transcription factor Foxo, affect the reassembly of the species-specific microbiota (Franzenburg et al., 2013b; Mortzfeld et al., 2018). In addition, the community stability in Hydra depends on bacteria-bacteria interactions and host factors. Curvibacter domination of the community and its coexistence with Duganella is only possible because of the host (Deines et al., 2020). Duganella is outcompeting Curvibacter when the factor host is removed from the equation. In conclusion, the model system Hydra provides a unique playground to study community dynamics while considering host, environment, and bacteria-bacteria interactions. The fact that Hydra has an associated microbial community which is rather simple is well established but what are the benefits of having the symbionts and why does Hydra invest in regulating the community. On one hand, one of the most important traits is the protection against other pathogenic microbes such as the fungi Fusarium sp. The bacterial community protects against the fungal infection in a community dependent manner and not based on the trait of a single bacterium (Fraune et al., 2015). Another example is the tissue disturbance by the interaction of two bacteria, a Spirochaetia and a Pseudomonas, which only occurs when both are present (Rathje et al., 2020). On the other hand, bacteria can have modulatory effects on the behavior and development of the host. The frequency of the spontaneous body contraction behavior of Hydra is reduced to 60% compared to the control when there are no bacteria (Murillo-Rincon et al., 2017). The effect can be partially reversed by recolonization with the bacterial community, indicating either that part of the effect is due to a failed community assembly or another unknown factor. Another work shows that bacteria can influence taxonomically restricted genes which further affect Wnt-signaling and lead to a softening of the head regulation (Taubenheim et al., 2020). Overall, the mentioned examples highlight the modulatory effects and importance of the associated microbial community on the well-being of Hydra. Hydra with its long history of behavioral and neurobiological work as well as its associated natural microbiota offer a unique opportunity to address questions about the conserved ways of interaction between neurons and microbes. The well described behavioral pattern such as the feeding response, the insights in 50 neurotransmitter modulation paired with the new molecular data sets and the development of new methods on host side as well as on symbiont side make the time perfect to dive into mechanisms underlying the nervous system and the interplay with the environment. Hydra as a chance for a mechanistic understanding Metazoan evolution unfolded amidst a microbial world, profoundly shaping eukaryotic development. Symbiotic interactions extended across organisms, modulating developmental trajectories, enhancing fitness, and influencing behavior. Metazoan evolution and microbial dynamics are interwoven, yielding intricate symbiotic connections that continue to captivate both evolutionary and microbiological exploration. Nevertheless, while the field is nearly exploding in publications, many papers overinterpret their results while failing to resolve the underlying mechanism of the interaction. One such field is the study of the interaction between the nervous system and microbes and the impact of the microbial molecules on behavior and the central nervous system. Even though the field has made major advancements in the last decade, many fundamental questions have not been solved yet. Among those questions are: how do animals integrate microbial signals into neuronal circuits and respond almost immediately? How are microbial signals sensed and translated or transported to distal tissues such as the brain? Are there universal principles of mechanisms of how hosts detect and integrate microbial molecules? To explore such fundamental questions, history has shown that doing comparative studies among a high variety of model organisms yield an understanding of groundbreaking mechanisms (Bosch et al., 2017). Especially in neuroscience, the exploration of organisms with simpler nervous systems enabled insights into the workings of a brain and opened an emerging field looking into the evolution of the nervous system (Arendt, 2018; Arendt et al., 2015; Bosch et al., 2017; Dupre and Yuste, 2017; Weissbourd et al., 2021). Taking the simplicity of a nervous system of an early branching metazoan and also including the environment – such as the microbial world – might be a powerful approach to understand the fundamental functions of a nervous system and neuro-microbe interaction. Such an exemplary system is Hydra, which falls under the phylum of Cnidarian. It is proposed that this organism shares a common ancestor with Bilaterian, one of the earliest ones, which already possessed a neural network. Hydra already has a long history of work focusing on neurobiology and symbiotic 51 bacteria. However, in recent years, many new tools have been developed for exploring Hydras behavior and underlying neuronal circuits which currently makes it a promising field of research. However, despite all the work on the nervous system and the interaction of Hydra and the associated microbes, many questions remain unanswered. On one hand, the neuronal circuits of complex behaviors such as feeding behavior are unknown even though many predictions have been made (Pierobon, 2015, 2012). This is particularly interesting because here a coordination of movements has to be done without a central nervous system, which can give new insights into nervous system functions. On the other hand, neurons produce neuropeptides and are responsible for shaping the microbial community (Augustin et al., 2017; Franzenburg et al., 2013b; Fraune et al., 2009). However, there is no evidence that microbes influence neuronal activity directly and thereby behavior so far. In the work on spontaneous contractions, an effect of microbes on the behavior has been shown but the link to neuronal activity or neurons per se is missing (MurilloRincon et al., 2017). Furthermore, why bacteria stimulate spontaneous body contractions and create a dynamic environment has not been explained and is mind boggling. In addition, there is no knowledge if neurons do respond also on a transcriptional level to the presence of bacteria. All those questions raised, can be answered and further explored with the development of new methods for Hydra such as calcium imaging (Dupre and Yuste, 2017). 52 Objectives and overview of this thesis. In this thesis, I aim to explore the effect of symbiotic bacteria on the behavior of Hydra and the coordination of complex behaviors with a simple nervous system. I think this work will advance the understanding of conserved functions of the nervous system and bacteria-neuron interactions. The acquired insights will hopefully inspire to explore unsolved questions in more complex systems with a new perspective gained by this work. Chapter II aims to investigate which effect spontaneous body contractions have on the microbial community and if we can understand the “why” of this phenomenon. Chapter III will focus in a first step on the underlying neuronal circuitry of the eating/feeding behavior of Hydra. In the second step it will explore the impact of symbiotic bacteria on the identified circuit and decipher the underlying mechanism. Chapter IV will focus on the evolution of the enteric nervous and central nervous system and how they can be found in an organism without a brain. 53 Chapter I: Neurons interact with the microbiome: an evolutionaryinformed perspective. Neuroforum Review article Christoph Giez, Alexander Klimovich and Thomas C. G. Bosch* *Corresponding author: Thomas C. G. Bosch, Christian-Albrechts-Universität zu Kiel, Kiel, Germany, E-mail: [email protected]. Christoph Giez and Alexander Klimovich, Christian-Albrechts-Universität zu Kiel, Kiel, Germany, Email: [email protected] Apr 01, 2021 - DOI: https://doi.org/10.1515/nf-2021-0003 Abstract: Animals have evolved within the framework of microbes and are constantly exposed to diverse microbiota. Microbes colonize most, if not all, animal epithelia and influence the activity of many organs, including the nervous system. Therefore, any consideration on nervous system development and function in the absence of the recognition of microbes will be incomplete. Here, we review the current knowledge on the nervous systems of Hydra and its role in the host– microbiome communication. We show that recent advances in molecular and imaging methods are allowing a comprehensive understanding of the capacity of such a seemingly simple nervous system in the context of the metaorganism. We propose that the development, function and evolution of neural circuits must be considered in the context of host–microbe interactions and present Hydra as a strategic model system with great basic and translational relevance for neuroscience. Keywords: antimicrobial peptides; evolution; Hydra; metaorganism; nerve nets. 54 Introduction: neurons interact with the microbiome Nervous systems allow animals to perceive signals from the environment and to respond to them. Novel technologies including sequencing and imaging has unveiled that an important component of the immediate environment of many if not all organisms is a coevolved and resident microbiota (Baquero and Nombela, 2012; Blaser et al., 2016). Microbes shaped the Earth since billions of years before the “invention” of any nervous system, and they continue to shape the Earth. In animals, including humans, microbes are colonizing all epithelia. Animal evolution therefore appears intimately linked to the presence of microbes. Considering organisms as “metaorganisms” or “holobionts” (Bosch and McfallNgai, 2011, 2021) incorporates this impact of the microbial world and attempts to drive the neurosciences to the next level of enquiry. Previous studies on germ-free (GF) animals, i.e., organisms treated with broadspectrum antibiotics to completely eliminate their microbes or animals born and raised in absolutely axenic conditions, show that specific microbiota can impact central nervous system (CNS) physiology and neurochemistry (Sharon et al., 2016). GF mice that are devoid of associated microflora exhibit neurological deficiencies in learning, memory, recognition, and emotional behaviours (Foster et al., 2017; Gareau, 2014). In developing mice embryos, proliferation of neurons in the dorsal hippocampus is greater in GF mice than in conventionalized mice. However, post-weaning exposure of GF mice to microbial clones did not influence neurogenesis, suggesting that neuronal growth is stimulated by microbiota at an early stage (Ogbonnaya et al., 2015). It is now well established that microbiota not only affect the CNS but also influence the enteric nervous system (Cryan et al., 2019). For example, mice kept under sterile conditions show reduced excitability of enteric neurons, resulting in slower gut peristalsis and protracted intestinal transit time. Interestingly, colonization of adult GF mice with microbes taken from the intestine of animals kept under standard laboratory conditions restores the peristaltic activity to normal levels (De Vadder et al., 2018; Obata et al., 2020), indicating that the gut monitors continuously the contents of the lumen and responds to potential changes. It also has been shown that intestinal microbiota directly affects transcriptional programs 55 in enteric neurons (Obata et al., 2020). These observations are medically relevant because changes in the composition of microbiota (known as dysbiosis) are also observed in common gastrointestinal disorders, including those characterized by changes in intestinal motility, such as irritable bowel syndrome (De Palma et al., 2017). Most if not all of these studies were done in laboratory mice. How relevant are they for our understanding of neurobiology in general? From our evolutionary point of view, the discovery of an interaction of microbes with the mouse nervous system(s) comes as no surprise. Below we show that similar interactions between the microbiota and the neurons are in place already at the beginning of animal evolution, suggesting that animal–bacteria interactions are likely as ancient as animals themselves. Hydra, a model to study neuron– microbiome interactions Hydra, a member of the animal phylum Cnidaria, is close to the earliest animals in evolution that had nervous systems (Figures 1, 2A and B). Cnidaria occupy a Figure 1: The nervous system of Hydra is a diffuse nerve net. Here, a population of neurons expressing a Hydra-specific Hym355 neuropeptide (green) and muscular fibers of epithelial cells (magenta) are visualized in a juvenile polyp. 62 (Figure 2F and G). When we blocked the activity of these genes in Hydra, this immediately led to a drastic reduction in rhythmic body contractions. Modulation of the activity of these “pacemaker” channels disturbed both the rhythm and the frequency of the spontaneous contractions of the Hydra body, indicating that they depend on the unique combination of ion channels. For this reason, we are convinced that these neurons are indeed the pacemaker cells that control the peristalsis; and that they are able to perceive signals from microorganisms and react to them. Interestingly, the human orthologs of these channels are expressed by the intestinal pacemaker cells in mammals (first identified by Ramón y Cajal and called interstitial cells of Cajal) and linked to the pathogenesis of irritable bowel syndrome (Beyder et al., 2014; Mazzone et al., 2019; Strege et al., 2018). This evolutionary connection can be stretched even further since the unique molecular architecture of pacemakers appears to be conserved between Hydra neurons, the pharyngeal pacemaker complex of Caenorhabditis elegans and the above mentioned enteric nervous system of the mouse. The peristaltic activity of the gut turns out as an evolutionarily ancient neurogenic behaviour dependent on microbial signals and essential for life. Bidirectional communication between pacemaker neurons and the symbiotic bacteria Our studies uncovered that Hydra neurons not only receive signals from the microbiome, but also actively affect the composition of the associated microbiota. A detailed molecular genetic analysis of Hydra’s individual nerve cells using single cell RNA sequencing technology showed (Klimovich et al., 2020) that distinct subpopulations of neurons exert a direct influence on the density and composition of the symbiotic bacteria using the tools of the innate immune system. Distinct neuronal types, including the pacemakers, produce neuropeptides that display highly selective antimicrobial activity and alter the composition and spatial distribution of the microbial communities on Hydra body (Augustin et al., 2017; Klimovich et al., 2020). In addition, neurons in Hydra produce many components of microbe associated molecular pattern (MAMP) receptors, such as Toll-like receptors, NOD-like receptors, C-type lectin, etc., indicating that neurons in Hydra are immunocompetent cells with critical roles in immune signaling function (Klimovich et al., 2020). Emphasizing further the role of Hydra neurons in immunity, bioinformatics and machine learning algorithms revealed that a large fraction of neuronal genes unique to this genus (so called taxonomically restricted 63 genes), are capable of encoding antimicrobial peptides (Klimovich et al., 2020). To our surprise, we uncovered that a number of Hydra-specific neuropeptides known to mediate neurotransmission and motor control have a second function; they act as antimicrobial peptides and shape the microbiome (Augustin et al., 2017). Intriguingly, a bidirectional interaction between neurons and microbes can also be observed in vertebrates. While defensin family AMPs are expressed in the murine enteric neurons (Klimovich et al., 2020), a plethora of other peptides, produced in the mammalian brain, may also play a role in controlling resident beneficial microbes (Holzer and Farzi, 2014). Moreover, similar to dual-function neuropeptides of Hydra, a neuropeptide PACAP known to regulate neurodevelopment, emotion and stress responses in the mammalian brain has been recently identified as an antimicrobial peptide (Lee et al., 2021). Strikingly, Figure 3: The nerve net of Hydra is composed of at least seven distinct spatially restricted neuronal populations. Here, one of them – a population of neurons expressing the RF-amide neuropeptide (green) in the hypostome of a polyp are visualized using specific antibodies. Muscular fibers of epithelial cells (magenta) are counterstained with Phalloidin. 64 antimicrobial peptides have structural features that make them prone to aggregation into plaques similar to those characteristics for the amyloids in the brain (Lee et al., 2020). Even more intriguingly, the β-amyloid protein also has antimicrobial potential and may normally function in the innate immune system (Soscia et al., 2010). These observations provide an exciting perspective that the accumulation of amyloid, considered a toxic waste product, may in fact be an immune reaction of the brain to the presence of microbes or their products (Abbott, 2020). Taken together, these observations uncover the existence of a common evolutionary conserved principle and support an emerging paradigm that the communication between the nervous system(s) and the microbiota are indeed bidirectional. The nervous system receives signals from the gut (gut–brain axis) affecting host behaviour and development; and on the other hand, is producing neuropeptides with antimicrobial activity and a possible role in controlling the microbiota. Conclusions: Open questions, and future perspectives: a new way of exploring neuronal circuits. Here we have reviewed that nerve cells are involved in controlling resident beneficial microbes in the early emerging metazoan Hydra, and that microbes affect the animal’s behaviour by directly interfering with neuronal receptors. Recent progress in molecular and imaging analysis allows us to present Hydra as a powerful system for studying neural interactions and neural circuit formation which allows easy access to combined genetic, cell biological, molecular, and biocomputational tools. It is increasingly evident that bidirectional interactions exist in many vertebrates among the gastrointestinal tract, the intestinal microbiota and the enteric and central nervous systems. The path taken so far enables us to address specific and evolutionary informative questions with regard to the evolutionary origin of host neuron–microbe interactions. Open questions include: − How do microbes affect innate behaviour such as Hydra’s feeding reflex? − What are the microbial taxa involved and the responsive neuron populations? − How different are these signals and factors closely related but different Hydra species? Preliminary observations point to a surprising difference in 65 neuroanatomy in closely related and apparently similar Hydra species; yet functional consequences of these differences remain unclear. − Does the resident microbiota influence neurogenesis in embryos and/or in adults? − Is the resident microbiota involved in educating neuronal precursor cells/stem cells which, in turn, influence the composition of the microbiota? − How do the commensal microbiota support the development of the complex nerve net made of distinct spatially restricted neuronal populations (Figures 3 and 4)? − Ample histochemical, biochemical and functional data has been accumulated, indicating the presence of different small molecule neurotransmitters such as catecholamines, serotonin, acetylcholine, glutamate and GABA in Hydra. Do the resident microbes contribute to the repertoire of neurotransmitters? − Last: what are the fundamental principles of the nerve net topology, dynamics and function that allow the simple nervous system of Hydra to be highly effective, multifunctional and energy-efficient? Understanding these basic rules of network design, implemented in the simple nervous systems of Hydra and other models, will be instrumental for development of highly efficient yet less energy-demanding microprocessors and computers (Bosch et al., 2017; Dupre and Yuste, 2017; Martinez and Sprecher, 2020). Some of these questions are already under study in laboratories around the globe, but a more focused effort is required. The fascinating perspective is that these efforts will assist our understanding of how all of the parts of a living organism operate together within the metaorganismic framework. The comprehensive elucidation of the neural code for behavior in an experimental system where one can have in principle access to either connectivity or functional data from every single neuron also enables the rigorous modeling of neural circuits, with simulations that are constrained completely in terms of the number of neurons, connections between the neurons and activity patterns. Model systems such as Hydra therefore may open new pathways to mimic basal neuronal mechanisms by electronic systems. Such studies will have relevance for understanding neural circuits in all animal species. In conclusion, the evidence is now irrefutable that “from so simple a beginning” (Charles Darwin) the neurobiology of animals has been, and is being, shaped by interactions with the microbial world. 66 Glossary Antimicrobial peptides (AMPs): Small molecular mass proteins with broad spectrum antimicrobial activity against bacteria, viruses and fungi. These peptides are usually positively charged and have both a hydrophobic and hydrophilic side that enable the molecule to be soluble in aqueous environments yet also enter lipid-rich membranes. Figure 4: Modern microscopy technologies allow analysing the complex anatomy of the Hydra nerve net with unprecedented resolution. Here, the cell bodies and neurites of RF-amidepositive neurons in the hypostome (green) are visualized using a specific antibody. Cell nuclei are counterstained with TO-PRO (magenta). 67 Axenic condition: Condition of animal culture, in which only a single species of organism is present and entirely free of all other contaminating organisms. This state is achieved by sterilizing the housing equipment, supplied food and air. Axenic culture is an essential tool for studies on symbiotic interactions in a controlled environment. Commensal microbe: A bacterial, viral, fungal or archaeal organism that under normal circumstances resides in or on host tissue, does not cause disease, and forms a symbiotic relationship with the host in which one derives some benefit, while the other is unaffected. Conventionalized host organisms: Carrying the full (undefined) load of organisms usually associated with this species. Dysbiosis: An altered state (or disbalance) of microbiota associated with a change in species composition, abundance and/or spatial distribution and typically associated with a disease. Germfree: Host organisms that are devoid of any other living germs or microorganisms. Holobiont: The cnidarian host organism and all of its symbiotic algae and stably associated microbiota. While the term “meta-organism” defines a superordinate entity that is applicable to all kinds of interdependent associations, the term “holobiont” is constrained to specific taxonomic groups. Metaorganism: An association composed of a uni-or multicellular macroscopic host and diverse microorganisms, including bacteria, Archaea, fungi, viruses, and various other microbial eukaryotic species including algal symbionts. Microbes: Microbial life forms including bacteria, archaea, fungi and viruses. Microbiota: Microbial life forms within a given habitat or host. Microbiome: The totality of microorganisms and their collective genetic material present in or on the body of a macroscopic host organism or in another environment. 68 Symbiotic interactions: A close and usually obligatory association between two or more different organisms of different species that live together, often but not necessarily to their mutual benefit. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission. Research funding: This work was supported in part by grants from the Deutsche Forschungsgemeinschaft, the CRC 1182 “Origin and Function of Metaorganisms”(to TCGB.) and funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 434434223 –SFB 1461 “Neurotronics: Bio-Inspired Information Pathways” (to TCGB and AK). T.C.G.B. appreciates support from the Canadian Institute for Advanced Research. Conflict of interest statement: The authors declare no conflicts of interest regarding this article. References Abbott, A. (2020). Are infections seeding some cases of Alzheimer’s disease? Nature 587,22–25. Anderson, D.J. and Perona, P. (2014). Toward a science of computational ethology. Neuron 84,18–31. Augustin, R., Schröder, K., Rincón, A.P.M., Fraune, S., Anton-Erxleben, F., Herbst,E.M., Wittlieb, J.,Schwentner, M.,Grötzinger, J.,Wassenaar, T.M., et al. (2017). A secreted antibacterial neuropeptide shapes the microbiome of Hydra.Nat. Commun. 8,1–8. 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Yamamoto, W. and Yuste, R. (2020). Whole-body imaging of neural and muscle activity during behavior in Hydra vulgaris: Effect of osmolarity on contraction bursts. enNeuro 7, ENEURO.0539¬19.2020, https://doi.org/10.1523/eneuro.0539-19.2020. 78 average last tens of minutes (Figure 2—figure supplement 3B), Hydra’s body column remains nearly fully extended and slowly revolves at full length around its foothold, tracing a cone-shaped volume over time (Figure 2B, Video 1). By contrast, spontaneous contraction events typically last a few seconds and are characterized by a stepwise shortening of the body column with one or more peak contraction events until the body column has shortened to 20% or less of its original length (Figure 2A–C). A peak contraction is defined as any part of the spontaneous contraction during which Hydra contracts at a rate of 25% change in body length per second or more. spontaneous contractions are frequently accompanied by axial rotation in a spiraling downward motion (Figure 2A and B, Video 1) and are typically followed by slow re-extension to inter-contraction interval length (Figure 2A) in a random direction (Figure 2— figure supplement 4). We quantified the speeds and timescales that different regions of Hydra’s body column experience during inter-contraction intervals and spontaneous contractions. During spontaneous contractions, specifically during Figure 2. Kinematic and fluid dynamics analysis of individual contraction events reveal the shedding of the fluid boundary layer (FBL) during each spontaneous contraction event. (A) Top: representative plot of the change in relative body length in Hydra as a function of time shows transition from an inter-contraction interval (IC) to a spontaneous contraction with two peak contraction events (PC, asterisks), and the return to an IC interval. Bottom: typical kinematic pattern associated with IC intervals and contractions. Arrows indicate distinct body trajectories during IC intervals (blue) compared to contractions (red). (B) Typical body trajectories in longitudinal and axial plane during IC intervals and spontaneous contractions visualized by timelapse microscopy. Maximal speeds (log10 scale) are indicated by color-coded trajectories of the head (oral end or tentacles). Trajectories are slightly offset to avoid obscuring the animal. Asterisks denote PC events. (C) Comparison of maximal velocities near head reached during IC intervals (n = 3 animals) and PC (n = 5 animals). Lines: average curves. Shaded areas: interquartile range. Inset: relative scaling of PC duration (TPC), IC interval duration (TIC), PC velocity magnitudes (UPC), and IC interval velocity magnitudes (UIC). (D) Application of a fluorescent dye reveals existence of a FBL during the IC interval and its shedding upon a contraction event. A representative time-lapse series. White arrows indicate FBL shedding after PCs, that is, the growing separation between the original, stained FBL and Hydra’s head. (E) Quantification of Hydra’s flow velocity field during IC intervals and (F) during a typical PC with axial rotation (top view). Flow vectors and velocities are indicated by color-coded arrows. (G) Relative change of fluid flow speed as a function of distance from Hydra’s surface measured along dotted lines in (E, F) during IC intervals (blue) and during PCs with rotation (red). Lines: average curves. Shaded areas: interquartile range. (H) FBL thickness, defined as distance from Hydra at which 90% freestream speed is reached, is inversely correlated to maximal flow speed (Umax). The online version of this article includes the following figure supplement(s) for figure 2: Figure supplement 1. The experimental setup for measuring and manipulating the contraction rates in Hydra. Figure supplement 2. Behavioral analysis of Hydra over 8 hr. Figure supplement 3. Spontaneous contractions are modeled mathematically as a Poisson process. Figure supplement 4. The 2D space covered by Hydra’s resting motion and re-extensions in the period of nine contractions is revealed by this overlay of detected motion. Figure supplement 5. Typical flow speed profiles derived from the particle imaging velocimetry (PIV) data. Figure supplement 6. Spontaneous contraction frequencies of Hydra when placed in small liquid volumes can reach up to 12 contractions per hour (CPH). 79 peak contractions characterized by simultaneous linear shortening and axial rotation, the oral region accelerates to peak velocities UPC of 10 mm/s. This is almost two orders of magnitude faster than maximal head speeds UIC during inter-contraction intervals, which are on the order of 0.1 mm/s in both longitudinal and axial directions (Figure 2B and C). Further, peak contraction rarely last longer than TPC = 1s and hence operate at 1000-fold smaller timescales than intercontraction intervals (with mean duration of ca. 24 min, i.e., 1440 s). To assess the effects of Hydra’s body kinematics on its fluid environment, we computed the Reynolds number Re = ℓU/µ at the tentacles near Hydra’s mouth, where the tentacle diameter is ℓ = 0.1 mm, the kinematic viscosity of water at 20°C is µ = 1.0034 mm2/s, and the tentacle speed U is in mm/s. The Re number compares the effects of fluid inertia to viscous drag forces. During intercontraction intervals, U = UIC is on the order of 0.1 mm/s, and Re is on the order of 0.01. At such low Re number, fluid flow is dominated by viscous effects and distinguished by the presence of a laminar and expansive fluid boundary layer. The fluid boundary layer can be envisioned as an envelope of fluid enclosing Hydra and moving at the same velocity as Hydra’s surface. The thickness of the fluid boundary layer is defined as the distance from the surface at which the fluid speed decreases by 90% compared to Hydra’s speed, that is, the distance at which the fluid is no longer following Hydra’s surface (Vogel, 2020). During peak contraction, Hydra’s speed U = UPC is on the order of 10 mm/s, Re increases to the order of 1, which indicates greater inertial effects that result in thinning of the fluid boundary layer (Schlichting and Gersten, 2000). To label the fluid boundary layer and assess the animal–fluid interactions experimentally (Nawroth and Dabiri, 2014), we added fluorescent dye adjacent to Hydra’s oral region while in the intercontraction interval state. In one representative recording (Figure 2D, left, Video 2), the dye faithfully followed the fully extended animal’s slow revolutions during the inter-contraction interval for more than 1 min, qualitatively confirming the presence of a stably attached fluid boundary layer. Intriguingly, as the animal underwent a spontaneous contraction (with two peak contraction events), the dye separated from the animal’s surface and stayed behind as Hydra retracted, indicating shedding of the original fluid boundary layer (Figure 2D, right, Video 2). Since the animals tend to slowly reextend into a random direction (Figure 2— figure supplement 4), their chances of re-encountering the shed fluid are small. To confirm these observations 80 quantitatively and to measure the dynamically changing fluid boundary layer thickness, we recorded the microscale fluid motion using particle imaging velocimetry (PIV) as described previously (Nawroth et al., 2017). During intercontraction intervals, fluid parcels at distances far from the body column followed the animal’s trajectory, reflecting an expansive fluid boundary layer of almost one full-body length in thickness (fluid boundary layer thickness = 5 mm) (Figure 2E and G, Figure 2—figure supplement 5A, Video 3). In contrast, during peak contractions, only the fluid close to Hydra’s surface accelerated with the body, whereas fluid at further distances remained unaffected (fluid boundary layer thickness = 1.5 mm) (Figure 2F and G, Figure 2—figure supplement 5B, Video 3). The thickness of the fluid boundary layer correlated inversely with body speed (Figure 2H). These measurements demonstrate shedding of the fluid boundary layer during contractions and significant reduction in the thickness of the fluid boundary layer that had developed during the inter-contraction interval. Thus, recurrent spontaneous contractions result in regular shedding of the fluid boundary layer (as illustrated by the streak of dye left behind in Figure 2D), enabling Hydra to partially escape from its previous fluid environment and thereby transiently reshape the chemical microenvironment at the epithelial surface where the microbial symbionts are localized. Importantly, the contraction events form a brief perturbation of the inter-contraction interval flow regime at the oral region, while the foot region remains motionless even during peak contractions (Figure 2B) and experiences slow flow speeds of maximal values on the order of 0.1 mm/s (Figure 2—figure supplement 5B). These flow speeds are comparable to flow speeds experienced at the head between contractions (Figure 2— figure supplement 5A). Taken together, these results suggest that Hydra’s spontaneous contractions lead to a maximal shedding of viscous boundary layers near the head’s surface, and minimal shedding near the foot’s surface. This differentiation in the fluid environment could be relevant because of our finding that Hydra’s surface is colonized by footand head-dominating microbiota (Figure 1D and E). Contraction-induced fluid boundary layer shedding may enhance the transport of bacteria-relevant compounds, such as metabolites, antimicrobials, and extracellular vesicles (Fischbach and Segre, 2016; Ñahui Palomino et al., 2021), between Hydra’s head and the fluid environment, compared to lower transport near the foot, hence generating biochemical microhabitats that could promote the observed microbial biogeography. This hypothesis is not readily amenable to experimental interrogation. To date, there are no universal tools for experimentally identifying individual and combinations of the many chemical compounds 81 released or absorbed by microbes (Thorn and Greenman, 2012). We therefore probed this hypothesis indirectly. We developed a simple mathematical model of chemical transport in a fluid environment that gets regularly reset by shedding events, as discussed next. Physics-based model predicts that contractions increase the exchange rate of chemical compounds to and from the surface We formulated a simple physics-based mathematical model of the transport of chemical compounds to and from Hydra’s surface where the microbes reside. We exploited two key findings from our experimental data. First, between contractions, transport of chemical compounds to and from Hydra’s surface is best described by molecular diffusion rather than fluid advection. Second, peak contractions are much shorter and faster than movement during inter-contraction intervals (TPC ≪ TIC and UPC ≫ UIC, see Figure 2C), and each peak contraction causes intermittent shedding of the fluid boundary layer and re-extension of Hydra’s head in a random direction. Thus, each contraction resets the fluid microenvironment and replenishes the chemical concentration around Hydra’s head, while the fluid environment at Hydra’s foot remains always at rest, akin to a permanent inter-contraction interval state. Our dye visualization and flow quantification showed no noticeable background flows between contraction events (Figure 2D). The fact that diffusion is dominant between contractions can be formally shown by computing the Péclet number Pe = LU/D, which compares the relative importance of advection versus diffusion for the transport of a given compound, such as oxygen. Using the length L of Hydra’s body column, the mean flow speed U over 1 hr (averaging over both intercontraction intervals and contracting periods), and the constant oxygen diffusion D at 15°C, we find that Pe is 0.005 ≪ 1, implying that diffusion is dominant between contraction events, even when the flow speed U is overestimated by averaging over both inter-contraction intervals and spontaneous contraction periods. To reflect the different fluid microenvironments in the highly motile head and static foot, we approximated the respective head and foot surfaces by two noninteracting spheres of radii a and b separated by a distance L (Figure 3A, box). When even a few percent of Hydra’s head or foot surface are covered by living 82 microbes or host cells, the diffusion-limited rate of absorption or emission of a chemical compound by these cells is well approximated by a uniformly covered surface (Berg and Purcell, 1977). Assuming equivalence of the transport of Figure 3. Mathematical model suggests that spontaneous contractions enhance mass transport to and from the surface. (A) Left, box: simplified animal geometry assumed in model consists of a sphere representing the head and a smaller sphere representing the basal foot. Right: by Fick’s law, a chemically depleted concentration boundary layer (CBL) forms around Hydra’s head and foot region through continuous uptake of chemical compounds at the surface during the intercontraction interval (IC). The CBL is shed from the head region, but not the foot region, through spontaneous contractions (PC). (B) Computationally modeled growth of chemically depleted CBL around Hydra’s head over time until steady state is reached (panel 4), assuming continuous uptake at surface and unlimited supply at far distance. (C) Top: growth of chemically depleted CBL as a function of time with and without contractions. Bottom: instantaneous and cumulative uptake rate, respectively, of a given chemical compound (here: oxygen) during the CBL dynamics above; (D) Predicted increase in cumulative uptake rate (as percentage of steady state) of a given chemical compound over 48 hr as a function of spontaneous contraction frequencies within the observed range (purple), and of increasing frequencies with unconstraint number of spontaneous contractions (magenta) compared to a constraint number of spontaneous contractions (blue graph). 83 emitted and absorbed chemical substances (see ‘Materials and methods’), we focus here on absorption only. This implies zero concentration of the chemical compound of interest, say oxygen, at Hydra’s surface. Starting in a compoundrich environment, a concentration boundary layer (CBL) depleted of that chemical compound forms and grows near the surface. In the absence of contractions, the concentration field reaches a steady state with zero rate of change of the compound concentration. Each spontaneous contraction event sheds the chemically depleted fluid boundary layer near the head and effectively resets the depletion zone growth process (Figure 3A). Accounting for unsteady diffusion following each spontaneous contraction, we computed the growth of the depletion boundary layer over time (stages 1–3 in Figure 3B), using as an example the diffusion coefficient D of oxygen to derive a dimensional timescale τ = a2/D (see ‘Materials and methods’). In the absence of fluid boundary layer shedding, such as near Hydra’s static foot, steady state is approached as time increases (stage 4 in Figure 3B). Near Hydra’s head, however, each spontaneous contraction resets the CBL thickness to zero (Figure 3C, top), resulting in an instantaneous and increased uptake of molecules, such as oxygen, in the head region (blue) compared to the foot (orange). Consecutive contractions increase the maximal cumulative uptake of compounds near Hydra’s head (blue) compared to the foot (orange) (Figure 3C, bottom). To investigate the functional implications of experimentally observed temporal distribution of spontaneous contractions over many hours, we combined our physics-based model of chemical transport (Figure 3A) with a stochastic model of contraction events. Specifically, the temporal sequence of Hydra’s spontaneous contractions can be estimated mathematically by a Poisson distribution of mean λ, given that the distribution of inter-contraction intervals are best fitted by an exponential distribution of mean 1/λ (Figure 2—figure supplement 3B). We calculated analytically the expected mean and standard deviation of the cumulative uptake JIC over a single inter-contraction period TIC and of the cumulative uptake J over an extended period T (containing multiple TIC) (see ‘Materials and methods’). We found that the expected mean value of JIC, given by ⟨JIC⟩ = √(a2λ/D), decreases with increasing contraction frequency λ, while the expected mean value of J, given by ⟨J⟩ = √(a2λ/D), increases with increasing λ. This is intuitive: as the contraction frequency increases, the inter-contraction time TIC decreases, so does the expected uptake JIC over a single inter-contraction period TIC. However, the fluid environment gets reset more often, with each 84 resetting event replenishing the chemical concentration, leading to an increase in the expected cumulative uptake J over an extended period T containing multiple contraction events. Next, we numerically simulated data sets of inter-contraction intervals TIC (Figure 2—figure supplement 3C) drawn from exponential distributions of mean 1/λ, where we let λ range from 0 to 10 at 0.05 intervals. For each λ, we conducted 10,000 numerical experiments, each lasting for a fixed total time period T = 48 hr. The obtained number of contraction events in each numerical experiment consisted of one realization taken from a Poisson distribution of mean λ. The total number of contraction events over all experiments was normally distributed, as expected from the law of large numbers (see ‘Materials and methods’). We computed numerically the cumulative oxygen uptake J over T = 48 hr for each realization (normalized by the cumulative uptake at steady state), and for each λ, we calculated the mean and standard deviation of the computed J. Plotting the mean and standard deviation of J as a function of λ (Figure 3D), we found that increasing λ increases the cumulative uptake J and that the mean (solid black line) and standard deviation (thick purple segment) are in excellent agreement with analytical predictions (see ‘Materials and methods’). Spontaneous contraction frequencies on the order of 10 contractions per hour and more have been reported in the Hydra (Murillo-Rincon et al., 2017, Yamamoto and Yuste, 2020), indicating that, in theory, increases in uptake rates are possible. In our experience, however, such high frequencies occur only transiently when Hydra has been stressed, for example, by transferring the animals into very small containers such as concave glass for imaging purposes. At later time points, the contraction frequency of animals in these conditions converge towards an spontaneous contraction rate around three contractions per hour (Figure 2— figure supplement 6), similar to what we observed in our regular setup condition using a large beaker setup (Figure 2—figure supplement 2A, control condition). This suggests that under unconstrained conditions, Hydra’s baseline contraction rate per hour is near 3, rather than 10. We used the model to test the effect of limiting the maximal number of contractions over 48 hr to 144 contractions, which is the total number of contractions at an average spontaneous contraction frequency λ =3 contractions per hour. Effectively, this constraint means that once the maximum number of 85 contractions is reached, no additional contractions are permitted during the remainder of the 48 hr, mimicking, for example, a finite energy budget for spontaneous contractions. Interestingly, imposing this constraint showed that maximal uptake gains are achieved at contractions frequencies consistent with the imposed constraint (Figure 3D, thick blue graph), and that increasing λ beyond three contractions per hour decreases the cumulative uptake. When testing the effect of a constant-rate contraction activity, that is, with a constant TIC approximately equal to 48/λ hours, the model predicts a slightly increased uptake compared to stochastic activity, indicating that a precise rhythm would only confer a small benefit over the stochastic mechanism (Figure 3D, thin purple and blue graphs). Analogous results hold for the removal for accumulated chemicals produced by microbes at Hydra’s surface (see ‘Materials and methods’). Our model indicates that Hydra’s spontaneous contractions facilitate a greater exchange rate – including both uptake and release – of chemical compounds in the oral region as compared to the static foot region. When spontaneous contraction frequency is reduced, the maximal exchange rate near the head is reduced as well and, in the extreme case of zero contractions, becomes almost identical to the steady state in the foot region. The simplicity of the model should not distract from the universality of the mechanism it probes: the effect of stochastic contractions on the transport of chemicals to and from a surface exhibiting spontaneous wall contractions. To make analytical progress, we assumed the surface is spherical, but, by continuity arguments, the conclusions we arrived at are qualitatively valid even for nonspherical surfaces. These conclusions can be restated concisely as follows: fast spontaneous contractions of an otherwise slowly moving surface in a stagnant fluid medium cause impromptu shedding of the fluid boundary layer and lead to improved transport of chemicals to and from the surface between contraction events. Higher contraction frequencies are beneficial, but require additional, may be prohibitive, metabolic cost. A stochastic distribution of contraction events over time, following a Poisson process, produces benefits that are nearly as good as those produced by regular contractions, but without the need for a biological machinery to maintain a precise rhythm. Limiting the total number of contractions 86 leads to decreased performance at contraction frequencies beyond what would allow the contraction events to be Poisson distributed. Taken together, our model suggests that changing the spontaneous contraction frequency will alter the fluid microenvironment and biochemical concentrations experienced by Hydra’s microbial community; in particular, reducing the spontaneous contraction frequency would make the microenvironment near Hydra’s head, where the greatest fluid boundary layer shedding occurs during spontaneous contractions, more similar to the foot, where minimal fluid boundary layer shedding occurs. Reducing the spontaneous contraction frequency changes the colonizing microbiota To directly probe the impact of spontaneous contractions on the associated microbial community, we decreased Hydra’s spontaneous contraction frequency by two established methods: (1) continuous exposure to either light or darkness (Kanaya et al., 2019, Rushforth et al., 1963) and (2) chemical interference with the ion channel inhibitors menthol and lidocaine (Klimovich et al., 2020; Figure 2— figure supplement 1). Continuous exposure to light and treatment with ion channel inhibitor lidocaine reduced the spontaneous contraction frequency slightly from an average value of 2.5 contractions per hour in control conditions to 2.3 contractions per hour in continuous light and to 2.0 contractions per hour in lidocaine treatment (Figure 2—figure supplement 2A) (note that we were unable to make the measurements in the continuous dark condition). Treatment with ion channel inhibitor menthol almost completely abolished the occurrence of contractions. The treatments reduced spontaneous contraction frequency without significantly changing the frequency of other common fast contractile behaviors, such as somersaulting (Figure 2—figure supplement 2B and C; Han et al., 2018). Consistent with earlier studies (Kanaya et al., 2019, Klimovich et al., 2020), lidocaine and constant light treatment increased the likelihood of longer TIC; the contraction events remained, however, Poisson distributed, and TIC remained exponentially distributed (Figure 2—figure supplement 3B), as assumed in our mathematical model. Computing the average contraction frequency based on the best exponential fit to biological data, we found that it to be equal to 2.5, and 2.2 contractions per hour for the lidocaine, and continuous light treatments, 87 respectively, as opposed to 2.9 contractions per hour for the control, again confirming the reduced contraction rate in the treatment groups. Figure 4. Perturbing the frequency of spontaneous contractions over extended time periods shifts the microbial composition in Hydra. Reducing the spontaneous contraction frequency with 48 hr treatment of ion channel inhibitors (menthol and lidocaine), continuous light exposure (cont. light), or continuous dark (cont. dark) exposure significantly affects the bacterial community. Control is incubation in freshwater (Hydra medium) and 12 hr of light alternating with 12 hr of dark conditions. (A) Bar plot of the relative abundance on the genus level showing the effect of the ion channel inhibitors. (B) Bar plot of the relative abundance on the genus level showing the effect of the light treatments. (C, D) Analysis of the bacterial communities associated with the control and disturbed conditions using principal coordinate analysis of the Bray–Curtis distance matrix. The polyps with disturbed contraction frequency show distinct clustering (ellipses added manually). (E–G) Box plots displaying the fold change of the relative abundance compared to the control of Flavobacterium, Pseudomonas, and Acidovorax in response to the different treatments. (For all plots: n = 8-10) *p≤0.05; **p≤0.01; ***p≤0.001 (ANOVA and Kruskal–Wallis). The online version of this article includes the following source data and figure supplement(s) for figure 4: Figure supplement 1. The microbiota changes due to reduced contraction frequency. Figure supplement 2. Analysis of the bacterial communities using different distance matrixes. Figure supplement 2—source data 1. Statistical analysis of the PCoAs using Anosim and Adonis. Figure supplement 3. The bacterial load is not affected by ion channel inhibitors or light treatments. Figure supplement 4. Video recording of fluorescently labeled Curvibacter reveals a very stable colonization pattern during a full contraction cycle. Figure supplement 5. Reduction in contraction frequency does not alter the glycocalyx of Hydra. 94 Statistics Statistical analyses were performed using two-tailed Student’s t-test or Mann– Whitney U-test where applicable. If multiple testing was performed, p-values were adjusted using Bonferroni correction. Quantitative real-time PCR analysis (qRT-PCR) In order to investigate the bacterial community change and validate if there is an increase in the bacterial load, we performed quantitative real-time PCR analysis. The samples from the 16S rRNA profiling experiment were used. Amplification was performed as previously described (Klimovich et al., 2018b) using GoTaq qPCR Master Mix (Promega, Madison, USA) and specific oligonucleotide primers (EUB 27F, EUB 338R). Also, 4–5 biological replicates of each treatment (light, darkness, menthol, and lidocaine) and control with two technical replications were analyzed. The data were collected using ABI 7300 Real-Time PCR System (Applied Biosystems, Foster City, USA) and analyzed by the conventional ΔΔCt method. Minimal inhibitory concentration assay (MIC) To test whether the ion channel inhibitors (menthol and lidocaine) have an antimicrobial activity, their effect was tested in the MIC assays as previously described (Augustin et al., 2017). The following bacterial strain isolates from the natural H. vulgaris strain AEP microbiota were used: Curvibacter sp., Acidovorax sp., Pelomonas sp., Undibacterium sp., and Duganella sp. (Franzenburg et al., 2013; Klimovich et al., 2020). Microdilution susceptibility assays were performed in 96-microtiter-well plates. We tested a range of concentrations of the ion channel inhibitors in Hydra medium (with an additional 10% concentration of R2A agar) to match the nutrient-poor conditions of the behavioral assay. The concentration range was chosen such that the dose used in the behavioral assay was the middle of the dilution series. The following concentration were tested (in µM): menthol: 400, 300, 200, 100, and 10; and lidocaine: 10,000, 5000, 2500, 1000, and 100. The inoculum of approximately 100 CFU per well was used. The plates were incubated with the inhibitors for 5–7 d at 18°C. The MIC was determined as the lowest concentration showing the absence of a bacterial cell pellet. The run was designed in a way that every concentration had four replicates. 95 Immunochemical staining of the glycocalyx To test whether alterations in the contraction frequency has any effect on the glycocalyx of Hydra, we incubated polyps for 48 hr in menthol solution or Smedium (control) and visualized the glycocalyx by immunochemical staining and confocal microscopy of whole-mount polyps. To facilitate the detection of the Hydra’s epithelial surface, we used transgenic polyps expressing eGFP in the ectoderm (ecto-GFP line A8; Wittlieb et al., 2006). The glycocalyx was stained using a polyclonal antibody raised in chicken against the PPOD4 protein, generously provided by Prof. Angelika Böttger. The PPOD4 protein has been previously shown to specifically localize to the glycocalyx layer adjacent to the membrane of ectodermal epithelial cells in Hydra (Böttger et al., 2012). Polyclonal rabbit-anti-GFP antibody (AB3080, Merck) was used to amplify the GFP signal. Immunohistochemical detection was carried out as described previously (Klimovich et al., 2018b). Briefly, polyps were relaxed in ice-cold urethane, fixed in 4% paraformaldehyde, incubated in blocking solution for 1 hr, and incubated further with the primary antibodies diluted to 1.0 μg/mL in blocking solution at 4°C. Following the protocol of Böttger and co-authors, tissue permeabilization steps were omitted to avoid detection of immature glycocalyx components within epithelial cells. Alexa Fluor 488-conjugated goat anti-rabbit antibodies (A11034, Thermo Fisher) and Alexa Fluor 546-conjugated goat anti-chicken anti-bodies (A11040, Thermo Fisher) were diluted to 2.0 μg/mL and incubations were carried out for 2 hr at room temperature. The samples were mounted into Mowiol supplemented with 1.0% DABCO antifade (D27802, Sigma). Confocal mid-body optical sections were captured using a Zeiss LSM900 laser scanning confocal microscope. To measure the glycocalyx thickness, the fluorescence profile across the ectoderm has been recorded and quantified for 10 transects, each 10 μm long, using Zen Blue v. 3.4.91 software (Zeiss). GFP labeling of Curvibacter sp. AEP1.3 To visualize the colonization and dynamics during contraction events of Curvibacter sp. AEP1.3, we chromosomally integrated sfGFP behind the glmSoperon via the miniTn7-system as previously established by Wiles et al., 2018. The protocol was modified in order to manipulate the freshwater bacterium Curvibacter sp. AEP1.3 as follows: instead of the Escherichia coli SM10, we used the strain E. coli MFDpir as delivery system because biand triparental mating 96 was already observed (Wein et al., 2018; Ferrières et al., 2010). In addition, the growth medium was changed to the routinely used medium of Curvibacter sp. R2A and antibiotic concentration of the selection media was adjusted to 2 μg/mL. Kinematics and fluid flow analysis Fluid boundary layer visualization The fluid boundary layer around Hydra was visualized as described previously (Nawroth et al., 2010; Nawroth and Dabiri, 2014). Briefly, animals were placed into custom-made cube-shaped acrylic containers filled with Hydra medium and allowed to acclimatize for 30 min. The fluid boundary layer was visualized using fluorescein dye (Sigma-Aldrich) added near Hydra’s head during inter-contraction periods using a transfer pipette. An LED light source was used to illuminate the dye from the side. Videos were recorded using a Sony HDR-SR12 camcorder (1440 × 1080 pixels, 30 frames per second; Sony Electronics, San Diego, CA) mounted to a tripod in front of the custom container. Videos were processed to enhance the contrast of the dye with respect to the background using Adobe Premiere Pro (San Jose, CA). Fluid flow analysis Fluid flow around H. vulgaris AEP polyps was quantified using particle image velocimetry under a stereo microscope as described previously (Nawroth et al., 2012). Briefly, H. vulgaris AEP polyps were placed in a custom-made acrylic container with glass walls filled with Hydra-medium that contained 1 µm greenfluorescent microspheres (Invitrogen). A violet laser pointer mounted on a custom micromanipulator stage was aligned with a plano-concave cylindrical lens with a focal length of −4 mm (Thorlabs, Newton, MA) to create an excitation light sheet that captured the particles in ca. 1 mm thick plane across or along the animal’s body column. The green-emitting particles were recorded from top or side using a stereo microscope equipped with a long pass filter (to remove the violet excitation wavelength) and a camera (same as above) mounted to the eyepiece. Videos were recorded at 60 frames per second. We used MATLAB (MathWorks, Natick, MA) with an open-source code package (PIVlab Thielicke and Stamhuis, 2014) to measure the particle displacement field across the illuminated plane at each time point and derive the instantaneous planar flow velocity field. From this 97 vector field, the flow velocity magnitude (speed) profiles along lines extending perpendicular form Hydra’s surface (Figure 2E and F) were extracted to determine the fluid boundary layer thickness (Figure 2—figure supplement 5), defined here as the distance normal from the surface to the point in the fluid where flow velocity has reached 90% of free stream velocity (here assumed zero), as commonly done in biological systems (Vogel, 2020). Acknowledgements Research in the laboratory of TCGB was supported in part by grants from the Deutsche Forschungsgemeinschaft (DFG), the CRC 1182 'Origin and Function of Metaorganisms' (to TCGB) and the CRC 1461 'Neurotronics: Bio-Inspired Information Pathways' (Project-ID 434434223 – SFB 1461) (to TCGB and AK). AK is supported by a DFG grant KL3475/2-1. We thank the Central Microscopy Facility at the Biology Department of the University of Kiel for excellent technical support. TCGB appreciates support from the Canadian Institute for Advanced Research. JN and EK acknowledge support from the National Institute of Health grant 1R01 HL 15362201-A1 and the National Science Foundation INSPIRE grant 1608744. Author contributions Janna C Nawroth, Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing; Christoph Giez, Data curation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing; Alexander Klimovich, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing; Eva Kanso, Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing; Thomas CG Bosch, Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing 98 References Augustin R, Schröder K, Murillo Rincón AP, Fraune S, Anton-Erxleben F, Herbst EM, Wittlieb J, Schwentner M, Grötzinger J, Wassenaar TM, Bosch TCG. 2017. 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Whole-body imaging of neural and muscle activity during behavior in Hydra Vulgaris: effect of Osmolarity on contraction bursts. ENeuro 7:ENEURO.053919.2020. DOI: https://doi.org/10. 1523/ENEURO.0539-19.2020, PMID: 32699071 103 Chapter III: Multiple neuronal populations control the eating behavior in Hydra and are responsive to microbial signals Accepted in Current Biology (2023) Christoph Giez1*, Denis Pinkle1, Yan Giencke1, Jörg Wittlieb1, Eva Herbst1, Tobias Spratte2, Tim Lachnit1, Alexander Klimovich1, Christine Selhuber-Unkel2, Thomas Bosch1* 1. Zoological Institute, University of Kiel, Christian-Albrechts-Platz 4, 24118 Kiel, Germany 2. Institute For Molecular Systems Engineering and Advanced Materials (INSEAM), University Heidelberg, Im Neuenheimer Feld 225, 69120 Heidelberg, Germany *Corresponding Authors: Christoph Giez, [email protected] Thomas C.G. Bosch, [email protected] DOI: doi.org/10.1101/2023.04.28.538719 110 Figure 2. Visualization of the neuronal subpopulations in the head of Hydra. A-G. Distribution, structure and morphology of ectodermal neuronal subpopulation N6. The constructs used for visualization and manipulation of N6 contains the promoter from an RFa neuropeptide (t2059aep, HVAEP9.T017227.1) regulating the expression of either GCaMP6S or NTR-GFP. A: Schematic of Hydra and the localization of N6 neurons. B: Immunohistochemistry of N6 neurons in the head (scale 100µm) stained with antibodies against GCaMP6S/GFP. C-D: Staining (artificial color added) of two representative enlargements showing the two different types of N6 neurons, with sensory neurons at the head tip (C) and ganglion neurons (D) found in groups around the head base (scale 10µm), as schematically presented in E. F: enlarged section of A with the neurites connecting the neurons (scale 50 µm). G: 2D-density plot of the distribution of neurons in a slice of the head (n=5). Higher densities of N6 neurons are present near the mouth and near the basis of the tentacles. H-L. Ectodermal neuronal subpopulation N3, for which the construct included the promoter of the neuropeptide Hym-355 (t12874aep, HVAEP2.T004115.1). H: Schematic of the localization of N3 neurons in the head (I, scale 100µm) and in the body, tentacles and foot (scale 10µm). Their distribution in the head is summarized in J, with an enlarged section shown in K (scale 50µm). Higher densities are present in the tip and basis of the head (L, n=12). 111 M–S. Endodermal neuronal subpopulation N4 with the construct containing the promoter of a NEUROD1-like protein (t14976aep, HVAEP4.T008286.1). M: the localization of N4 neurons in the polyp. N: Overview of N4 neurons in the head (scale 100µm). O-P: Staining (artificial color added) of two representative enlargements showing the two different types of N4 neurons, with sensorylike neurons (O) and ganglion neurons (P, scale 10µm). In the head region, they are mostly present at the basis of the tentacles (Q, R, scale 50µm) with a lower density at the tip of the head (S, n=10). T-U. Density of neurons/mm2 in head, body and foot, for subpopulation N3 (T) and N4 (U). Highest densities of the latter are found in the head (p<0.01, n=5-11, ANOVA, Turkey post-hoc test). V. Schematic representation of the distribution and organization of the different neuron subpopulations in the head, from tip (center) to tentacle base. The overlapping locations of the three subpopulations are separated here for clarity. * p≤0.05; ** p≤0.01; *** p≤0.001 N6, N3 and N4 neurons are differentially active during the mouth opening Animals were observed during mouth opening as part of their eating behavior and neuronal activity of head neurons was analyzed. The signals for the distinct sensory and ganglion cell types of N6 and N4 were recorded separately. Following the glutathione (GSH) stimulus, the mouth started to open by contraction of the epithelia32, which was recorded by plotting mouth width (Fig. 3A-C). After GSH stimulation, the first signal was recorded within 16.8±26.5s (n=6) for N6 sensory cells, whereas the N6 ganglion cells responded 9.3±5.3s later (Fig. 3A, suppl. Video 3-4), at which time point the mouth started to open. As mouth opening continued, N6 cells activity slowly decreased (Fig. 3A). This was in contrast to the activity of N3 neurons, which at first sight seemed unresponsive to the GSH stimulus, both in the head and the foot region (Fig. 3B, suppl. Video 5-6). The N4 neurons responded strongly to the GSH stimulus. A faster response was observed for the N4 ganglion cells located at the base of the head, with a slightly slower response of the N4 sensory-like neurons (Fig. 3C, suppl. Video 7-8). After the delayed response of the N4 subpopulation, around 30s, the whole cell population started to spike in a synchronous manner (Fig. 3C, suppl. Fig. 4). Interestingly, N3 neurons responded opposite to N4 to GSH stimulus, as their spiking frequency decreased significantly (p<0.001, Fig. 3D-E). In individual polyps with a relatively frequent N3 spiking at the baseline (Fig. 3D), this became less frequent after glutathione administration. In individual polyps with a low baseline frequency, the firing of N3 neurons stopped completely (Fig. 3D). This was stepwise restored to higher frequencies in the late and end phase of feeding (330-420s post-stimulus and ~1800sec). In contrast to N3, the spiking frequency 112 of N4 neurons dramatically increased in response to GSH (Fig. 3F) and remained high during late phase of feeding (Fig. 3G). Since N6 cells did not produce pulses but showed continuous signal which increases or decreases (Fig3A), an analysis of the spiking frequency could not be performed. Figure 3. Neuronal response during the eating behavior. A-C. Response of neuron subpopulations to a glutathione food stimulus. A: N6 neurons were differentiated into the sensory (red line) and ganglion cells (orange line). The sensory cells responded before the ganglion neurons did. Lines represent the mean of either sensory or ganglion neuronal population from one representative animals with grey shading showing the standard deviation (see suppl Fig. 3 for more animals). At the same time, mouth width was recorded (black line, in pixel). B: the spiking activity of N3 neurons in the head and foot was less obviously affected by glutathione administration. A slightly higher fluorescence change and baseline activity was recorded for neurons in the foot than in the head (one representative animal, see suppl Fig. 4 for more animals). C: N4 neurons in the head responded strongly to glutathione administration, with a delay for the sensory cells (mean of population: light green line, light grey shading), whose reaction was also weaker than for the ganglion N4 neurons (dark green line, dark shading, one representative animal, see suppl. Fig. 3 for more animals). D-G. Spiking frequency of N3 and N4 neurons before and after glutathione administration. D: the spiking activity of N3 in 5 individual polyps decreased in frequency or stopped altogether after the GSH stimulus. E: the spiking frequency of N3 neurons at baseline (90s before glutathione, n=10) was significantly lowered during the early feeding response and mouth opening (0-90s post glutathione, n=10) and was restored during the later and end feeding response (late: 330-420s post 113 glutathione, n= 5; end: >1800sec, n=3). F: the spiking frequency of N4 neurons increased dramatically after glutathione administration (n= 4). G: This increase compared to baseline (n=4) was highly significant during early feeding (n=4, p<0.01, ANOVA, Turkey post-hoc test) and persisted during the late feeding response (n=4, p<0.01, ANOVA, Turkey post-hoc test). H-K. Linear correlations of neuronal activity from before and after adding GSH with either mouth width or mouth opening speed. H. A positive correlation was found between N6 ganglion and sensory cells with mouth width (H, n=7) as well as with mouth opening speed (I). J. A negative linear correlation was observed between spiking frequency of N3 neurons with mouth width (J, n=15). K. A positive correlation existed between N4 neurons firing and mouth width (K, n=6). Correlation coefficients and p-value done via Spearman. * p≤0.05; ** p≤0.01; *** p≤0.001 Next, we assessed whether there was a correlation between the mouth opening dynamics and neuronal activity. For this, a time window before and at the onset of neuronal activity was used and the determined change of fluorescence was correlated with either the mouth width (measured in pixel, px) or speed of mouth opening (px/sec, Fig. 3H-K). A linear positive correlation was observed for both effects, in sensory as well as ganglion N6 cells (Fig. 3H-I). Fitting the N6 data in a linear correlation for mouth width and speed, the correlation was slightly better for the ganglion cells than for the sensory cells (n = 7, Fig. 3H-I). This indicates that N6 ganglion cells were more likely involved in the mouth opening event and tissue movement, while the N6 sensory cells are receiving the sensory input. The negative correlation between N3 neuron firing and mouth width (Fig. 3J) suggested that a higher frequency of N3 neuron firing correlated with a smaller to no mouth opening (R2 = 0.4, n= 18). The positive correlation between firing of the synchronous N4 neuron population and mouth width fitted with the highest correlation (R2=0.83, n=4, Fig. 3K). In combination, these data suggest that during eating behavior, N6 sensory neurons are active right before the mouth is opening. The activity of N6 ganglion cells correlates with the mouth opening and speed of tissue movement during mouth opening. The spiking of N3 decreases during eating while N4 cells fire more frequently, sending synchronized pulses through the complete polyp. Multiple neuronal subpopulations are involved in eating behavior To identify the contribution of individual neuronal subpopulation in the eating behavior we used the NTR-Mtz cell ablation system. Genetic constructs were used that contained nitroreductase (NTR) fused to GFP, to convert metronidazole 114 (Mtz) to a toxic product that induces apoptosis in the target cell population (suppl. Fig. 5A)45. As a control, H. magnipapillata strain Sf1 polyps were included that lacks interstitial cells (neurons, nematocytes, gland cells and germline) after application of a heatshock46. Figure 4. Ablation experiments highlight specific roles of the neuronal subpopulations. A. Immunohistochemistry staining for GFP under the promoter for N6 (red) and RFamid (turquoise; expressed in N6 and other neurons) of polyp heads in absence and in presence of 10mM metronidazole (Mtz, 8h and 12h/overnight (ON)). Scale bar 50µm. Note the depletion of N6 neurons over time. B. Different NTR transgenic lines of Hydra in absence and presence of 10mM Mtz for 12h. Note the inflated body shape following ablation of N4 and the fully contracted body in absence of N3. C. The effect of ablating neuronal subpopulations on the mouth opening time. The measured mouth opening time was normalized to the mean response of the control within each experiment before pooling all data. The presence of i-cells is essential for mouth opening. Absence of N6 and N4 significantly (p<0.001, N6: n=46; N4: n=44, N4+N6: n = 7) decreased mouth opening, and when lacking in combination it abolished the behavior. Ablation of N3 had no significant effect (p>0.05). Treatments were compared to the “Wildtype+Mtz” group. 115 D. The percentage of animals displaying ‘plate eating’ behavior decreased when neuronal subpopulations were ablated (n=18-44). The ablation of N4 inhibited this behavior completely. E. The mouth opening response time after administration of GSH was delayed following ablating of neuronal subpopulations. N6 and N4, alone or in combination, had a strong impact on the response time, but N3 did not. (N6: n=46; N4: n=44; N3: n =18; N4+N6: n = 3) F. The tentacle movement response time was also affected by ablating the neuronal subpopulations, in particular by N6 (N6: n=46; N4: n=44; N3: n =18; N4+N6: n = 6). All statistical analyses are based on Kruskal-Walli’s rank sum test and Dunn test as post-hoc with Bonferroni method. * p≤0.05; ** p≤0.01; *** p≤0.001. The N6 specific promoter caused a strong expression of the NTR-GFP fusion protein in Rfa-positive cells in the polyp’s head (Fig. 4A). Indeed, 93% of Rfa+ cells were also GFP positive (GFP negative: 12±5.57 cells, mean±sd), indicating that the N6 line was nearly fully transgenic. Incubation with 10mM Mtz eliminated the N6 neuronal subpopulation within 12h (Fig. 4A, suppl. Fig. 5B). Other neuronal populations remained intact, for instance Rfa+ cells in the tentacles remained detectable, demonstrating that the cell ablation was specific for the target N6 subpopulation. Mtz treatment of control animals containing the GCaMP6S construct had no effect (Suppl. Fig. 5D, G). Despite the ablation of N6 neurons, the transgenic animals had no changed phenotype (Fig. 4B, compare the polyp pair to the left, without and with Mtz treatment). Similar transgenic animals were produced for ablation of N4 and of N3 (Suppl. Fig. 5D-I). Polyps lacking N4 neurons that are normally present in head, body and foot, showed an inflated body shape (Fig. 4B, middle pair) and animals lacking N3 neurons (expressed in all body parts) were fully contracted (right-hand pair). The effect of apoptotic removal of these different neuronal subpopulations on the eating behavior of the polyps was studied in freely moving Hydra individuals (Suppl. Fig. 6, Suppl. Video1, material and methods). Following GSH stimulation, the duration of the mouth opening period was recorded (how long the mouth stays open), as well as the response time required to initiate tentacle or mouth movement (how long till the first reaction). Results were reported as fold-change compared to control (Fig. 4C, E, F). Interestingly when using GSH as artificial food stimulus polyps attempted to ingest the chamber surface (Fig. 4D). This was 116 additionally scored as ‘plate eating’ and described as an extremely wide mouth opening. As expected, presence of neurons is a pre-requisite for the eating behavior, as their absence in heat-treated Sf1 animals abolished mouth opening completely (Fig. 4C). Ablation of either N6 or N4 subpopulation resulted in a severe reduction in mouth opening time (fold-change compared to control: N4: 0.31±0.398, n=44, p<0.0001; N6: 0.32±0.233, n=46, p<0.0001; Fig. 4C). Removal of N3 caused a non-significant reduction of mouth opening time (0.599±0.94, n=18, p>0.5). When N4 and N6 neurons were removed in combination, the transgenic animals completely stopped opening their mouth (0±0.09, n=7; Fig. 4C). Plate eating was observed in 65% of control animals when the freely moving polyps spread their mouth wide over the surface of the chamber (Fig.4D). Ablation of N4 neurons completely inhibited this extreme wide opening of the mouth (Fig. 4D). The response time to open the mouth after the GSH stimulus was affected by removal of N4 and N6 neurons but not by removal of N3 (Fig. 4E). Absence of the subpopulation N6 also strongly delayed tentacle movement (p<0.001, n=46, Fig. 4F). Taken together, the data show that mouth opening duration, its response time and the response time for tentacle movement during eating behavior are all controlled by two distinct neuronal subpopulations endodermal N4 and ectodermal N6, with a degree of redundancy that adds some resilience to the functioning of this fitness-relevant and important behavior as single ablation could not completely inhibit mouth opening. A global neuronal network of connected localized neuronal subpopulations regulates epithelial contraction To investigate if neuronal subpopulations form synaptic-like connections, immunohistochemistry was performed with antibodies targeting the combined RFa+ neuronal subpopulations N1, N6 and N7, or the transgenic lines expressing GFP (Fig. 5, suppl. Fig. 7). This uncovered that N3 is connected to multiple other ectodermal neuronal subpopulations (Fig5F-H). Contacts suggestive of synapticlike structures between N3 and N6RFa+ neurites were identified in the head (Fig. 5F), while in the foot N3 and N1RFa+ neurons were in close proximity (Fig. 5G). In the tentacles N3 was aligned in nerve bundles with neurites in contact with N7RFa+ 117 sensory neurons (Fig. 5H). Potential contacts between ectodermal and endodermal neuronal subpopulations (endodermal N4 with either N3 or N6) were not observed but regions of close proximity (Suppl. Fig. 7). To investigate the sequential activity of the neurons in the neuronal circuit, we measured the time gap between the first neuronal activity (first activity in a single neuron, not whole neuronal population) and the onset of mouth opening (Time before mouth opening, Fig. 5A-B). A longer time-gap relates to an earlier response in the eating behavior. Figure 5A shows that the earliest responses were observed for sensory N6 cells, followed by N6 ganglion cells and then N4 ganglion/sensory cells (Fig. 5I, n=7-15). Since measuring the activity of neuronal populations in separate animals, high variability was observed (Fig. 5A). Measuring the population N4 and N6 in the same animal (double construct, see material and methods), a significant difference between the different populations was detected (Fig. 5B). Together, this suggests that sensory N6 cells detect the food stimulus first, to pass the signal on to ganglion N6 cells, before the N4 cells respond. The number of primary neurites located in top part of the head and at its base was determined for N6 and N3 (Fig. 5C). The top of the head contained the fewest N6 neurites, and the base contained the most (Fig. 5C). This would enable a signal picked up by N6 sensory cells to be not only propagated but also enhanced via N6 neurites at the base, where a potential contact between N6 and N4 cells (cf. suppl. Fig. 7) ensures involvement of the latter. At the same time, contact between N6 and N3 would allow the inactivation of the N3 cells. As mouth opening requires the contraction of epithelia, we also measured the time required to initiate contraction of both ectoderm and endoderm involved in mouth opening (see Methods for the application of calcium imaging constructs under control of an actin promoter for this)47. First, we observed that the ectoderm of the head base contracted before the endoderm did (Fig. 5D, E. While the endoderm was activated in the whole head region at some point during the behavior, with a faster response at the top than at the base of the head (Fig. 5E), the ectoderm was only active at the base of the head, close to the tentacles (Fig. 5D). The time required for ectodermal contraction at the head base and for endodermal contraction till mouth opening differs. 118 All data taken together suggest that the reaction flow went from the N6 sensory cells to the ectodermal epithelium and to N6 ganglion cells, and from there to the N4 ganglion neurons and then to the endodermal epithelium. This is summarized in Figure 5I. Figure 5. The model of the neuronal circuit controlling the eating behavior in Hydra. A. Time gap between the first neuronal activity and the beginning of mouth opening (time point 0sec). The N6 subpopulation is split into sensory and ganglion cells. A lower value indicates a faster response to the food stimulus, as seen for N6 sensory cells (n=7-15, Kruskal-Wallis and Dunn posthoc). B. Analysis of the time sequence within the same animal (n = 8) highlighting the significant time difference between the different neuronal subpopulations (Repeated Measures ANOVA and pairwise t-test). C. Number of primary neurons of the different neuronal populations in the head (n= 4-11). N6 ganglion cells have the most primary neurites. D-E. Contraction response of the epithelia to the food stimulus, with ectoderm (D) and endoderm (C). The time point when the mouth opened is indicated by dashed line. No contraction of ectoderm in the head top was identified but a time relapse between contraction of endoderm at the head top and base is visible C. (n=4) F-H. Identification of contact points between N3 and other ectodermal Rfa+ neurons by immunohistochemistry using antibodies targeting N1, N6, N7 in combination, and GFP for visualization of N3. Contact points (white arrows) are present between N3 and N6 in the head (D), where N1 and N7 are absent. In the foot (E) contact points are found between N3 and N1 and in the tentacles (F) they exist between N3 and N7. 119 I: The model of the neuronal circuit involved in the eating behavior. N6 sensory cells detect glutathione first and propagate the signal to N6 ganglion cells, where it spreads to the endodermal N4 ganglion cells. At the same time, the signal propagates to N3 cells which modulate the response and stops firing, leading to mouth opening. Contact between N3 ganglion cells and N1 and N7 neurons ensures further spread of the signal through the body of the polyp. * p≤0.05; ** p≤0.01; *** p≤0.001 The role of bacteria: mono-association of Curvibacter sp. reduces mouth opening. Since there are symbiotic bacteria in the immediate proximity of the head neurons36, we next asked whether these bacteria might have an influence on the neuronal circuit identified here that control eating behavior. For this, germ-free (GF) animals were compared with wildtype (Wt) and recolonized with a number of pure cultures of native bacteria as described previously48,49. Intriguingly, germfree animals kept their mouths open much shorter than control animals did (p<0.01, n= 30, Fig. 6A). Mono-association of polyps with single members of the core bacterial community, (including Duganella, Pelomonas or Undibacterium species, Fig. 6B) rescued this defect (Fig. 6A), although monoassociation with Pseudomonas or Acidovorax had no effect (Fig. 6A). Completely unexpected results were obtained with animals that were monoassociated with Curvibacter sp., which is the most abundant representative in the wildtype Hydra AEP microbiota (Fig. 6B)48–50. Exclusive presence of these bacteria reduced the mouth opening time to nearly zero (n=47, Fig. 6A, C). The effect could be restored to some degree by co-addition of a second bacterial species, whereby all tested di-associations produced similar effects (Fig. 6C). The combination of Curvibacter with Undibacterium and Duganella restored the mouth opening time to normal (Fig. 6C). The strong inhibitory effect on the mouth opening time by mono-association of Curvibacter led us to investigate the effect of these bacteria on the neuronal activity during eating behavior. For this, the neuronal activity of Wt, GF and polyps mono-associated with Curvibacter was compared by calcium imaging (Fig6 D-F). As shown in Figure 6D, the activity of N6 sensory neurons in germ-free animals was much lower compared to controls. Interestingly this could be restored by presence of the Curvibacter symbiont (Fig. 6D). The spiking frequency of N4 was 126 increasing bacterial diversity while adding back specific members of the core microbial community (Fig. 6C). The inhibitory effect of Curvibacter sp. on eating behavior was not accompanied by a detectable change in N6 and N3 neuronal activity compared with the control (Fig. 6D-F). Instead, mono-association of Curvibacter sp. reversed the effect of germ-freeness back to control conditions. This highlights that Curvibacter sp. affects neuronal activity and also that neurons are able to detect the presence of Curvibacter sp. Since the N6 sensory neurons are in close contact with the microbiota36, their response was to be expected. Surprising was that Curvibacter reduced the spiking frequency of the endodermal subpopulation N4 (Fig. 6E) which was rather unexpected and suggests that Curvibacter sp. has a more global effect on the nervous system. The transcriptional response of Curvibacter sp. to the host environment points to the secretion of glutamate in the presence of glutamine, which was supported by in vitro observations (Fig. 7B-D). The neuronal subpopulations N3, N4 and N6 express glutamate receptors and a NMDA receptor that could responds to bacterial glutamate and integrates this information into the neuronal circuit of the eating behavior (NMDAR and mGlu, see Suppl. Fig. 8). Interestingly, Hydra down regulates those glutamate receptors in the presence of Curvibacter (Fig. 7G) which also included the NMDA receptor. This additionally supports the role of glutamate. Since N4 and N6 but not N3 neurons showed a response to Curvibacter sp. (Fig. 6D-F), we assume that N4 and N6 receive and integrate the bacterial signal which may affect the downstream signaling instead of the neurons themselves. In the presence of other bacteria such as Undibacterium, glutamate gets scavenged, and the inhibitory effect abrogated (Fig. 6C and Fig. 7B-C, H). In case of Duganella it is more complex and other factors such as bacteria-bacteria interactions can come into play as described previously66. Our work shows that the old observation published by Lenhoff (1961)52 that glutamate has a negative effect on eating behavior in Hydra may find its explanation in the microbial colonization of Hydra. 127 Evolutionary perspective Altogether, our findings confirm and expand on the idea that in animals without a central nervous system, a complex behavior is controlled by coordination of multiple subpopulations of neurons, forming circuits and modules54. Our observations presented here show that this not only requires the coordination of multiple neuronal circuits, but also that signals from the microbial environment play an important role. We present data that support a model (Fig. 7H) in which in the critical phase of mouth opening, may be affected by microbially produced glutamate. Already in 1963, the evolutionary biologist Tinbergen outlined an organizational framework that would control complex behavior67,68. His research involved four levels of analysis: phylogenic, developmental, functional, and mechanistic investigations. Our findings of the influence of the microbiota on the neuronal control of Hydra´s eating behavior, which co-evolved with this host49,57, adds this as an additional environmental perspective to be considered when studying complex behavior. That bacteria are able to produce molecules that are active on neuronal cells has been known for quite some time69,70, but most work has been carried out in mammals. Here we show that the integration of bacterial signals into neuronal circuits might be as evolutionary ancient as the first nervous system, as it already exists in cnidarians. Our observation that bacterial glutamate can play a crucial role in this interaction, together with the numerous findings on the influence of this molecule on mammalian intestinal physiology9,70, support the idea that it is part of an ancestral interkingdom language. Acknowledgements This work was supported in part by grants from the Deutsche Forschungsgemeinschaft (DFG), the CRC 1182 “Origin and Function of Metaorganisms” (to TCGB.) and the CRC 1461 “Neurotronics: Bio-Inspired Information Pathways” (Project-ID 434434223 – SFB 1461) (to TCGB and AK). T.C.G.B. appreciates support from the Canadian Institute for Advanced Research. AK is supported by a DFG grant KL3475/2-1. C.S. and T.S. acknowledge funding by the DFG under Germany’s Excellence Strategy 2082/1-390761711 (3D Matter 128 Made to Order). We thank Trudy Wassenaar for critical reading of the manuscript. We thank the members of the Bosch lab for support and discussion, and Andreas Tholey, Christoph Kaleta, Georgios Marinos and Karlis Moors for discussion. We also thank Urska Repnik and Marc Bramkamp from the Central Microscopy Facility at the Biology Department of the University of Kiel for excellent technical support. We highly appreciated the expertise provided by the sequencing facility at the Institute of Clinical Molecular Biology (IKMB) in Kiel, Germany. Authors contribution C.G. and T.C.G.B. conceptualized the project and wrote the manuscript. T.C.G.B., J.W., A.K., Y.G., D.P. and C.G. designed and performed experiments on transgenesis. T.C.G.B., D.P., C.S., T.S., E.H. and C.G. designed and performed histological, behavioral experiments. C.G., E.H., T.L. and T.C.G.B. designed and performed neuronal activity and microbiota experiments. T.L. and C.G. analyzed the data. Declaration of interest The authors declare no competing interests. Data and code availability • Source data reported in this paper will be shared by the lead contact upon request. • Codes used for the analysis and statistical analysis will be shared by the lead contact upon request. • Any additional information required to reanalyze the data in this paper will be shared by the lead contact upon request. 129 STAR Methods Materials availability The plasmids and transgenic Hydra vulgaris AEP generated in this study are available upon request. Code availability All codes used in this study are available upon request. Data availability All data presented in this study are available upon request. Experimental Procedures Hydra maintenance In this study used Hydra polyps (Hydra vugaris AEP, Hydra magnipapillata sf1) were cultured according to standard procedures in standard Hydra culture medium (CaCl2 0.042g/L; MgSO4x7H20 0.081g/L; NaHCO3 0.042g/L, K2CO3 0.011g/L in dH2O) 71. The animals were kept in 250mL glass beaker at 18°C with a 12/12h light cycle. The feeding regime was strictly three times per week with Artemia nauplii for at least two weeks before any experiment. Animals were starved for 1-3 days before either an ablation experiment or a calcium imaging analysis. There was no difference in the mouth opening duration between 1-3 days of starvation. Generating germ-free animals and re-colonization Germ-free animals were derived by treating animals for five days with an antibiotic cocktail containing rifampicin, ampicillin, streptomycin and neomycin in final concentrations of 50 µg/ml each and spectinomycin of 60 µg/ml, as previously described49. Control polyps were incubated in 0.1% DMSO for the same time since rifampicin is dissolved in DMSO. The antibiotic cocktail was replaced after 72h of incubation. After 5 days in antibiotics, the animals were transferred to sterile Hydra culture medium and incubated for another 2 days. On the second day in sterile Hydra culture medium, animals were recolonized with defined bacteria or communities and medium was exchanged. After another 3 days of incubation with defined bacteria or communities, polyps were used for the behavioral assays or RNA sequencing. The germ-free status was checked twice 130 during the protocol, on the seventh day and the tenth day of the protocol via plating macerated polyps on R2A-agar plates. Random samples were also tested via PCR using universal rRNA primer Eub-27F and Eub1492R72. No colonies formed on the R2A agar plates after one week of incubation at room temperature and absence of amplification product confirmed the germ-free status. Germ-free animals were monocolonized with pure bacteria cultures of the core members of Hydras microbiota: Curvibacter AEP 1.3 (NCBI:txid1844971), Duganella C 1.2 (NCBI:txid1531299), Undibacterium C 1.1 (NCBI:txid1531302), Acidovorax sp. AEP 1.4, Pelomonas AEP 2.2 (NCBI:txid1531300) and Pseudomonas sp.50. Bacteria were cultured from existing isolate stocks in R2A medium at 18°C for three days and subcultured the day before recolonization (dilution depending on the bacterium). In all experiments we started from a fresh cryostock and identity of bacteria was regularly tested. From the overnight culture approximately 105-106 cells were added to the 50mL sterile Hydra culture medium containing 30-50 animals. For the different combinations of bacteria, each bacterium was added in at equal ratios. After three days the recolonization success was checked by plating three macerated polyps per treatment in a 1:1000 dilution on R2A agar plates and counting the CFUs after three to four days of incubation at 18°C. Recolonized animals were only used when recolonization was successful and in agreement with previous published values73. Promoter identification and extraction Marker genes specifically expressed in the different neuronal subpopulations were identified using the single cell atlas previously published 24,25 (suppl. Fig. 1). Genes were then mapped against the different available genomes of Hydra (nih.gov/HydraAEP) and their promotor were extracted as 1000-1500bp upstream of the gene, by including the first 30bp of the open reading frame. The sequence was than cloned into pGem-T Easy (Promega, cat# A1360) while restriction enzyme binding sites were inserted to further clone the construct into the LigAF vector (for sequences see suppl. Table 1). Transgenesis and constructs Transgenic Hydra vulgaris AEP were derived following the established protocol by Wittlieb et al.71,74 using a modified version of the LigAF vector. Different lines 131 were produced in which the specific promotors for desired expression in the neuronal subpopulation regulated either GCaMP6S (as in Dupre et al. 26) with an actin terminator or the nitroreductase (NTR)44 (in silico codon optimized) coupled to an eGFP at the C-terminus followed by an actin terminator sequence (see suppl. Table 1 for sequences). We generated single constructs for N3, N4 and N6 for NTR-GFP and GCaMP6S as well as a double construct with the promotor of N4 driving NTR-GFP and the promotor of N6 driving the expression NTR-GFP for ablating both population at once. The same construct was designed for GCaMP6S as well. As previously described, the construct was injected via microinjection in embryos resulting in mosaic animals. Animals were screened for transgenic neurons and selected to produce fully stable transgenic animals. We then induced embryogenesis in the transgenic lines and derived F1-generations which ensured that the construct was incorporated in all cells. This was successful for transgenic lines N4 and N6 while for N3 reached a non-mosaic stable population only (see suppl Table 1). Histology For antibody staining, Hydra polyps were relaxed with 2% urethan(Sigma-Aldrich, U2500) in Hydra culture medium for less than 2min and fixed for 2h (RT) or overnight (4°C) in Zamboni (Morphisto, cat#12773). Following 3 washes in PBS with 0.1% tween (PBST) followed by an incubation in PBS with 0.5% TritonX100 and an 1h of blocking in PBST with 1% bovine serum albumin (BSA, Roth, cat# 8076.1). Animals were than incubated overnight at 4°C with the primary antibody in PBST and 1% BSA. Primary antibodies used in this study were: anti-GFP (Biozol, cat# GFP-1010, 1:1000 dilution) and anti-FMRFamid (BMA Biomedicals, cat# T-4322, 1:1000 dilution). After the primary antibody incubation, four 15min washes in PBST with 1%BSA were performed before adding the secondary antibody. Secondary antibodies used in this study were: goat anti-chicken Alexa Fluor 488 (Invitrogen, cat# A11039, 1:1000 dilution) and donkey anti-rabbit Alexa Fluor 546 (Invitrogen, cat# A10040, dilution 1:1000). Animals were incubated for 2h at RT with the secondary antibody. After the secondary antibody another four 15min washes in PBST (here 0.5% tween) with 1% BSA were performed followed by a short 5min incubation in TO-PRO™-3 Iodide (642/661)(Invitrogen, cat# T3605, 1:1000 dilution). The animals were mounted in moviol with DAPCO on glass slides and stored at 4°C till imaging. 132 Imaging and analysis Fixed and stained animals were imaged either with a LSM900 (Zeiss) or Axio Vert.A1 (Zeiss) using colibri 7 (Zeiss) as a light source. Further processing of the images was performed with Zen Blue 3.4 software (Zeiss) or Fiji75. For the analysis of neuronal densities and distribution we used the Cell Counter plugin by Fiji. For counting, a rectangular area was subsampled from the images to count comparable areas (see Fig2B, I and N). For the densities, we calculated the density of neurons per mm2. For the 2D density plots (Fig2G, L and S) we aligned the rectangle area using Fiji and extracted the xand y-coordinates. Data were analyzed using R (v4.0.3)76 over RStudio IDE77 and for the visualization the plugin tidyverse (v1.3.1)78 was used. For the characterization of the primary neurites, we counted all neurites originating from a neuron soma. In all cases at least five animals were analyzed. Multicolor images shown throughout are pseudo-colored composites (maximum projection), with brightness and contrast adjusted for clarity. NTR and sf1 cell ablation experiments Animals were incubated overnight in 10mM Metronidazole (Sigma, cat# M1547)44,45,54. On the next morning animals were screened under a fluorescence microscope for absence of GFP+ cells. Once it was determined that the ablation had been successful, the animals were washed once in Hydra culture medium and used for behavioral assays or histology on the same day. Each experiment included a control of Hydra vulgaris wildtype and the corresponding GCaMP6S transgenic line with Metronidazole and the NTR-GFP transgenic line without Metronidazole. In all experiments at least 5 animals per treatment were used. Hydra magnipapillata Sf1 were exposed to 28°C for 48h together with a control (H. magnipapillata) for the heat shock and afterwards kept for 19 days under standard culture conditions. Neurons were quantified on day 5, 8, 11, 14 and 19 using a cell maceration protocol22. Polyps were dissociated in maceration solution (1:1:13, Glycerol, Acidic acid, Hydra culture medium) at 32°C for 30 minutes. Afterwards cells were fixed in 8% PFA and spread out on gelatin-coated slides. Counting was done blinded. 133 Behavioral analysis Acquisition To analyze the effect of cell ablation and bacteria on the eating behavior, we developed a recording system where we can observe multiple animals at once and animals were minimally restrained. For this, a chamber was used where 5-6 animals could be observed under controlled fluid flow (Suppl. Fig. 6). The chamber consists of a two-piece aluminum case and two plexiglass pieces in which one cavity was milled and the other used as a lid (see suppl. Fig. 6). These were connected and liquid tight via braces. Animals could survive in the chamber for weeks as long as fresh Hydra medium was supplied. The chamber has a height of 0.4mm and two channels on both sides fitted with tubes through which medium can be manually supplied. The animals were recorded at 18°C in an insulated climate chamber to avoid external stimuli using M3C Wild Heerbrugg binocular microscopes and Axiocam 208 color (Zeiss), taking a picture every 2 seconds. Mouth opening, tentacle response and analysis The animals were given 10 min to adapt to the recording chamber before recording started and another 5-10 min before reduced glutathione (GSH, Roth, cat#6382.1) was supplied via the tube system. In all assays a final concentration of 10µM GSH was used, prepared in the same medium as the animals were kept in prior to the experiment using a 0.1M stock solution. Each animal was only recorded once. Acquired movies were blinded to their treatment and assigned with a random three-digit number before analysis. The behavior was manually annotated. The following different behaviors were scored: the mouth opening time, tentacle movement and the type of mouth opening (see suppl Video 1). As animals exhibited multiple mouth openings during the assay, for the mouth opening time only the first event was recorded. The raw data from the video analysis were further normalized by the mean of the respective controls within each experiment to obtain the fold-change information between treatments. The data were merged for analysis and plotting. 134 Mouth opening width In order to correlate the mouth opening behavior and neuronal activity, we measured the width of the mouth opening during GCaMP6S recording via automated tracking of the opposite edges of the mouth. This tracking was done using icy79 and the tracking plugin80. Afterwards tracks were manually cleaned, and missing links were integrated. Using the track manager with the integrated function “Distance profiler” the distances between the two different tracks were calculated in pixel. The tracks were then smoothed using the integrated ksmooth function in R76. As the mouth opening onset to analyze the time sequence of neuronal activation before mouth opening, the first increase in the slope was taken after the addition of GSH and where there is no decrease within a 20 sec window. GCaMP6S imaging acquisition and calcium traces extraction To analyze the neuronal activity during the eating behavior, we developed a system to record freely moving animals while adding GSH. The animals were placed in commercially available channel slides with a height of 0.2mm and a width of 5mm (Suppl. Fig. 6D.; Ibidi, cat# 80166). After an animal was placed in the channel sled, tubing was connected on both sides, and recording was started. GSH was added using a 1-ml syringe attached to one tube after 2-3min, depending on the behavior of the animal, and recording lasted for approximately 10min. GSH was only added when the animal stayed elongated and did not show contraction or somersaulting behavior. Imaging was performed using the Axio Vert. A1 (Zeiss) with the Colibri 7 as a light source (Zeiss) equipped with the fluorescence filter 38 HE (Zeiss), 5x and 10x Plan Apo objective, and the Axiocam 705 mono (Zeiss). Acquired videos were further processed with Zen Blue 3.4 (Zeiss) to 700x600px, 8-bit and aligned with the Fiji plugin Linear Stack Alignment with SIFT81. The aligned stacks were than used for tracing neurons as described by Lagach et al.82. Neurons were automatically traced in icy79 using the protocol “Detection and Tracking of neurons with emc2”82 with individually adjusted parameters depending on the population, magnification, and animal size. Afterwards the quality of the tracks was manually controlled, and missing links were added, or false tracks were removed. For N6 and N4 neurons, tracks were manually separated for the different neuronal sensory (-like) and ganglion cell types. 135 GCaMP6S trace analysis The raw traces were normalized to obtain the fluorescence change ΔF/F0 using the background fluorescence as F0. This background fluorescence was taken by selecting a frame without visible neuronal activity drawing the outline of the animal’s body column and calculating the mean grey therein via Fiji. For further analysis the mean activity of each population or neuronal type was taken with the standard deviation to the mean since it summarized all major events (suppl. Fig. 6). All visualization and normalization were done using customized scripts in R76. N3 and N4 spiking frequency was deconvoluted and analysed using either CASCADE83 and/or MATLAB’s (Mathworks) “findpeaks” function with manually adjusted parameters. In all experiments at least 4 animals were used. In Figure 3 A-C only, representative polyps were shown and the mean of the whole neuronal population with the standard deviation, for N4 and N6 divided into sensory and ganglion neurons. More replicates shown in the suppl. Fig. 3. The time sequence of activation of the nerve subpopulation before an opening of the mouth was determined by the time difference between the first activation of the first cell and the opening of the mouth. As the timepoint of first neuronal response, the first activation of the first single cell was taken (shown in Fig. 5AB). At least 7 animals pre transgenic line were taken. To find a difference in N6 between germ-free and monocolonized with Curvibacter or wildtype microbiota, the area under the curve (AUC) was compared. For this purpose, the mean value of the wildtype microbiota AUC was taken and the difference to the other treatments was calculated. At least 8 animals per treatment were used. GCaMP6S and mouth width analysis For calculation of positive or negative correlations between the mouth opening and the mean activity of the different neuronal subpopulations, the smoothed mouth width data were used. The visualization was done using R and the tidyverse package (Fig. 2A-C) 76,78. The mouth opening width was adjusted to the scale of the fluorescence change as stated on the right y-axis title. To perform linear correlation analysis, for N6 we compared the fluorescence changes and in case of N3 and N4 the frequency to the mouth opening width at the given time 142 B. N4 subpopulation activated by GSH shown with GCaMP6S. Here shown multiple animals undergoing the eating behavior. Calcium traces were split into sensory-like and ganglion neurons in the first row otherwise the response of the whole population is shown (mainly ganglion). The mean of each cell type population is shown with the standard deviation. In addition, the mouth opening is shown as mouth width over time (measured in pixel) adjusted to the fluorescence change. C. N3 subpopulation activated by GSH shown with GCaMP6S. Here shown multiple animals undergoing the eating behavior. Calcium traces were split into foot and head neurons in the first row otherwise the response of the whole population is shown. The mean of each cell type population is shown with the standard deviation. In addition, the mouth opening is included as mouth width over time (measured in pixel) adjusted to the fluorescence change in the first two plots. Supplement Figure 4. N3 and N4 synchronous firing. Neuronal populations N4 (A) and N3 (B) show synchronous firing activity in the whole population. A. Activity of N4 during the eating behavior. B. N3 during an activity episode before addition of GSH. 143 Supplement Figure 5. Cell ablation experiment. A. Schematic representation of the functioning of the NTR-MTZ system. B. Quantification of N6 GFP positive cells in vivo after 0h, 5h, 8h and 21h. After 8h till 21h almost all cells were lost. (Scale bar 200µm). C. Apoptotic GFP+ cell (Scale bar 10µm). D-F. Analysis of cell ablation of N4 neurons using antibody staining against GFP and RFamid. D. N4::GCaMP6S line with 10mM MTZ after >12h of incubation. E-F. N4::NTR-GFP with 10mM MTZ after >12h. E. Almost all GFP cells disappeared. F. Other neuronal populations are not affected. Here shown by RFamid positive cells (N6 and N7). G-I. Analysis of cell ablation of N3 neurons using antibody staining against GFP and RFamid. D. N3::GCaMP6S line with 10mM MTZ after >12h of incubation. E-F. N3::NTR-GFP with 10mM MTZ after >12h. E. Almost all GFP cells disappeared. F. Other populations are not affected. Here shown by RFamid positive cells (N6 and N7). Scale 100µm 144 Supplement Figure 6. Set up of recording animals for GCaMP6S and animal behavior. A-B. Experimental set up for GCaMP6S recordings. A. The inverse Zeiss microscope (Axio vert. A1, Zeiss). B. The chamber where animals were kept during recording with a technical drawing of the chamber (units in mm, Ibidi cat#80166). C-D. Experimental set-up for behavior analysis. D. The customized chamber for recording the behavior in the ablation and bacterial manipulation experiments. Technical drawing shown with units in mm. 145 Supplement Figure 7. Potential zones of contact between the ectodermal neuronal population N6 and the endodermal population N4. A-B. Regions of close contact in the head at the base of the hypostome, near the head-tentacle junctions. C. No similar structure or proximity observed between endodermal N4 and ectodermal N1. 146 Supplement Figure 8. Glutamate (ionotropic) receptor (NMDAR) and transporter in Hydra’s i-cell lineage (except gland cells). vGLUTs are mainly expressed in nematocytes (NC and NB). G007865 and G001571 are expressed mainly in neurons and the strongest in N3 (i_EC3N). Closest sequence to a NMDA receptor is expressed in N3, N4 and N6 (G023766, human to Hydra: e-value= 0.00). Here different nomenclature was used based on the paper by Cazet et al. 2023. 147 Supplement Figure 9. Effect of bacteria and Curvibacter on neuronal subpopulations. A.-C. Densities of neuronal subpopulations (wildzype microbiota, germ-free and Curvibacter). No significant difference was observed in the change of neuronal densities compared to wildtype (n = 10). D. Count of sensory cells which showed a GCaMP6S signal either without glutathione (-GSH) or with glutathione (+GSH). In Curvibacter, the tendency shows that more sensory neurons are show a GCaMP6S signal without glutathione (n= 4). E. Quantification of N6 ganglion, N6 sensory and cells between locations of ganglion and sensory cells (Intern; n= 8-13). F. Quantification of neurites between ganglion and sensory cells (quantification of a half of a head, numbers are not per neuron, n = 10) and number of neurites reaching into tentacles (ganglion-tentacle, n = 10). 148 References: 1. Cazet, J.F., Siebert, S., Little, H.M., Bertemes, P., Primack, A.S., Ladurner, P., Achrainer, M., Fredriksen, M.T., Moreland, R.T., Singh, S., et al. (2023). A chromosome-scale epigenetic map of the Hydra genome reveals conserved regulators of cell state. Genome Res, gr.277040.122. 10.1101/GR.277040.122. Supplement Table 1. Construct sequences and lines. Supplement Table 2. Curvibacter RNA Sequencing Raw reads, analyzed and annotated, related to Figure 7 Supplement Table 3 Hydra RNA Sequencing Raw reads, analyzed and annotated, related to Figure 6 Supplement Table 4 Statistical analysis of eating behavior and ablation of different neuronal subpopulations. 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