Various morphologic features of the ENS have been characterized (Furness, 2006). However, resolving the broad architecture of neurons in the gut with current imaging methods, primarily using immunohistochemistry (IHC), are limited by several physical obstacles of the GI tract: large area (~40cm long in a mouse), gut motility / tissue pliability (i.e., with or without luminal contents, relaxed vs. contracted smooth muscle), and 3-dimensional biogeography (i.e., longitudinal and cross-sectional analysis). Furthermore, the ENS is localized within two distinct plexuses of the GI tract and studies have often characterized myenteric neurons by microdissection of the longitudinal muscle / myenteric plexus tissue layer (Fried and Gulbransen, 2015), a manipulation of the tissue that disassociates axons from their destination in 3-dimensional space and overlooks the organization of submucosal neurons. To overcome these limitations, we labelled enteric neurons in living mice with recombinant adeno-associated viruses (rAAVs) expressing fluorescent proteins, and used tissue clearing techniques to enhance visualization of intact GI tissue (https://github.com/mazmanianlab/Yoo_et_al_2021/tree/main/ENS%20quantification). The AAV capsid variant AAV-PHP.S was used since it has been optimized for systemic delivery in mice (Deverman et al., 2016) and characterized for its increased tropism to the peripheral nervous system, including the ENS (Chan et al., 2017) . We first looked to compare the general infectivity of AAV-PHP.S to the traditionally-used AAV9, which has been empolyed in the past for viral delivery to the ENS (Benskey et al., 2015) . Similar to Chan et al., the cargo for both AAVs was the gene encoding a nuclear localized green fluorescent protein expressed under the strong, ubiquitous CAG promoter. Following a single intravenous (i.v.) systemic injection of the AAVs, AAV-PHP.S efficiently transduced cells throughout the GI tract and exhibited greater infectivity of cells throughout layers of the GI tract, compared to AAV9, as seen by increased cellular expression of nuclear localized CAG-GFP (Figure 1A). Gene products driven by the human Synapsin 1 (hSYN1) promoter have previously been shown to restrict expression to neurons (Jackson et al., 2016; Kügler et al., 2003) and minimize expression in peripheral targets such as the dorsal root ganglia (Haenraets et al., 2017). We packaged fluorescent proteins genes under the hSYN1 promoter into AAV-PHP.S, delivered them systemically, and found that ~90% (±1.5%) of cells in the ENS labelled with antibodies against Protein Gene Product 9.5 (PGP9.5), a pan-neuronal protein, co-localized with virally labelled neurons in the small intestines (SI) and colon (Figure 1B).

Efforts to study the circuitry and neurochemistry of the gut nervous system have been hampered by the physical length of the GI tract, making broad characterization of the ENS by IHC both technically challenging and costly. With AAV-mediated labeling, a single systemic injection of AAV-PHP.S:hSYN1-mNeonGreen at 1012 viral genomes (vg) was sufficient to label expansive regions of ENS as seen in the SI and colon Figures 1C, Figure 1 - Figure Supplement 1A and 1B). This minimizes the technical variability inherent to IHC staining of numerous tissue sections, and enables uniform imaging of millimeters of tissue at micron resolution across many samples. Pan tissue labeling revealed spatial diversity of the ENS, such as architectural differences of ganglia proximal and distal to the mesentery (insets in Figure 1C), helping orient images relative to each other and thus facilitating 3-dimensional tissue reconstruction. Indeed, coupling viral-labeling with tissue clearing methods, such as a sorbitolbased clearing method (ScaleS) (Hama et al., 2015; Treweek et al., 2015; Yang et al., 2014) , we were able to preserve the structure of intestinal tissue. Imaging intact tissue enabled a global view of the ENS including extrinsic nerve processes in the gut (Figure 1 - Figure Supplement 1A and 1B), how ENS architecture changes in association with Peyer’s patches (small intestinal lymphoid tissues) (Figure 1 - Figure Supplement 1C and 1D), and revealed different patterns of ENS ganglionic morphology when tissue is fixed with or without luminal contents (Figure 1 Figure Supplement 1E-1G).

When three separate viruses, each encoding a distinct fluorescent protein (PHP.S:hSYN1mTurquoise2, PHP.S:hSYN1-mNeonGreen, or PHP.S:hSYN1-mRuby2 [together AAVPHP.S:hSYN1-XFP]), are delivered together at a combined dose of 1012 vg, stochastic and combinatorial viral transduction of cells resulted in multi-color labelling of neurons. Labeling of neighboring cells with different colors demarcated cellular boundaries (Figure 1D) and morphologies (Figure 1E) much more clearly than IHC, and allowed unambiguous assignment of axons to specific cell bodies from which they emanate. This approach is also tunable by the design of genetic promoters for different cell types, for example labeling of enteric glial cell bodies with virally-expressed tdTomato driven by the glial fibrillary acidic protein (GFAP) promoter (Figure 1F).

Lastly, passive CLARITY (PACT) tissue clearing (Hama et al., 2015; Treweek et al., 2015; Yang et al., 2014) facilitated imaging of both myenteric and submucosal neurons within the same image stack captured by confocal microscopy (Figure 1G). This method allows direct imaging of the ENS in intact GI tissue via whole mount microscopy, eliminating the need to microdissect distinct layers of the GI tract and prepare thin sections that full tissue reconstruction challenging, and also reduces mechanically-introduced artifacts to tissue. Together, this cell labelling approach provides an effective and economical method to label the ENS at high resolution, with cell specificity, with spatial positioning throughout the tissue, and is widely deployable without the need for genetically-engineered mouse strains. While we demonstrate the cellular versatility of this method, the remainder of this study focuses on investigating neurons in the gut.

The small intestine and colon display similar, divergent, and synergistic functions (Hansen, 2003). However, it remains poorly understood how the cellular architecture of the ENS contributes to GI function, and broad, simultaneous characterization of the myenteric and submucosal plexuses has remained elusive (Foong et al., 2014; Sang and Young, 1998; Steele et al., 1991). Wild-type mice were transduced with AAV-PHP.S:hSYN1-XFP via a single i.v. injection to broadly label enteric neurons in the gut (Figure 2A). Viral infection was uniform throughout the SI and colon (Figure 2 - Figure Supplement 2A), outside of a small ~1.5cm section of the medial colon that, for unknown reasons, was consistently not well transduced and was therefore excluded from further analysis. Seven regions of the SI and two regions of the colon were sampled proximally to distally. Tissue regions were approximately 1cm in length and sampled every 2 to 3 cm. General ENS architecture was explored by quantifying the number of neurons and ganglia, as well as the size of ganglia (i.e., the number of neurons in each ganglion) in the myenteric and submucosal plexuses (Figures 2B-2G), normalized to the number of crypts to control for inherent variabilities within tissues. Importantly, no differences were found in crypt diameter along the GI tract (Figure 2 - Figure Supplement 2B). We saw that in the SI, the number of neurons and ganglia generally increased toward the distal portions of the myenteric plexus compared to proximal regions, while the converse was true for neurons and ganglia in the submucosal plexus (i.e., lower numbers in distal relative to proximal regions) (Figures 2B and 2C). Additionally, the size of the ganglia (i.e., the number of neurons per ganglia) increased in the distal regions of the SI myenteric plexus, a feature not observed in the submucosal plexus (Figure 2D). While neuronal numbers were similar in the proximal and distal regions of the colonic neural plexuses (Figure 2E), the number of myenteric ganglia increased (Figure 2F) while the size of each ganglion decreased in the distal colon (Figure 2G). Interestingly, submucosal neurons in the proximal colon were localized to the natural folds the tissue (Figure 2A, dashed lines in lower right panel), and these ganglia appear smaller in the distal colon. We conclude that combining viral labelling with tissue clearing enables exceptionally broad analysis of both myenteric and submucosal neurons throughout the gut, uncovering biogeographic features of ENS architecture in mice.

Existing approaches to measure intracellular calcium flux, a surrogate for neuronal firing, in the ENS include ex vivo preparations of intestinal tissue (Hennig et al., 2015) or surgically implanting an abdominal window to obtain in vivo ENS calcium recordings (Rakhilin et al., 2016; 2019). These techniques involve extensive manipulation of tissue to obtain access to the longitudinal muscle and myenteric plexus layer, require physical separation of the ENS from its extrinsic neural connectivity, and are technically challenging. We sought to develop a more tractable method to directly assess the activity of specific neurons in the ENS of live mice. A genetically-encoded calcium indicator, GCaMP6F (Chen et al., 2013) , driven by the CAG promoter was targeted to the ENS via systemic viral delivery with AAV-PHP.S. Mice were anesthetized, intestines removed from the peritoneum, and unstimulated calcium transients in the proximal colon were recorded in individual neurons by in vivo confocal microscopy (Figure 2H and Video Supplement 1). Neurons in myenteric ganglia were determined by their size and those that displayed robust changes in fluorescence intensity were chosen at random for analysis. Measuring neuronal calcium flux over time revealed an average spike rate of 29.1 spikes/minute with an interquartile range of 19.0 - 37.7 spikes/minute (min.=7.0 spikes/min., max.=63.0 spikes/min) (Figure 2I). Time between calcium spikes was calculated for each cell, and the average peak interval was 2.6 seconds with an interquartile range of 1.6 - 3.2 seconds (min.=1.0 seconds, max.=8.6 seconds) (Figure 2J). Heatmaps (Figure 2K) and traces (Figure 2L) constructed from GCaMP6F fluorescence intensities further display the heterogeneity of neuronal firing among and within cells (i.e., tonic vs. phasic, small vs. large fluorescence delta, duration of spike). These data highlight the heterogeneity of ENS activity and underscores the resolution of our method in achieving cell specific measurements of neuronal activity (https://github.com/mazmanianlab/Yoo_et_al_2021/tree/main/gcamp6f).

Subpopulations of neurons can be defined by the neurotransmitters they produce, and are often demarcated by the rate limiting enzymes required for neurotransmitter production. Choline acetyltransferase (ChAT) and tyrosine hydroxylase (TH) are the rate limiting enzymes in acetylcholine and catecholamine biosynthesis, respectively. In the GI tract, acetylcholine is a major excitatory neurotransmitter mainly studied for its role in motility and secretomotor function (Furness, 2012; Mittal et al., 2017). Although ChAT+ and TH+ neurons are intrinsic to the GI tract, they have yet to be systematically characterized and interrogated for their role(s) in GI physiology. We employed ChAT-Cre and TH-Cre animals, where the Cre-recombinase (Cre) is expressed under the control of each respective gene promoter. Technically, genetic constructs can be engineered to have the payload transgene in a double-floxed inverse orientation (DIO), such that the transgene is flipped and expressed (and protein translated) in a Cre-dependent manner; in other words, AAV’s deliver transgenes that are expressed in ChAT+ or TH+ neurons only.

Accordingly, we transduced ChAT-Cre or TH-Cre mice with AAV-PHP.S:hSYN1-DIO-XFP and cleared tissues using ScaleS to visualize these neuronal subpopulations. Remarkably, each neuronal population occupied spatially distinct layers of the GI tract, with ChAT+ neurons primarily localized to the myenteric plexus whereas TH+ neurons were more abundant in the submucosal plexus (Figure 3A). Upon quantification, there are more ChAT+ than TH+ neurons in all assayed regions of the SI (Figure 3 - Figure Supplement 3A), an increase largely due to higher cell numbers in the myenteric plexus (Figure 3B). However, there were more TH+ submucosal neurons in the small intestine compared to ChAT+ cells (Figure 3C), and this spatial disparity was accentuated when calculating the ratio of myenteric-to-submucosal neurons (Figure 3 - Figure Supplement 3B). Furthermore, densities of myenteric neurons increased distally in the SI of TH-Cre mice (Figure 3B, SI-1 vs SI-10/13/15, p < 0.01; SI-4 vs SI-10/13/15 p < 0.05), though still lower than neuronal numbers in ChAT-Cre mice. In the colon, the ratio of myenteric and submucosal neurons did not differ between ChAT+ and TH+ cells (Figure 3 Figure Supplement 3B). These data reveal that spatial localization of ChAT+ and TH+ neurons is distinct. Additional subsets of neurons in the gut can be mapped by this approach.

Proteins from the mouse, gut microbes, and diet converge and interact in the GI tract (Albenberg and Wu, 2014) . To determine effects of specific neuronal subset activation on the gut environment, we performed untargeted label-free proteomics on cell-free supernatants of cecal contents from ChAT-Cre and TH-Cre mice, with and without neuronal activation by DREADDs (https://github.com/mazmanianlab/Yoo_et_al_2021/tree/main/proteomics). Material from the cecum (a compartment at the juncture of the SI and colon in mice) were collected one hour following the final C21 treatment (see Figure 3E), and analyzed by liquid chromatography / tandem mass spectrometry (LC-MS / MS). Interestingly, the abundance of Niemann-Pick C1Like 1 protein (NPCL1) is increased in the cecum of activated ChAT-Cre mice. NPCL1 is expressed on the apical surface of enterocytes, and is an integral protein involved in the absorption of free cholesterol from the lumen, the precursor of bile acids (Jia et al., 2011). Goblet-cell related proteins also trended upwards, specifically Mucin-19 (MUC19) and Zymogen granule 16 (ZG16) (Figure 4A), a protein localized to secretory granules (Rodríguez-Piñeiro et al., 2013). Conversely, one of the most highly downregulated proteins was an aldehyde dehydrogenase (Q3U367) encoded by the Aldh9A1 gene, which is involved in the catalytic conversion of putrescine to gamma-aminobutyric acid (GABA) (Matsushima et al., 1986). Little is known about the role of GABA in the ENS. Pancreatic digestive enzymes were the most significantly upregulated proteins in cecal contents of ChAT-activated mice (Figure 4A). These include chymopasin (CTRL), chymotrypsinogen 1 (CTRB1), and pancreatic lipase related protein 2 (PNLIPRP2). Accordingly, network analysis of upregulated proteins revealed that KEGG pathways associated with digestion represent the majority of the network (Figure 4B). This novel finding is consistent with evidence that cholinergic, viscerofugal neurons send signals from the GI tract to other organs of the digestive system, including the pancreas (Furness et al., 2014).

Activation of TH+ neurons also altered the luminal proteome of the cecum. Interestingly, 88% (52/59) of the differentially abundant proteins (padj.<0.25, pnom<0.021) are distinct from those identified in activated ChAT-Cre mice (padj.<0.25, pnom<0.042). Also 90% of the cecal proteins in TH-Cre mice were upregulated (53/59) compared to only 18% of proteins in ChAT-Cre mice (20/112), suggesting that activation of distinct neuronal subsets is associated with largely distinct changes in GI function. We observed signatures of increased protein-protein interactions in cecal contents of activated TH-Cre mice, evidenced by more network nodes and connections (Figures 4C and 4D). When TH+ neurons are activated, Filamin B (FLNB) and spectrin beta chain, non-erythrocytic 1 (SPTB2) were two of the most significantly enriched proteins (Figure 4C), and both are associated with the intestinal brush border and membrane vesicles (Donowitz et al., 2007; McConnell et al., 2011) . Accordingly, vesicle-mediated transport was a major protein network we identified (Figure 4D) and relatedly, coatomer proteins also trended upward (COPA and COPB2) (Figure 4C). Other enriched protein interaction networks that emerged were associated with the immune system, metabolic pathways, and ribosomes (Figure 4D). For example, immune related proteins such as immunoglobulin A (IgA) (in ChAT-Cre) and IgG2b and complement component C3 (in TH-Cre) trended upward (Figure 4A and 4C), results that are in accord with the immune-related biological processes identified by gene expression after neuronal activation (see Figure 3).

Perhaps the most intriguing observation was the strong depletion of acidic mammalian chitinase (CHIA) upon activation of TH+ neurons (Figure 4C). Chitin is a natural polysaccharide that comprises a major component of fungal cell walls (Latgé, 2007). Intestinal chitinases are poorly studied in mice and CHIA has not been investigated for its role in regulating fungal populations in the gut. Because our samples contain host and microbial proteins, this result prompted us to search our dataset against microbial protein databases. Remarkably, the decrease in CHIA abundance following TH+ neuron activation was accompanied by a large bloom in the proportion of fungal-associated peptides in the microbiome (representing ~59% of peptides mapped to any microbe) (Figure 4E). In contrast, fungal peptides represented only ~0.4% of enriched peptides in the lumen of activated ChAT-Cre mice (Figure 4F). We propose that reduction of chitinase production by activated TH+ cells is directly associated with the observed dramatic increase in fungal proteins, and if true, would represent a novel circuit by which mice regulate fungal loads in the gut. Taken together, profiling the gut lumenal proteome upon neuronal activation revealed intriguing changes, with ChAT+ neurons preferentially impacting digestion and TH+ neurons preferentially involved in host-microbe interactions.

Given the observed changes in the gut proteome, including proteins of microbial origin, we reasoned that neuronal activation in the ENS may impact the composition and community structure of the gut microbiota. Accordingly, the microbiome was profiled via shotgun metagenomics from a longitudinal collection of fecal samples prior to and following ChAT+ or TH+ neuron activation (pre-C21 samples, and at days 1, 5, and 9 after initial C21 dose) and cecal contents (10 days after initial dose) (see Figure 3E) (https://github.com/mazmanianlab/Yoo_et_al_2021/tree/main/metagenomics). In ChAT-Cre mice, Faith’s phylogenetic diversity (i.e., alpha-diversity) decreased dramatically by day 9 in the feces and also in the terminal cecum sample (day 10) (Figure 5A), an outcome supported by the large number of microbial taxa that are less abundant in samples from ChAT+ neuron activated mice (Figure 4 - Figure Supplements 4 and 5). In contrast, TH-Cre mice displayed similar phylogenetic diversity after activation, though there is a trend toward decreased diversity from pre-C21 to day 5 that recovers by day 9 in the feces (Figure 5A). The composition of microbial communities was determined using weighted UniFrac (i.e., beta-diversity); distances were calculated and principle coordinate analyses (PCoA) were performed. There is separation between control and activated animal groups in the feces and cecal contents of ChAT-Cre mice, a shift that was largely absent in samples from TH-Cre mice (Figure 5B-5G). Interestingly, over the experimental time course, Verrucomicrobia were significantly enriched in activated ChATCre mice (Figure 5H). To uncover differentially abundant bacterial taxa, we used linear discriminant analysis effect size (LEfSe) (Segata et al., 2011) and then visualized the data into cladograms depicting the phylogenetic relationship of differentially abundant taxa (Figure 5I and 5K). Family level changes to cecal bacterial communities are displayed in Figures 5J and 5L for ChAT-Cre and TH-Cre mice, respectively. The bacterial species Akkermansia muciniphila appears to drive the increase in Verrucomicrobia in activated ChAT-Cre mice (Figure 5M) but not in TH-Cre mice (Figure 5N). A. muciniphila metabolizes host-derived mucus as a nutrient source (Derrien et al., 2004; Van Herreweghen et al., 2017), consistent with the increase in luminal mucin proteins (MUC19) and digestive enzymes in activated ChAT-Cre mice (see Figure 4A). A. muciniphila is implicated in many diseases, such as obesity (Everard et al., 2013; Plovier et al., 2017), multiple sclerosis (Cekanaviciute et al., 2017; Jangi et al., 2016) , and seizures (Olson et al., 2018). Various additional associations can be postulated for ENS – microbiome interactions from this dataset.

Beyond microbial species assignment, metagenomic analysis can reveal gene families and pathways that are differentially abundant in the microbiome. Only ChAT-Cre mice showed activation-mediated changes in beta-diversity of both gene families and pathways (Figures 5O5R), and these shifts occurred in the cecum and feces collected 9 days after activation. The most distinguishing features were highly represented in the control groups and downregulated in activated ChAT-Cre mice. The families and pathways that distinguish the groups (Figure 5 Figure Supplement 5C and 5D) were mainly associated with bacterial activity, such as nucleotide biosynthesis and metabolism, and protein translation and transport. The downregulation of these gene families and pathways in activated ChAT-Cre mice supports the corresponding decreases in bacterial alpha-diversity (see Figure 5A). We conclude that neuronal activation actively reshapes the gut microbiome at community-, species-, and geneticlevel resolutions, with considerable differences in effects between ChAT+ or TH+ neurons. Potential mechanisms that mediate influences by the ENS on gut bacterial composition include altered intestinal motility, regulation of immune response, control of mucus production, and shifts in metabolism / nutrient availability, among others.

Having characterized outcomes of neuronal activation on DNA (microbial metagenome), RNA (host transcriptome), and protein (host and microbial proteome), we reasoned that a major impact of these changes may manifest in the profile of gut metabolites. Mouse and microbial cometabolism represents an intimate and intertwined relationship with implications to health. Via untargeted metabolomics using LC-MS/MS, we assayed detailed molecular changes in cecal contents and feces following neuronal activation in the gut (https://github.com/mazmanianlab/Yoo_et_al_2021/tree/main/metabolomics). In ChAT-Cre and TH-Cre mice, we observed a strong, activation-dependent clustering of metabolome profiles in cecal samples taken one hour following the last C21 injection (Figures 6A and 6B). In longitudinal fecal samples of ChAT-Cre mice, we observed separation of metabolomes between control and activated groups by day 5 of C21 injection, a state that persisted until day 9 (Figures 6C-6F). In TH-Cre fecal samples, data were plotted on axes that minimized experimental bias from an apparent batch effect (see Methods), revealing differences between metabolomes of control and TH+ neuronal activation over time (Figures 6G-6J). Furthermore, there is clear separation in cecal metabolites in ChAT-Cre versus TH-Cre mice, upon activation (Figure 6 Figure Supplement 6A and 6B), and such separation occurs in the feces by Day 9 of the activation time course (Figure 6 - Figure Supplement 6C-6F). Thus, targeted activation of ChAT+ and TH+ neurons in the ENS strongly influences the gut metabolome.

The Global Natural Products Social Molecular Networking (GNPS) (Wang et al., 2016) tool is an open-access mass spectroscopy repository and analysis pipeline that we employed to visualize and contextualize our data. In the cecal contents of ChAT-Cre and TH-Cre mice, metabolic networks consisting of both annotated and unannotated molecules were revealed (Figures 6K and 6L). Annotated, non-networked metabolites were chosen for their differential abundance in either control or activated samples. Specifically, in TH-Cre mice, there were strongly increased non-networked metabolites in activated mice whose closest spectral matches are linoelaidic acid (ID: 626), oleanolic acid methyl ester (ID: 378), and coproporphyrin I (ID: 739). Metabolites that spectrally resemble xanthine (ID: 259), genistein (ID: 846), and trans-Ferulic acid (ID: 707) were decreased upon activation (Figure 6K and Figure 6 - Table Supplement 2). In both ChAT-Cre and TH-Cre mice, the molecular networks largely consisted of level 3 annotations (based on the Metabolomics Standards Initiative (MSI) (Sumner et al., 2007)) of compounds belonging to the bile acid molecular family and their conjugates, as well as unannotated analogs. Primary bile acids are host (mouse) derived chemicals from cholesterol biosynthesis, which are subsequently metabolized by gut bacteria into secondary bile acids (Aries et al., 1969; Sakai et al., 1980) . Interestingly, metabolites with their closest spectral match to the primary bile acid cholic acid (ID: 108, 114, 215, 219, 221, 224, 259) were significantly enriched in the cecum of activated ChAT-Cre mice (Figures 6L-6N and Table s2). Additional metabolites that closely resemble the spectra of tauro-conjugated primary bile acids trended upwards, such as taurocholic acid (ID: 234, 238) and taurohyocholic acid (ID: 235). Conversely, features with spectral matches to the family of secondary bile acids and bile acid metabolites such as ursodeoxycholic acid (ID: 13), deoxycholic acid (ID: 100), beta-hyodeoxycholic acid (ID: 1, 143) and 12-ketodeoxycholic acid (ID: 19, 138) were decreased in ChAT+-activated mice (Figures 6L-6N). These data suggest that activation of ChAT+ neurons may modulate primary bile acids secretion, either directly or indirectly, and/or their metabolism to secondary bile acids. While additional examples of host-microbial co-metabolism were detected (Table s2), the intriguing pathway alterations in bile acids following activation of ChAT+ neurons may have critical influences on various host physiologies and health. For example, bile acids and their metabolites have historically been studied for their essential role in digesting dietary fats and regulating the absorption of lipids (de Aguiar Vallim et al., 2013) and thus have been a pharmacological target for nonalcoholic fatty liver diseases (e.g., NASH) (Gottlieb and Canbay, 2019). Bile acids regulate intestinal immune responses (Fiorucci et al., 2018; Ichikawa et al., 2012; Vavassori et al., 2009) and the production of neurotransmitters by gut enterochromaffin cells (Yano et al., 2015). Expression of bile salt hydrolases and hydratases increases the fitness of both commensal and pathogenic bacteria (Begley et al., 2005; Delpino et al., 2007; Hofmann and Eckmann, 2006; Jones et al., 2008; Sannasiddappa et al., 2017) . Recently, taurocholate, a bile acid that induces expression of virulence genes in enteric pathogens, and its metabolism by the human microbiota has been linked to susceptibility and resistance to infection (Alavi et al., 2020) . These extensive and important functions attributed to bile acids had not previously been known to be regulated by ChAT+ neurons in the gut, a discovery that may have broad implications on nutrition, metabolism, immunity, neurotransmission, and infection.

We thank members in the Mazmanian’s laboratory for discussions throughout the research and critical reading of the manuscript. We thank Dr. Andres Collazo and Caltech’s Biological Imaging Facility for training access to microscopy capabilities and the Caltech Proteome Exploration Laboratory for access to LC-MS/MS capabilities. This research was funded in whole or in part by Aligning Science Across Parkinson’s ASAP-000375 through the Michael J. Fox Foundation for Parkinson’s Research (MJFF). For the purpose of open access, the author has applied a CC BY 4.0 public copyright license to all Author Accepted Manuscripts arising from this submission. This work was supported by grants to S.K.M. from the Heritage Medical Research Institute, Emerald Foundation, Caltech Center for Environmental and Microbial Interactions (CEMI), the National Institutes of Health (GM007616 and DK078938), and the Department of Defense (PD160030).

B.B.Y. and S.K.M. designed experiments. B.B.Y. performed experiments. J.A.G., K.Y.C., C.C. (supervised by V.G.) helped in AAV-mediated ENS characterization. J.A.G. performed analysis of GCaMP6F fluorescence. P.T.B (supervised by D.W.W.), M.J.S., A.M. performed acquisition and analysis of proteomic data. V.C., G.H., G.S., Q.Z., J.S., and (supervised by R.K.) performed acquisition and analysis of metagenomic data. K.W. (supervised by P.C.D.) performed acquisition and analysis of metabolomic data. T.M.T. assisted in animal-related work. B.B.Y. and S.K.M wrote the manuscript with input from all authors.

Table Supplement 1 - Gene Set Enrichment Analysis of Gene Ontology (GO) Terms (Related to Figure 3).

Gene set enrichment analysis of gene ontology (GO) terms that are upregulated in the distal SI and proximal colon of activated ChAT- and TH-Cre mice.

All mouse experiments were performed in accordance with the NIH Guide for the Care and Use of Laboratory Animals using protocols approved by the Institutional Animal Care and Use Committee at the California Institute of Technology. TH-Cre (from Viviana Gradinaru at Caltech. Originally from Ted Ebendal B6.129X1-Thtm1(cre)Te/Kieg) (Lindeberg et al., 2004) and ChATCre mice (Jackson Laboratories, Bar Harbor, ME- Stock# 028861) were obtained from Jackson Laboratories, and homozygous Cre-mice were bred to wild-type mice to yield male and female heterozygous Cre-mice used for our studies. Wildtype specific pathogen free (SPF), C57BL/6 (Jackson Laboratories, Bar Harbor, ME Stock #000664) males and females were used for breeding and experiments.

Virus was produced using methods described in Challis et. al. (Challis et al., 2019) Briefly, human embryonic kidney (HEK293T) cells were triple-transfected with pUCmini-iCAP-AAVPHP.S, pHelper Plasmid, and one of the following pAAV genomes (CAG-NLS-GFP, hSYN1tDTomato, GFAP-tDTomato, hSYN1-mRuby2, hSYN1-DiO-mRuby2, hSYN1-mNeonGreen, hSYN1-DiO-mNeonGreen, hSYN1-mTurquoise2, hSYN1-DiO-mTurquoise2, hSYN1-DiOhM3Dq-mRuby2, CAG-GCaMP6F). Cells were grown in DMEM + Glutamax + Pyruvate (Gibco, Gaithersburg, MD- Stock# 10569-010) + 5% FBS + non-essential amino acids (Gibco, Gaithersburg, MD- Stock# 11140-050), penicillin-streptomycin (Gibco, Gaithersburg, MDStock# 15070-063). Virus is precipitated from cells and supernatant with an 8% PEG solution (wt/vol), and purified by ultracentrifugation using 15%, 25%, 40%, 60% stacked iodixanol gradients.

Mice were anesthetized using 2% isoflurane. Virus was titered to 1012 VGs, resuspended to a volume of 100µl with sterile PBS, and injected retro-orbitally.

AAV-PHP.S:hSYN1-mNeonGreen was delivered systemically to WT mice at 6-8 weeks of age. 3-4 weeks following viral infection, 1cm of proximal, medial, and distal SI and colon were harvested and flash frozen in TRIzol (ThermoFisher Scientific, Waltham, MA- Cat. No. 15596018) and RNA was extracted per manufacturer’s instructions. Viral ssDNA is present in the RNA fraction. Contaminating RNA was eliminated with RNase treatment (ThermoFisher Scientific, Waltham, MA- Cat. No. AM228) per manufacturer’s instructions. Quantitative PCR was performed on viral DNA with primers against WPRE (Forward: 5’GGCTGTTGGGCACTGACAAT-3’; Reverse: 5’-CCGAAGGGACGTAGCAGAAG-3’) and values were normalized to ubiquitous mitochondrial gene, mtRNR2 (Forward: 5’CCGCAAGGGAAAGATGAAAA-3’; Reverse: 5’-TCGTTTGGTTTCGGGGTTTC-3’). Plotted relative abundances are mean values across three regions sampled in either SI or colon.

TH-Cre and ChAT-Cre mice are used for these experiments. “Activated” mice are infected with AAV-PHP.S:hSYN1-DIO-hM3Dq-mRuby2 and “Control Mice” are infected with AAVPHP.S:hSYN1-DIO-mRuby2. This is to control for both AAV-PHP.S-mediated expression and the effects of Compound 21 dihydrochloride (C21) (HelloBio, Princeton, NJ- HB6124). C21 was injected intraperitoneally at a dose of 3mg/kg in both activated and control mice. Mice for time course experiments were single-housed in sterile cages with autoclaved water following first C21 administration (C21 administered for 10 consecutive days). Injections of C21 were administered at the same time every day (10AM).

Tissue preparation, immunohistochemistry, Imaging, and Quantification 100mg/kg of pentobarbital (Euthasol - Virbac, Carros, France) was administered IP, and tissues were perfused with 30mL of phosphate buffer solution (PBS) and next with cold 4% paraformaldehyde (PFA) in PBS. GI tract was post-fixed in 4% PFA overnight at 4oC, and stored in PBS + 0.025% sodium azide. Tissues that underwent subsequent immunohistochemistry were made transparent by use of the passive CLARITY technique (PACT) (Treweek et al., 2015) . Briefly, perfused and fixed tissues were embedded with polymerized 4% (wt/vol) acrylamide, and lipids were eliminated using 8% (wt/vol) SDS solution. Tissues were blocked in 3% donkey serum and permeabilized with PBS + 0.3% Triton (PBST). Primary antibodies were incubated in PBST for 48 hours and washed with PBST for 24 hours (replacing wash 3 times). Tissues were next incubated in secondary antibodies (and DAPI) for 24 hours and washed in PBS for 48 hours, intermittently replacing the wash solution with fresh PBS. Primary antibodies used were rabbit PGP9.5 (1:300; Millipore Sigma AB1761-I) and chicken GFAP (1:500, BioLegend Cat #829401). Secondary antibodies used were goat anti-chicken Alexa 647 (Life Technologies A-21450) and donkey anti-rabbit Alexa 568 (Life Technologies A10042). Tissues imaged just for virally expressed, endogenous fluorescence were made transparent using sorbitol-based optical clearing method, ScaleS (Hama et al., 2015). Tissues were mounted in respective mounting mediums (RIMS and Scales S4), on a glass slide with a 0.5mm spacer (iSpacer, SunJin Lab Co.). Images were acquired on Zeiss LSM 780 or 880, and microscope, laser settings, contrast, and gamma remained constant across images that were directly compared. All confocal images were taken with the following objectives: Fluar 5× 0.25 M27 Plan-Apochromat 10× 0.45 M27 (work- ing distance 2.0 mm) and Plan-Apochromat 25× 0.8 Imm Corr DIC M27 multi- immersion. Neurons in each ganglion were counted by counting cells that are of a distinct color. Large intestinal ganglia were defined as distinct if separated by a width of 3 or fewer neurons.

AAV-PHP.S-CAG-GCaMP6F (1012 VGs) was delivered systemically to WT C57. 3-4 weeks after infection, mice were anesthetized with 2% isoflurane on a heating pad (Kent Scientific, Torrington, CT- DCT15) with plastic sleeve covers (Kent Scientific, Torrington, CT- DCT1520P). The abdominal cavity was surgically opened to expose the intestines. The proximal colon was identified, and this portion was placed on top of a stack of 4-6 glass microscopy slides (depending on size of animal) (VWR, Radnor PA- Cat. No. 48300-026). Tissue was secured onto glass slide with a biorthogonal silicon elastomer (Kwik-Sil) (World Precision Instruments, Sarasota, FL- KWIK-SIL), and a glass coverslip. Elastomer stiffens within 1 minute of application and coverslip must be held steadily to ensure a flat imaging surface. Anesthetized mouse is placed under an upright confocal microscope (Zeiss LSM 880). Using a 10X objective, GCaMP6F fluorescence was taken at 5Hz (1 image every 200ms). Periods of movement of tissue and luminal contents is normal during live imaging. Cell tracking was performed in 2D by using TrackMate ImageJ plugin (https://imagej.net/TrackMate) and fluorescence intensity was recorded from cells. Average background was determined by taking the fluorescence of a region of interest that did not contain a cell, over the duration of the video. Background was subtracted from cell fluorescence intensities.

Data sets of fluorescent values recorded at a rate of 0.206 s from GCaMP6F-expressing unstimulated neurons in the myenteric plexus of the proximal large intestine and analyzed with the MLspike software for Matlab downloaded from GitHub (https://github.com/MLspike/spikes) (Deneux et al., 2016). MLspike determines a new baseline to subtract from the raw fluorescence data to allow accurate modelling. The software uses a version of the Viterbi algorithm to obtain the most probable spike train. From the model, fluctuating baseline, model-estimated spike train, and most probable spike times were extracted. Polynomial coefficients (p2) were changed from 0.5 to 0.55 and (p3) from 0.01 to 0.03 for GCaMP6F fluorescence as recommended (Deneux et al., 2016). The minimum range for baseline (bmin) was changed from 0.7 to 0.5 as determined by observed fluorescence values.

Small intestinal tissue was quickly harvested from ChAT-Cre mice, flushed and placed in oxygenated (95% O2, 5% CO2), ice cold Krebs-Henseleit solution for 1 hour followed by 15 min at room temperature. A segment was cut along the mesenteric attachment, and pinned flat (mucosa facing down) on a Sylgard-lined recording chamber (Warner Instruments, PH1) in oxygenated Krebs-Henseleit solution. C21 was added at 10nM and GCaMP6F fluorescence was detected on an upright microscope (Zeiss, Oberkochen, Germany- Examiner D1).

Tissue collection and RNA extraction Mice were cervically dislocated and the GI tract was removed. 1cm of tissue above and below the cecum were dissected and cleaned to represent tissue from the distal SI and proximal colon, respectively. Tissue was homogenized in TRIzol (ThermoFisher Scientific, Waltham. MA- Cat. No. 15596018) solution using bead-based homogenizing methods, and total RNA was extracted using chloroform per manufacturer’s instructions.

Library Preparation, Sequencing, and Analysis The cDNA libraries were prepared using the QuantSeq 3’mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen, Greenland, NH) supplemented with UMI (unique molecular index) as per the manufacturer’s instructions. Briefly, total RNA was reverse transcribed using oligo (dT) primers. The second cDNA strand was synthesized by random priming, in a manner that DNA polymerase is efficiently stopped when reaching the next hybridized random primer, so only the fragment closed to the 3’ end gets captured for later indexed adapter ligation and PCR amplification. UMIs were incorporated to the first 6 bases of each read, followed by 4 bases of spacer sequences. UMIs are used to eliminate possible PCR duplicates in sequencing datasets and therefore facilitate unbiased gene expression profiling. The basic principle behind the UMI deduplication step is to collapse reads with identical mapping coordinates and UMI sequences. This step helps increase the accuracy of sequencing read counts for downstream analysis of gene expression levels. The processed libraries were assessed for its size distribution and concentration using Bioanalyzer High Sensitivity DNA Kit (Agilent Technologies, Santa Clara, CA- Cat. No. 5067-4626 and -4627). Pooled libraries were diluted to 2 nM in EB buffer (Qiagen, Hilden, Germany, Cat. No. 19086) and then denatured using the Illumina protocol. The libraries were pooled and diluted to 2 nM using 10 mM Tris-HCl, pH 8.5 and then denatured using the Illumina protocol. The denatured libraries were diluted to 10 pM by pre-chilled hybridization buffer and loaded onto an Illumina MiSeq v3 flow cell for 150 cycles using a single-read recipe according to the manufacturer's instructions. Single-end 75bp reads (max 4.5M reads) were obtained. De-multiplexed sequencing reads were generated using Illumina BaseSpace. UMI specific workflows that were developed and distributed by Lexogen were used to extract reads that are free from PCR artifacts (i.e., deduplication). First, the umi2index tool was used to add the 6 nucleotide UMI sequence to the identifier of each read and trims the UMI from the start of each read. This generates a new FASTQ file, which is then processed through trimming and alignment. Second, after the quality and polyA trimming by BBDuk (https://jgi.doe.gov/dataand-tools/bbtools/bb-tools-user-guide/bbduk-guide/and alignment by HISAT2 (version 2.1.0) (Kim et al., 2015) , the mapped reads are collapsed according to the UMI sequence of each read. Reads are collapsed if they have the same mapping coordinates (CIGAR string) and identical UMI sequences. Collapsing reads in this manner removes PCR duplicates. Read counts were calculated using HTSeq (Anders et al., 2015) by supplementing Ensembl gene annotation (GRCm38.78). Raw read counts were run through ShinySeq to obtain differentially expressed genes and downstream gene ontology analyses (Sundararajan et al., 2019). 2018). Tandem mass specta were matched to peptide sequences using the ProLuCID/SEQUEST (1.4) (Xu et al., 2015; 2006) software package. The validity of spectrumpeptide matches were assessed using the SEQUEST-defined parameters XCorr (crosscorrelation score) and DeltaCN (normalized difference in cross-correlation scores) in the DTASelect2 (2.1.4) (Cociorva et al., 2007; Tabb et al., 2002) software package. Search settings were configured as follows: (1) 5ppm precursor ion mass tolerance, (2) 10ppm fragment ion mass tolerance, (3) 1% peptide false discovery rate, (4) 2 peptide per protein minimum, (5) 6006000Da precursor mass window, (6) 2 differential modifications per peptide maximum (methionine oxidation: M+15.994915 Da), (7) unlimited static modications per peptide (cysteine carbamidomethylation: C+57.02146 Da), and (8) the search space included half- and fully tryptic (cleavage C-terminal to K and R residues) peptide candidates with unlimited (mouse database, custom metagenomic shotgun database) or 2 missed cleavage events (ComPIL 2.0). Differential Analysis of Detected Proteins using Peptide-Spectrum Matches (Spectral Counts) Detected proteins were grouped by sequence similarity into "clusters" using CD-HIT 4.8.1 (Fu et al., 2012; Li and Godzik, 2006; Li et al., 2001) at the following similarity cut-offs: 65%, 75%, 85%, and 95%. The following is an example command line input: "cd-hit -i fastafile.fasta -o outputfile -c 0.65 -g 1 -d 0". Tandem mass spectra identified as peptides (peptide spectrum matches, PSMs) were mapped to CD-HIT generated clusters. PSMs mapping to >1 cluster were discarded. Cluster-PSM tables were generated and differential analysis was performed in DESeq2 (1.25.13) (Love et al., 2014). Briefly, count data (PSMs) were modeled using the negative binomial distribution, and the mean-variance relationship was estimated. Variance was estimated using an information sharing approach whereby a single feature's (or cluster's) variance was estimated by taking into account variances of other clusters measured in the same experiment. Feature significance calling and ranking were performed using estimated effect sizes. Multiple testing correction was performed by Benjamini-Hochberg method within the DESeq2 package. Volcano plots were generated in Prism (GraphPad).

Differential Analysis of Detected Proteins using Ion Intensity (Precursor Intensity) Detected proteins were grouped into "clusters" by sequence similarity using CD-HIT 4.8.1 (Fu et al., 2012; Li and Godzik, 2006; Li et al., 2001) at the following similarity cut-offs: 65%, 75%, 85%, and 95%. The following is an example command line input: "cd-hit -i fastafile.fasta -o outputfile -c 0.65 -g 1 -d 0". Using the Census software package (Park et al.; 2008) (Integrated Proteomics Pipeline 6.5.4), peptide ion intensities were calculated from .ms1 files. Peptide ion intensities were assigned to their parent peptide, then parent peptides were mapped to their appropriate CD-HIT generated clusters. Ion intensities belonging to parent peptides that map to >1 CD-HIT cluster were discarded. Cluster-ion intensity tables were generated.

Ion intensity data were analyzed using the DEP package (Zhang et al., 2018) operating in R. Intensity values were automatically Log2 transformed in DEP. The cluster list was subsequently filtered with the 'filter_proteins' function such that clusters with missing values above a 65% threshold were discarded. Remaining intensities were further transformed by the 'normalize_vsn' function (Huber et al., 2002). Missing data in remaining clusters were imputed using a mixed approach. Clusters where either the control or treatment group contained only null entries were classified as 'missing not at random' (MNAR) and imputed with 0 values. All other groups were treated as 'missing at random' (MAR) and imputed using the maximum likelihood method ('MLE') (Gatto and Lilley, 2012). Note that for a given cluster, missing values for treatment groups were imputed separately by treatment group. Differential expression analyses were performed on filled-in cluster-ion intensity tables using the 'test_diff' function (Ritchie et al., 2015) and multiple testing correction was performed using the 'add_rejections' function.

Upregulated proteins with a nominal p-value<0.2 were searched against protein-protein interactions in the STRING database (http://www.string-db.org) where high confidence interactions were selected for. Briefly, the STRING database sources protein-protein interactions from primary databases consisting of genomic context predictions, high-throughput lab experiments, (conserved) co-expression, automated textmining, and pervious knowledge in databases (Szklarczyk et al., 2019).

Upregulated tryptic, microbial peptide sequences, with fold change and nominal p-value cutoffs of 2 and p<0.2 respectively, were input into Unipept (http://unipept.ugent.be) (Gurdeep Singh et al., 2019; Mesuere et al., 2015), equating leucine and isoleucine and filtering duplicate peptides. Briefly, Unipept indexes tryptic peptide sequences from the UniProtKB and details peptides with NCBI’s taxonomic database. Lowest common ancestor is calculated for each tryptic peptide.

Fecal Collection AAV-PHP.S:hSYN1-hM3Dq-mRuby2 (1012 VGs) was delivered systemically to TH-Cre and ChAT-Cre mice. 3-4 week after infection, C21 (3mg/kg) was administered daily for 10 consecutive days. Fecal pellets were collected in sterile containers one day before initial C21 dose, and between doses thereafter.

Fecal sample DNA extraction and Library Preparation DNA was extracted with the Qiagen MagAttract PowerSoil DNA kit as previously described (Marotz et al 2017) . Our standard protocol is optimized for an input quantity of 1 ng DNA per reaction. Prior to library preparation, input DNA is transferred to a 384-well plate and quantified using a PicoGreen fluorescence assay (ThermoFisher, Inc). Input DNA is then normalized to 1 ng in a volume of 3.5μ L of molecular-grade water using an Echo 550 acoustic liquidhandling robot (Labcyte, Inc). Enzyme mixes for fragmentation, end repair and A-tailing, ligation, and PCR are prepared and added in approximately 1:8 scale volumes using a Mosquito HV micropipetting robot (TTP Labtech). Fragmentation is performed at 37 °C for 20 min, followed by end repair and A-tailing at 65 °C for 30 min.

Sequencing adapters and barcode indices are added in two steps, following the iTru adapter protocol (Glenn et al., 2019). Universal adapter “stub” adapter molecules and ligase mix are first added to the end-repaired DNA using the Mosquito HV robot and ligation performed at 20 °C for 1 h. Unligated adapters and adapter dimers are then removed using AMPure XP magnetic beads and a BlueCat purification robot (BlueCat Bio). 7.5 μL magnetic bead solution is added to the total adapter-ligated sample volume, washed twice with 70% EtOH, and then resuspended in 7μ L molecular-grade water.

Next, individual i7 and i5 are added to the adapter-ligated samples using the Echo 550 robot. Because this liquid handler individually addresses wells, and we use the full set of 384 unique error-correcting i7 and i5 indices, we are able to generate each plate of 384 libraries without repeating any barcodes, eliminating the problem of sequence misassignment due to barcode swapping (Costello et al., 2018; Sinha et al., 2017). To ensure that libraries generated on different plates can be pooled if necessary, and to safeguard against the possibility of contamination due to sample carryover between runs, we also iterate the assignment of i7 to i5 indices each run, such that each unique i7:i5 index combination is only repeated once every 147,456 libraries. 4.5μ L of eluted bead-washed ligated samples is added to 5.5μ L of PCR master mix and PCR-amplified for 15 cycles. The amplified and indexed libraries are then purified again using magnetic beads and the BlueCat robot, resuspended in 10μ L water, and 9μ L of final purified library transferred to a 384-well plate using the Mosquito HV liquidhandling robot for library quantitation, sequencing, and storage. 384 samples are then normalized based on a PicoGreen fluorescence assay.

Shallow shotgun metagenome sequencing and diversity analysis The Illumina data for each HiSeq lane was uploaded to Qiita, a tool with standardized pipelines for processing and analyzing metagenomic data (Gonzalez et al., 2018). Adapter sequences were removed from the reads using the Atropos v.1.1.15 (Didion et al., 2017) command (from the qp-shogun 0.1.5 pipeline) and the trimmed sequences were downloaded from Qiita. The reads for each sample were filtered of any potential mouse contamination using Bowtie2 v.22.2.3 (Langmead and Salzberg, 2012). The filtered reads were then aligned to the Web of Life (WoL) reference phylogeny (Zhu et al., 2019) with Bowtie2 using an adapted SHOGUN pipeline (Hillmann et al., 2018). The WoL contains 10,575 bacterial and archaeal genomes with each genome representing an OTU. Sequencing reads that did not map to a single reference genome as well as reads that mapped to multiple genomes were not included in the analysis. If an OTU had a relative abundance less than 0.01% in a given sample, the OTU was not included for that sample. Additionally, OTUs with fewer than 5 assigned reads were not considered. The samples were rarefied to a depth of 12,750 reads and those with fewer than the rarefaction depth were excluded. The QIIME2 v.2019.7 (Bolyen et al., 2019) DEICODE plugin was used to calculate the Aitchison distances, a compositional beta diversity metric, and perform Robust Aitchison PCA to create biplots that visualize relationships between features and samples (Martino et al., 2019). The QIIME2 diversity plugin was used to calculate the other alpha- and beta-diversity metrics used in this study.

Metagenomic based functional profiling The filtered reads were also analyzed using HUMAnN2 v2.8.1 (Franzosa et al., 2018) to establish functional profiles for the samples. HUMAnN2 is a pipeline that begins by using MetaPhlAn 2 to compile custom databases of reference genomes based on the species detected in a sample (Truong et al., 2015) . HUMAnN2 then maps the filtered onto these custom databases and the reads that do not map to any of the references are then subjected to a translated search against UniProt Reference Clusters or UniRef (Suzek et al., 2007). Here, the UniRef90 database was used for the translated search and installed according to the HUMAnN2 documentation. The results from both the search performed using the custom reference genome database and the search against the UniRef90 database were combined and the gene families identified in each sample were reported in units of read per kilobase (RPKs) to account for gene length. HUMAnN2 also compared the gene families found in a sample with the MetaCyc pathways database (Caspi et al., 2018) and output a table reporting the pathway abundances found in each sample. After rarefying gene family tables to a depth of 166,000 RPKs and using a depth of 22,600 for pathway abundances, the QIIME2 diversity and DEICODE plugins were used to calculate alpha- and beta-diversity metrics.

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