Neuropeptide SP protects against colitis and linked anxiety-like behavior through the putative roles of gut microbiota and metabolite inositol

Domain: neuroscience
Published: 2026-01-08
Editorial outcome: major_revision

Abstract
The gut-brain axis links gut inflammation to psychiatric symptoms in inflammatory bowel disease (IBD), but the underlying mechanisms remain unclear. We demonstrate that neuropeptide substance P (SP) alleviated intestinal injury and behavioral disorders induced by dextran sodium sulfate in mice. SP mitigated hippocampal neuroinflammation and inhibited microglial activation and astrocyte loss. Furthermore, SP improved gut microbiome dysregulation, and its protective effects depended on the putative roles of microbiota. Notably, through modulating microbiota, SP dampened the NF-κB pathway in microglia, and increased GABAergic/Ca2+ signaling within astrocytes. SP elevated the microbiota-derived metabolite inositol. Supplementing inositol mimicked SP’s benefits and activated GABAergic signaling, while the inositol inhibitor reversed SP’s neuroprotective impacts, highlighting inositol’s indispensable role. Collectively, SP exerts beneficial effects via microbiota’s putative roles and inositol, involving the suppression of microglial NF-κB pathway while enhancing astrocytic GABAergic/Ca²⁺ signaling. Our findings underscore SP’s potential as a therapeutic intervention for these disorders in IBD.

Introduction
Inflammatory bowel disease (IBD), which comprises ulcerative colitis and Crohn’s disease, is a complex chronic and disabling illness that elicits gastrointestinal inflammation throughout life1. Recent research has found a strong association between the symptoms of intestinal lesions in IBD and psychiatric comorbidities2. Clinically, the occurrence of anxiety and depression is up to 30% and 25% in IBD patients3. Psychological disorders in IBD patients not only negatively affect the quality of life but also deteriorate the severity of the disease in suffering4. Regrettably, the mental health challenges associated with IBD tend to be given less attention than the intestinal manifestations of the disease. Despite the precise etiology behind the development of IBD and the related psychiatric comorbidities is complex, and not yet incompletely understood.

Accumulating evidence has suggested the composition of the gut microbiome not only impacts intestinal health but also controls brain physiological function through the gut-brain axis, which permits the bidirectional communication between the gut microbiome and the central nervous system (CNS), highlighting the significance of the microbiota-gut-brain (MGB) axis in neuroscience5,6. Imbalance of the gut microbiome may contribute to CNS impairments. Recent studies have illustrated that disrupting the intestinal microbiota of colitis mice can significantly impact the gut-brain axis, leading to neuroinflammation and brain pathology7. Transferring the microbiota of healthy individuals to the gastrointestinal tract of IBD patients alleviated gut inflamed symptoms and mental impairment8. Crucially, the metabolites produced by the gut microbiota have been reported to mediate host-microbiota interactions, showing the potential to regulate host physiology via the MGB axis9. However, little is known about how DSS-induced alterations in the gut microbiota and their derived metabolites impact centrally mediated systems implicated in mood, warranting further research.

The growing body of evidence indicates that gut microbiome imbalances can compromise intestinal barrier integrity, allowing the translocation of inflammatory molecules and noxious products generated by pathogenic bacteria from the gut lumen into the systemic circulation. These factors can then disseminate to the brain, thereby directly eliciting neuroinflammatory signaling cascades10,11. Neuroinflammation can alter the normal levels and interactions of key neurotransmitters in the brain, such as serotonin (5-HT), dopamine, and gamma-aminobutyric acid (GABA), which are important for regulating mood and behavior12,13. Undoubtedly, microglia, recognized as crucial players in neurodegenerative disorders, become persistently activated under inflammatory stimuli, leading to the production of neurotoxic mediators, including cytokines14. Therefore, a deeper understanding of the specific biomolecular pathways and signaling cascades that mediate the influence of the gut microbiome on neuroinflammation would be essential to advance the field of research on mood disturbances. Additionally, as the most abundant cells within the brain, astrocytes were activated and transited into reactive astrocytes post-CNS injury15. Astrocytes are implicated in the remodeling of the CNS through their dual roles. For another, reactive astrocytes secrete a diverse array of pro-inflammatory cytokines, and chemokines16. For one thing, they release neurotrophic factors, maintain synaptic neurotransmitter levels, and transmit information through gap junctions and calcium waves17–19. Interestingly, the latest research has suggested that a specific subgroup of astrocytes, which are regulated by the gut microbiome, exhibit a protective anti-inflammatory function within the brain. It cannot be denied that the current body of work on anxiety has so far centered on hippocampal neurons and neural circuits linked to the disorder. In contrast, the action mechanism of hippocampal astrocytes in anxiety has not been sufficiently addressed. Regardless, these findings provide evidence that gut microbiota has been recognized as a pivotal regulator between gut and brain signal transduction. Targeting the modulation of the intestinal microbiota and of the produced metabolites seems to be a very potential approach for the treatment or prevention of dysbiosis-driven disorders.

Over the past few years, various researchers have reported that neurotransmitters, known as a crucial mediator in the crosstalk between the gut and brain, possess the ability to affect the intestinal microbial community20. Neuropeptides such as substance P (SP) are generated at all levels of the MGB axis, and they contribute substantially to the bidirectional signaling processes occurring between the gut and brain21. In principle, the gut microbiota would be expected to exhibit a response to neuropeptides, if it were to possess the corresponding receptor21. Nonetheless, there remains a lack of direct evidence supporting the involvement of SP in the communication between microbial communities within the gut and the CNS. SP has been implicated in the modulation of various psychiatric disorders. On one hand, several studies indicate SP’s involvement in the pathophysiological processes. For example, Schwarz et al. reported that SP concentration is elevated under stress, and that pharmacological antagonism or genetic depletion of NK-1 receptors results in an anxiolytic response, suggesting SP as a potential therapeutic target for treating stress and anxiety-related disorders22. On the other hand, the neuroprotective effects of SP have garnered significant attention. SP has been shown to promote memory, reinforce learning, and exhibit anxiolytic-like effects when administered systemically or directly into the nucleus basalis of the ventral pallidum23. Furthermore, SP may counteract the neurotoxicity of beta-amyloid protein, a key factor in the neuropathology of Alzheimer’s disease, and could also have beneficial effects in the context of Parkinson’s disease and its related models24. Moreover, Ahn et al. demonstrated that SP mitigates functional impairments resulting from a partial 6-hydroxydopamine lesion of the nigrostriatal system and provides neuroprotection in ischemic stroke by regulating M2 microglia polarization25. Our previous work has demonstrated the ability of SP to improve DSS-induced acute colitis, showing that SP counteracted the inflammatory response and ferroptosis triggered by DSS exposure26. It also remains to be investigated whether and how SP alters the gut microbial impacts on the intestinal barrier dysfunction and CNS in anxiety-like symptoms in IBD.

In this study, we investigated the protective effects of SP against dextran sodium sulfate (DSS)-induced pathological features of colitis, as well as hippocampal neuroinflammation and behavioral disorders in mice. We explored the ability of SP to improve microbial dysbiosis and further verified the putative role of gut microbiota and microbiota-derived metabolite inositol in SP’s beneficial impacts on intestinal impairment and anxiety-like behavior through fecal microbiota transplantation (FMT), antibiotic treatment, and inositol supplementation. Importantly, we demonstrated that the neuroprotective activity of SP was achieved through the suppression of the NF-κB pathway within microglia and the enhancement of GABAergic neurotransmission and downstream Ca2+ signaling within astrocytes in the hippocampus.

Results
SP ameliorates colitis symptoms and anxiety-like behaviors in DSS-induced mice
In this study, we used DSS treatment to successfully induce colitis mouse model which is a widely used chemically induced model resembling human ulcerative colitis27. As shown in Fig. 1a, mice were given DSS through their drinking water and were administrated intravenously with SP. In order to assess the role of SP in the pathogenesis of colitis, the weight, rectal bleeding, and stool consistency of the mice were monitored. Supplementation of SP remarkably mitigated the gross symptoms of colitis (Fig. 1b–d and Supplementary Fig. 2a). As shown in Fig. 1e, the colon tissue of normal mice was observed without inflammatory cell infiltration or mucosal erosion. In contrast, mice in the DSS group displayed intestinal mucosal and submucosal edema, severe inflammatory cell infiltration, crypt loss, and epithelial injury. The histological scores of the DSS-treated mice were significantly higher than those of the normal mice. Administration with SP showed protective effects on the colon, with intact crypts and mucosal epithelial cells, and no significant edema or inflammatory cell infiltration observed in the submucosa. Consistently, histological scores were significantly decreased by SP administration compared to the DSS-treated group. Crypt depth was markedly declined in the DSS group, but this was alleviated following SP administration (Supplementary Fig. 2b). As illustrated in Fig. 1f, transmission electron microscopy (TEM) analysis revealed intact intestinal epithelium in the CON group, with neatly arranged microvilli. The DSS group exhibited irregularities in the colonic epithelium, characterized by widened intercellular spaces as well as shortened and curated microvilli. Conversely, SP treatment effectively mitigated these changes. Prior research has indicated that mucin and goblet cells serve as the basic lines of defense to safeguard the gut28. We explored the role of SP on goblet cells in the colon during colitis. Acidic mucous substances were stained lake blue using Alcian Blue (AB), while neutral mucous substances were stained purple blue with Periodic Acid-Schiff (PAS)29. As illustrated in Fig. 1g, AB-PAS staining revealed that positively stained goblet cells in the colonic mucosa were densely distributed on both sides of the crypts, exhibiting a regular and full shape in the normal group of mice. In contrast, the DSS group exhibited only a few crypts remaining in the lamina, leading to a notable decrease in the number of positively stained goblet cells that contain mucus. However, following treatment with SP, the loss of goblet cells was remarkably alleviated. Mucin 2 (MUC-2), produced by goblet cells, is a major component of the colonic mucus barrier30. Immunofluorescent staining revealed that the secretion of MUC-2 in DSS-induced colitis was significantly decreased compared to the CON group. Conversely, an increase in MUC-2 expression was observed following SP administration (Fig. 1h).

Increased intestinal permeability allows foreign substances to enter and compromises the integrity of the barrier function31. We next assessed intestinal permeability by measuring serum levels of FITC-dextran. The serum levels of FITC-dextran were markedly elevated in DSS-induced mice compared to those in the CON group; however, this elevation was mitigated by SP administration (Fig. 1i). The results indicate that SP effectively repaired the impairment of the intestinal barrier and decreased the permeability of the intestinal epithelium. Previous study has shown that excessive pro-inflammatory factors are associated with the destruction of the intestinal epithelium32. We further measured the levels of inflammatory cytokines in the colon tissues using Luminex multiplex assays (Fig. 1j). The amounts of TNF-α, IL-1β, IL-6, IFN-γ and IL-12P70 were dramatically higher in the DSS group than in the CON group. Of particular note, after SP administration, levels of TNF-α, IL-1β, IL-6, IFN-γ and IL-12P70 were reduced. Intriguingly, no obvious clinical symptoms or histopathologic changes were observed in the SP alone-treated group compared to the CON group. These data suggest that SP could preserve the integrity of the intestinal barrier and alleviate colonic inflammation.

There is a growing body of evidence suggesting that anxiety-like behaviors are commonly observed in patients with IBD, but the impact of SP on IBD-linked behavior abnormalities has not been substantiated. In the present study, we employed the open-field test (OFT), elevated plus-maze (EPM), elevated zero maze (EZM) and light-dark box (LDB) to assess the effects of SP on anxiety-like behaviors in DSS-induced mice. In the OFT test, the total distance in the entire area, as well as the time spent and distance moved in central region in the DSS group was dramatically less than that in the CON group, demonstrating that DSS remarkably reduced the autonomous activity and exploration desire of mice. However, SP significantly reversed this phenomenon (Fig. 1k, l). Moreover, the potential anxiolytic-like activity of SP, as assessed by the EPM in mice, is presented in Fig. 1m, n and Supplementary Fig. 3a. The results demonstrated that the DSS group exhibited decreased percentage of distance traveled and time spent in the open arms, along with fewer percentage of open-arm entries and reduced total distance traveled, compared to the CON group. In contrast, SP treatment effectively reversed DSS-induced behavioral alterations. As a modification of the EPM, EZM is used to assess anxiety-like behaviors33. We examined the anxiolytic effects of SP by the EZM test. As shown in Fig. 1o, p and Supplementary Fig. 3b, the percentage of distance traveled and time spent in the open zones, the percentage of times entering the open zones and total distance traveled in the DSS group were significantly lower than those in the CON group. However, the DSS+SP group showed significantly greater percentage of distance traveled and time spent in the open zones, percentage of times entering the open zones and total distance traveled compared to the model group. In the LDB test, compared to the CON group, mice exposed to DSS exhibited lower percentage of distance traveled and percentage of time spent in the light box, indicating that DSS exposure decreased the exploration desire of the mice, along with fewer total distance traveled. Conversely, SP treatment increased the percentage of distance traveled in the light box, percentage of time spent in the light box and total distance traveled (Fig. 1q and Supplementary Fig. 3c). Additionally, the mice could freely move between the light and dark boxes, with the number of passages through the door shown in Fig. 1q. However, no differences in the transition frequencies were observed among the four groups. Notably, no statistically significant differences were observed between the CON group and the SP alone-treated group across all behavioral analyses. Collectively, these data suggest that SP protects against anxiety-like behaviors of mice with colitis.

Considering that potential malaise-induced by the inflammation might affect the anxiety behavioural readout, we assessed the anxiety-like behavior when colonic inflammation was eliminated. As shown in Supplementary Fig. 4a-c, following DSS withdrawal (day 15), the DAI and colonic tissue structure in the DSS and DSS+SP groups were recovered to the levels of the normal group. To further, we examined the anxiolytic effects of SP using the OFT and EPM test. In the OFT test, less time was spent and a shorter distance was traveled in the central area in DSS-treated mice compared with the CON group. In contrast, SP remarkably improved these behavioral changes (Supplementary Fig. 5a-c). In the EPM test, the percentage of open-arm entries, as well as the percentage of time spent and distance traveled in the open arms were significantly decreased in the DSS group than the CON group. However, SP profoundly reversed these alterations (Supplementary Fig. 5e-h). Notably, no significant differences in the total distance traveled were observed among the three groups in the OFT and EPM test, indicating locomotor activity was recovered (Supplementary Fig. 5d, i). These results indicate that anxiety-like behavior associated with colitis is persistent, even after the potential discomfort induced by intestinal inflammation has resolved; meanwhile, SP exerts anxiolytic effects.

SP mitigates hippocampal damage and neuroinflammation in DSS-induced mice
Hippocampal impairment can affect the function of the emotional regulation center, which may lead to psychiatric disorders, such as depression and anxiety34,35. Therefore, H&E staining has been utilized to evaluate morphological changes in the hippocampus of each group. As shown in Fig. 2a, b, significant pathological changes, such as neuronal loss, nuclear condensation, and neuronal shrinkage, were observed in the DSS group. However, cell bodies were clearly visible, and appeared continuous and uniform, without obvious disruptions in the DSS+SP group. Notably, no apparent pathological changes were observed between the SP treatment alone and the CON group. Nissl staining was performed to analyze the potential effects of SP on the neuronal cell distribution. The results showed that the DSS group exhibited a large loss of Nissl bodies and disorganized and loosely arranged cells within the DG region of the hippocampus. In contrast with the DSS group, SP markedly attenuated the neuronal damage caused by DSS, helping to preserve neuronal morphological structure and the number of Nissl bodies. The neuronal density and distribution within the CA1 and CA3 regions did not exhibit any obvious changes among the four groups (Fig. 2c, d). The neuroinflammatory response has been demonstrated to aggravate hippocampal impairment and disrupt the important balance of neurotransmitters, thereby affecting emotional regulation and causing anxiety-like disorders13,36. Here, to explore the role of SP in mitigating neuroinflammation, we investigated the concentrations of inflammatory mediators in the hippocampal tissue (Fig. 2e, f). The results showed that SP significantly decreased the levels of IL-1 beta, TNF-alpha, IL-9, IFN-gamma, IL-13, IL-6, G-CSF, GM-CSF, MIP-1 bata, and IL-12P70, but elevated the levels of IL-4, IL-5, and IL-10 in the hippocampus of DSS-treated mice. These results suggest that SP mitigates DSS-induced hippocampal neuroinflammation by modulating the balance between pro-inflammatory cytokines and anti-inflammatory factors.

Previous studies have demonstrated the activation of microglia in response to neuroinflammation37. In addition, astrocytes also have the capacity to exhibit anti-inflammatory and neuroprotective activities in the context of neuroinflammation like in Parkinson’s disease or multiple sclerosis38. To investigate how SP affect microglia/astrocytes, we counted the two cell types in the hippocampus of each group. As illustrated in Fig. 2g, h, immunofluorescence analysis was performed for DG and CA1 regions, and the results showed the number of Iba-1-positive microglia cells was elevated in the DSS group compared to the CON group; however, the SP group exhibited a significant downregulation in the Iba-1-positive cells. By contrast, an increase in the astrocyte marker GFAP-positive cells was observed in the SP group compared to the DSS group (Fig. 2i, j). The M1 phenotype of microglia is closely associated with the promotion of neuroinflammation. Activated M1 microglia secrete pro-inflammatory mediators to exacerbate neuronal damage and disrupt the homeostatic function of the CNS39. To elucidate the characteristic phenotype of activated microglia, the expression of M1-associated marker iNOS was analyzed by western blot. As shown in Fig. 2k, l, the protein level of iNOS was markedly increased in DSS-treated mice compared with the mice from the CON group, whereas supplementation with SP reversed this alteration. Concurrently, the western blot data showed results consistent with the immunofluorescence analysis, revealing increased protein expression of GFAP after SP treatment (Fig. 2k, l). It can be reasonably inferred that SP ameliorated the anxiety-like behavior in colitis mice by dampening pathological lesions and neuroinflammation in the hippocampus, which might be related to suppressing the polarization of microglia towards the M1 phenotype and preventing astrocytes loss.

SP ameliorates gut microbial dysbiosis and alters microbiome functions in DSS-induced mice
To evaluate whether SP could cross the blood-brain barrier and directly affect the brain, we assessed the expression of SP in the hippocampus among different groups using ELISA. We found no significant differences in the concentration of SP among the CON, DSS, and DSS+SP groups (Supplementary Fig. 6a). Additionally, SP is produced by enzymatic cleavage of the precursor protein (perprotachykinin-A). RT-qPCR analysis revealed that, following SP administration, the mRNA expression of TAC1 encoding preprotachykinin-A in the hippocampus of the DSS+SP group did not differ significantly from that in the DSS group (Supplementary Fig. 6b). These results suggest that administration of SP could not effectively cross the blood-brain barrier to exert its effects on the brain.

Emerging research has established a strong link between alterations in the gut microbiome and anxiety-related conditions. To investigate whether SP could mitigate the microbiota imbalances induced by DSS exposure, the composition of gut microbiota was analyzed using 16S rRNA gene sequencing. We evaluated the alteration in α-diversity using the Shannon index. Compared with DSS induction, SP did not appear to have a significant impact on the richness and diversity of intestinal flora (Fig. 3a). To fully assess the β-diversity of microbial communities, the overall structure of gut microbiota was defined using a principal 1 analysis (PCoA) based on the binary Jaccard distance (Fig. 3b). Significant differences in β-diversity were observed across the CON, DSS, and DSS+SP groups (PERMANOVA, p=0.001), indicating that the bacterial community structures were significantly different. At the phylum level, compared with the CON group, the DSS group showed enrichments in Bacteroidetes, and Proteobacteria, whereas compared with the DSS group, the DSS+SP group exhibited a higher relative abundance of Firmicutes (Fig. 3c). At the family level, the relative abundance of Bacteroidaceae and Enterobacteriaceae was elevated in the DSS group compared to that in the CON group, while the relative abundance of Lachnospiraceae and Muribaculaceae was significantly increased in the DSS+SP group, compared with that in the DSS (Fig. 3d). At the genus level, the relative abundance of Bacteroides and Alistipes was reduced, and that of unclassified_Lachnospiraceae, unclassified_Muribaculaceae and Lachnospiraceae_NK4A136_group was up-regulated in the DSS+SP group, compared with those in the DSS group (Fig. 3e). The random forest analysis predicted that g_Unclassified_Christensenellaceae and g_Parasutterella could be prominent bacterial classification, which had a significant impact on the differences between groups (Fig. 3f). One-way analysis of variance (ANOVA) was performed to compare the relative abundance of dominant species among the three groups. At a phylum level, a significantly higher abundance of p_Firmicutes was observed in SP-treated mice compared to DSS-treated mice. DSS exposure provoked a substantial escalation in the abundance of p_Bacteroides, which was also dramatically reversed by SP (Fig. 3g). Meanwhile, at the genus level, g_Bacteroides and g_Escherichia_Shigella were notably reduced in abundance in the SP group compared to the DSS group, whereas g_unclassified_Muribaculaceae, g_Prevotellaceae_UCG_001, g_Candidatus_Arthromitus as well as g_unclassified_Lachnospiraceae were remarkably more abundant in the SP group (Fig. 3h, i). Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) analysis was used to predict intestinal microbial metabolic function. Compared with the CON group, several functional modules in the DSS group (e.g., Pentose phosphate pathway, Biofilm formation-Vibrio cholerae, Glycerolipid metabolism, Quorum sensing, Photosynthesis) were found to be suppressed (Fig. 3j). In contrast, the DSS+SP group exhibited increased activity in functional modules, such as Nucleotide excision repair, Inositol phosphate metabolism, Methane metabolism, Bile secretion, NOD-like receptor signaling pathway, Epithelial cell signaling in Helicobacter pylori infection and Mineral absorption) compared to the DSS group (Fig. 3k). Overall, these data suggest that the administration of SP protected against DSS-induced gut microbiota dysbiosis in mice.

Additionally, we investigated the regulatory effects of SP on the composition of gut microbiota using 16S rRNA sequencing analysis of colonic content in non-colitis mice. Notably, the α-diversity analysis, as assessed by the Shannon index, revealed no significant differences in bacterial richness and diversity between the two groups (Supplementary Fig. 7a). The overall structure of the gut microbiota, investigated using binary Jaccard-based PCoA, indicated that the SP group displayed a shift in the clustering of bacterial composition, which was distinct from the CON group, suggesting that SP treatment induced changes in the gut microbiota composition (Supplementary Fig. 7b). We then analyzed the gut bacterial composition at the genus level. Results revealed that SP significantly reduced the relative abundance of Limosilactobacillus and Bacteroides relative to control mice. Conversely, the abundance of unclassified_Muribaculaceae, Lachnospiraceae_NK4A136_group, and unclassified_Lachnospiraceae was significantly increased in SP-treated mice compared to those in control mice (Supplementary Fig. 7c). The differences in the relative abundance of gut microbiota at the genus level between the CON group and the SP group were analyzed using ANOVA (Supplementary Fig. 7d).

SP protects against intestinal damage and anxiety-like disorders induced by DSS through modulating the gut microbiota
Given our evidence that SP effectively regulates gut microbiota to alleviate colitis, we probed further into the putative role of gut microbiota in mitigating colitis and anxiety-like behaviors following SP treatment. To this end, antibiotic treatment on mice for 14 days was carried out to eliminate the gut microbiota, as depicted in Fig. 4a. At the end of the ABX treatment (on day 14), the addition of antibiotics dramatically depleted the microbiota under aerobic conditions (Fig. 4b, c). Following DSS treatment (on day 21), the microbiota remained significantly reduced compared to the normal group, although a slight increase was observed. Additionally, fecal DNA analysis revealed that the fecal DNA from mice treated with antibiotics (on day 14) and continuously treated with DSS for 6 days (on day 21) was lower than that of the normal mice (Supplementary Fig. 8a). Antibiotic-induced perturbation of the microbiota can disrupt intestinal homeostasis and compromise the integrity of intestinal defenses40. As expected, the significant enlargement of the cecum was consistent with the typical characteristics observed in pseudo-germ-free (PGF) mice compared to those in untreated mice (Fig. 4f). These results indicate that the administration of the antibiotic cocktail disrupted microbiota composition and effectively depleted gut microbiota by days 14 and 21, suggesting that a PGF mouse model was successfully established.

Subsequent analysis indicated that antibiotic intervention had no significant effect on the body weight of healthy mice (Supplementary Fig. 8b). Strikingly, the beneficial effects of SP on clinical symptoms, such as DAI (Fig. 4d), body weight (Fig. 4e), colonic length (Fig. 4f and Supplementary Fig. 9a), spleen/body weight ratio (Fig. 4g and Supplementary Fig. 9b), and intestinal structural integrity (Fig. 4h-k and Supplementary Fig. 9c), were notably weakened in the absence of the intestinal microbiome in mice. Consistently, anxiety-like behaviors improved by SP treatment was completely reversed after gut microbiota depletion (Fig. 4l-m). These results indicate that gut microbiota might exert effects in SP’s protection against colitis and anxiety-like behavior.

To further confirm the putative role of gut microbiota in SP’s protective effects, we transplanted the feces of CON, DSS, and DSS+SP donor mice into recipient mice for one week (Prior to FMT, all recipient mice were pretreated with antibiotics for 14 days). As shown in Fig. 5a, the FMT-DSS+SP group, which received feces from the DSS+SP group showed less body weight loss (Fig. 5b), lower DAI (Fig. 5c), relieved the colon shortening (Fig. 5d, e) and decreased spleen/body weight ratio (Supplementary Fig. 10a, b) when compared to the FMT-DSS group, which received feces from the DSS group. As illustrated in Fig. 5f, the mice subjected to FMT-DSS induction exhibited pronounced histological damage, featuring epithelial impairment, crypt loss, and inflammatory cell infiltration, but these pathological changes were notably ameliorated following FMT-DSS+SP treatment, as evidenced by lower histological scores and increased crypt depth (Supplementary Fig. 10c). AB-PAS staining revealed that in the process of colitis, the reductions in acidic and neutral mucin abundances, as well as the quantity of goblet cells, were largely restored to normal levels through FMT from SP-treated donor mice (Fig. 5g, h). To assess the effects of FMT-DSS+SP treatment on cytokines, we measured the concentrations of inflammatory factors in the colon using ELISA. Elevated levels of TNF-α and IL-6 were observed in the colon of the FMT-DSS group; however, these levels were attenuated following FMT-DSS+SP treatment (Fig. 5i). Moreover, myeloperoxidase (MPO), an enzyme found in neutrophils, serves as a common index for inflammation, with increased MPO activity indicating greater neutrophil infiltration in the colon41. FMT-DSS treatment resulted in increased MPO activity compared to FMT-CON mice, while FMT-DSS+SP treatment significantly reduced MPO activity (Fig. 5i).

We investigated the influence of FMT from DSS+SP-treated mice on the behavioral characteristics of mice with colitis. To begin, an open field test was performed to assess their anxiety-like behavior. Shortened distance traveled in the center area, less time spent in the center area and shortened total distance moved were shown in mice with colitis treated by FMT-DSS compared with the FMT-CON group. However, FMT-DSS+SP treatment profoundly improved these behavioral changes in the FMT-DSS group (Fig. 5j, k). Also, H&E staining was conducted to validate the histopathological alterations observed in the hippocampus. As shown in Fig. 5l, cell degeneration and damaged nuclei or nuclear shrinkage were augmented in the FMT-DSS mice compared to the FMT-CON group in DG and CA3 regions. All of these observed effects were alleviated through FMT-DSS+SP treatment. In Fig. 5m, the mRNA levels of inflammatory cytokines such as TNF-α and IL-1β in the hippocampus exhibited an obvious upward trend in the DSS group, and the mRNA expression of IL-4 was down-regulated, but FMT-DSS+SP treatment was able to impede the alterations induced by FMT-DSS. Besides, no significant difference was observed in the mRNA expression of IL-6 between the FMT-DSS and FMT-DSS+SP groups. Similarly, western blot analysis showed that the protein levels of iNOS and CD80 were significantly down-regulated in the hippocampus of the FMT-DSS+SP group when compared to the FMT-DSS group, however, FMT-DSS+SP treatment significantly suppressed the FMT-DSS-induced decrease in the protein expression of CD206 (Fig. 5n and Supplementary Fig. 11a-c). Additionally, FMT from DSS+SP-treated mice had a significant upregulation in the protein expressions of GFAP compared with FMT from DSS-treated mice, implying FMT-DSS+SP treatment could inhibit the decline of astrocytes induced by FMT-DSS treatment (Fig. 5n and Supplementary Fig. 11d). To clarify the characteristic phenotype of the activated microglia in the hippocampus, the co-localization of the iNOS (M1-associated marker) or CD206 (M2-associated marker) with Iba-1 was analyzed using double-labeled immunofluorescence staining, respectively. The data showed that after FMT-DSS treatment, the number of iNOS-immunopositive (iNOS+) cells increased in the hippocampus, including the CA1 and DG regions. However, the numbers were significantly lower in the FMT-DSS+SP group than in the FMT-DSS group (Fig. 5o and Supplementary Fig. 11e). As well, we observed CD206-immunopositive (CD206+) microglia were few after FMT-DSS treatment, whereas compared with the FMT-DSS group, the FMT-DSS+SP group showed a dramatic increase in the number of CD206+ microglia (Fig. 5p and Supplementary Fig. 11f).

The donor microbiota was sourced from the CON, DSS, and DSS+SP groups, of which the composition of the gut microbiota was shown in Fig. 3. To investigate the microbiota reconstruction in the recipient groups, we analyzed the gut microbiota in the colonic content of FMT-CON, FMT-DSS, and FMT-DSS+SP mice using 16S rRNA gene sequencing analysis. Firstly, the Shannon index, indicative of intestinal flora richness, showed an increase in the diversity of intestinal microbiota in the FMT-DSS+SP group, compared to the FMT-DSS group, although no significant differences were observed between groups (Supplementary Fig. 12). PCoA using binary Jaccard distances demonstrated that the gut microbiota from these three groups was distinctly separated, indicating differences in microbial composition (Fig. 5q). As seen in Fig. 5r, FMT-DSS+SP treatment increased the abundance of p_Firmicutes and p_Bacteroidetes while decreasing the abundance of p_Proteobacteria in comparison with the FMT-DSS group. Furthermore, FMT-DSS+SP treatment resulted in an increase in the abundances of g_unclassified_Muribaculaceae and g_unclassified_Lachnospiraceae, while the relative abundances of g_Bacteroides and g_Ligilactobacillus were reduced compared to those in the FMT-DSS group (Fig. 5s). ANOVA was conducted at the genus level to identify the species for relative abundance boxplot analysis, allowing for comparison of the dominant gut microbiota that differed significantly among the three groups. Compared to the FMT-DSS group, the relative abundances of unclassified_Muribaculaceae, unclassified_Lachnospiraceae, and Lactobacillus were elevated, while the abundances of unclassified_Bacillaceae, uncultured_proteobacterium, Ligilactobacillus and Bacteroides were reduced in the FMT-DSS+SP group (Fig. 5t). Therefore, FMT from mice treated with DSS+SP effectively improved gut microbiota imbalance. Taken together, gut microbiota plays an important and putative role in SP’s protection against colitis, hippocampal damage, and anxiety-like disorders.

SP affects the expression of hippocampal genes by modulating the gut microbiome, involving the NF-κB and GABAergic/Ca2+ signaling pathways
Through modulating the gut microbiome, SP reduces pathological injury and neuroinflammatory responses in the hippocampus, thereby improving anxiety-like behavior, but the potential action mechanism remains unclear. To explore how SP affects the function of the hippocampus mediated via intestinal flora, we performed RNA-seq analysis on the hippocampus of mice subjected to FMT treatment. Principal component analysis (PCA) showed that there were significant differences in the transcriptomes among three groups with four independent replicates (Supplementary Fig. 13a). Compared with the FMT-CON group, 436 differently expressed genes (DEGs) were identified in the FMT-DSS group, of which 271 were upregulated and 165 were downregulated. Furthermore, 387 significantly altered genes were identified in the FMT-DSS+SP vs. FMT-DSS groups. Among them, 82 genes were upregulated, and 305 genes were downregulated by FMT-DSS+SP treatment (Supplementary Fig. 13b). A Venn diagram revealed that there were 436 FMT-DSS-regulated DEGs between FMT-DSS and FMT-CON groups, 387 SP-regulated DEGs between FMT-DSS+SP and FMT-DSS groups, and 158 genes that were co-regulated by the treatment of FMT-DSS and FMT-DSS+SP (Supplementary Fig. 13c). The gene expression profile in the hippocampus comparing FMT-DSS to FMT-CON, or comparing FMT-DSS+SP with the FMT-DSS group, were presented in a hierarchical clustered heatmap (Fig. 6a, b). The volcano plot also revealed similar results in the FMT-DSS vs. FMT-CON groups and in the FMT-DSS+SP vs. FMT-DSS groups (Supplementary Fig. 13d, e). To gain a deeper understanding of these signaling differences, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed. Results showed the notable enrichment in a series of signaling pathways (top 20 enriched), including upregulated or downregulated alterations, in the FMT-DSS vs FMT-CON (Fig. 6c, d) as well as in the FMT-DSS+SP vs FMT-DSS (Fig. 6e, f). Of note, the NF-κB signaling pathway was activated in the FMT-DSS group, which could be dramatically reversed by FMT-DSS+SP administration. RT-qPCR analysis demonstrated the mRNA expressions of IKBα and p65 were upregulated in the hippocampus of the FMT-DSS group compared to the FMT-CON group, however, treatment with FMT-DSS+SP notably reversed these processes (Fig. 6g). Western blotting analysis demonstrated the NF-κB pathway molecules, including p-p65 and p-IKBα, were upregulated in the hippocampus of the FMT-DSS group compared to the FMT-CON group, however, treatment with FMT-DSS+SP notably reversed these alterations (Fig. 6h and Supplementary Fig. 14a, b).

Remarkably, KEGG pathway analysis also revealed that the neuroactive ligand–receptor interactions and Ca2+ signaling pathway were significantly activated in the FMT-DSS+SP group. Among the neuroactive ligand-receptor interaction signaling, ligand-gated channels, such as gamma-aminobutyric acid receptor (GABAR), are permeable for calcium upon activation42. Likewise, the mRNA expressions of Gabra1, Gabra3, Gabrb2 and Camk2d genes were decreased in the FMT-DSS group compared with the FMT-CON group, but FMT-DSS+SP treatment significantly suppressed the reduction of these genes in the hippocampus (no significant change in the Gabrg2 gene was observed) (Fig. 6i). Consequently, the increased protein expression of GABA Rα1, GABA R2, GAD65 (a key enzyme responsible for the synthesis of GABA), and CaMKII (a sensor of calcium concentration change) in hippocampus tissues of the FMT-DSS+SP group was further confirmed by western blotting assay (Fig. 6j and Supplementary Fig. 14c-f). Additionally, the UALCAN database was used to investigate the potential involvement of NF-κB pathway and GABAergic/Ca2+ signaling in patients with COAD. Our analysis revealed that there was a significant increase in the mRNA levels of key NF-κB-related genes (Supplementary Fig. 15a-e), and the mRNA expressions of genes in the GABAergic/Ca2+ signaling pathway were decreased (Supplementary Fig. 15f-l).

Microglia, as key players in the inflammatory signaling process, are associated with the NF-κB signaling pathway, which is crucial for regulating inflammation43. Excitingly, SP has been shown to prevent the decrease in the number of astrocytes induced by DSS. Astrocytes are known to perform many important functions that support neurons and maintain brain health44. We hypothesized that the effects of FMT treatment on the NF-κB and GABAergic/Ca²⁺ signaling pathways may be closely linked to changes in microglia and astrocytes. To investigate this, we performed double-immunofluorescence staining to test the p-IKBα signaling within Iba1+ microglia in the CA1 and DG regions of the hippocampus. As shown in Fig. 6k and Supplementary Fig. 14g, administration of FMT-DSS significantly increased the number of p-IKBα+ microglia in the hippocampus of mice. In contrast, p-IKBα+ microglia were dramatically lower in the FMT-DSS+SP mice than in the FMT-DSS mice. GABA Rα1 immunoreactivity was observed in astrocytes, and FMT-DSS+SP treatment dramatically elevated the expression of GABA Rα1 within astrocytes (Fig. 6l and Supplementary Fig. 14h). Using double-immunofluorescence staining, the co-localization of the CaMKII with astrocyte marker GFAP was analyzed. Similarly, in alignment with the results of western blotting, the number of CaMKII+ astrocytes was down-regulated in the FMT-DSS group, which could be reversed by FMT-DSS+SP treatment (Fig. 6m and Supplementary Fig. 14i). To gain a more precise understanding of the molecular mechanisms involved in microglia and astrocytes, we isolated these cells from hippocampal tissue by flow cytometry sorting. As illustrated in Supplementary Fig. 16a, microglia were identified as CD11b+CD45low, and astrocytes were identified as ACSA2+. The collected microglia and astrocytes were subjected to separate RNA sequencing analyses. A heatmap depicted the differentially expressed genes in the NF-kappaB signaling pathway in the microglia, revealing that genes enrichment in this pathway was evident from the FMT-DSS group but absent in the FMT-DSS+SP group (Fig. 6n). KEGG pathway analysis of differentially expressed genes showed a downregulation of pro-inflammatory pathways, including the NF-kappaB signaling pathway, the TNF signaling pathway, and cytokine-cytokine receptor interactions, in the microglia of mice treated with FMT-DSS+SP compared to those treated with FMT-DSS (Supplementary Fig. 16b). Gene set enrichment analysis (GSEA) analysis indicated that the gene set associated with the NF-kappaB signaling pathway was significantly down-regulated in the FMT-DSS+SP group compared with FMT-DSS group (Fig. 6n). Additionally, the RNA-Seq analysis of astrocytes from mice treated with FMT-DSS+SP revealed broad enrichment of genes in neuroactive ligand–receptor interaction and calcium signaling pathway compared to those from mice treated with FMT-DSS (Fig. 6o). Notably, KEGG pathway analysis revealed that neuroactive ligand–receptor interaction and GABAergic synapse were significantly enriched in the astrocytes of the FMT-DSS+SP group (Supplementary Fig. 16c). GSEA also revealed a significant down-regulation of the gene set in neuroactive ligand–receptor interaction in the FMT-DSS group compared to the FMT-CON group, however, the FMT-DSS+SP group exhibited an up-regulation of the gene set related to neuroactive ligand-receptor interaction compared to the FMT-DSS group (Fig. 6o). Overall, these results suggest that the suppression of the NF-κB pathway in microglia, coupled with the activation of GABAergic and downstream Ca2+ signaling in astrocytes, is responsible for SP’s anti-neuroinflammatory and anti-anxiety activities mediated by the gut microbiota.

SP augments the enrichment of gut microbiota-derived metabolite inositol
Considering the interplay between the intestinal microbiota and host metabolism, as well as the role of microbiota-derived metabolites in gut-brain communication45, an untargeted metabolomic analysis was subsequently carried out on samples that had previously undergone 16S rRNA gene sequencing. To further verify the differences among samples from three groups, the score plot of partial least squares discriminant (PLS-DA) analysis was performed. There was an obvious separation among the CON, DSS, and DSS+SP groups, and the DSS+SP group was closer to the CON group than to the DSS group, indicating that SP significantly mitigated DSS-induced dramatic alteration of metabolites (Fig. 7a). A total of 980 metabolites were identified in the DSS and CON groups, whereas 395 metabolites were identified in the DSS+SP and DSS groups. A total of 144 metabolites were identified in the three groups (Fig. 7b). The results of volcano plot showed that compared to the CON group, 352 metabolites were up-regulated and 628 down-regulated in the DSS group. However, 215 metabolites were increased, and 180 metabolites declined in the DSS+SP group vs the DSS group in mouse colonic contents (Fig. 7c, d).

KEGG pathway enrichment of the differential metabolites showed that Amino acid metabolism (Arginine and proline metabolism, and Cysteine and methionine metabolism), Biosynthesis of other secondary metabolites (Neomycin, Kanamycin and gentamicin biosynthesis, Betalain biosynthesis, Biosynthesis of various antibiotics and Caffeine metabolism) and Carbohydrate metabolism (Ascorbate and aldarate metabolism, Amino sugar and nucleotide sugar metabolism, Galactose metabolism and Inositol phosphate metabolism) were the main pathways based on KEGG analysis (Fig. 7e). Specifically, heatmap analysis was performed to observe the relative expression of these differentially expressed metabolites in the three groups; the top five most abundant metabolites in the DSS+SP group, compared with the DSS group included myo-Inositol, 6-O-Methylguannine, Threoninyl-Gamma-glutamate, Diplodiatoxin, and Tacrine (Fig. 7f). Enrichment analysis of the elevated metabolites in the DSS+SP group was conducted through KEGG database. Data indicated that Inositol phosphate metabolism, O-Antigen nucleotide sugar biosynthesis, Galactose metabolism, Vitamin B6 metabolism and Pentose and glucuronate interconversions were the top five enrichment pathways, and of these, inositol phosphate metabolism was remarkably changed (Fig. 7g). Notably, we further identified and screened some evidently changed metabolites in the DSS+SP group vs the DSS group, as shown in Fig. 7h, i. Among them, N-oleoyl GABA and tacrine were enriched in the biosynthesis of neurochemicals. In particular, inositol was the most significantly increased metabolite in colonic contents in response to SP administration, and was enriched in a variety of metabolic processes, involving biosynthesis of antibiotics, inositol phosphate metabolism, ascorbate and aldarate metabolism, and galactose metabolism. The above results reveal that the administration of SP enhanced microbiota-derived metabolite production, particularly inositol, indicating that metabolites might be involved in the SP-mediated protection against colitis and anxiety-like behaviors.

Administration of inositol ameliorates DSS-induced colitis and anxiety-like disorders
To verify the protective effects of inositol, which was a highly generated metabolite in the DSS+SP group, we administered inositol to the mice in drinking water (Fig. 8a). Supplementation with inositol potently repressed the weight loss and DAI induced by colitis (Fig. 8b, c). The colon length dramatically declined in the DSS group compared to the CON group, and this downregulation was repressed by inositol supplementation (Supplementary Fig. 17a). Histological examination revealed that DSS-induced reductions in crypt depth, as well as damage or exfoliation of epithelial cells, were substantially ameliorated following inositol treatment (Fig. 8e and Supplementary Fig. 17b), as indicated by lower histological scores (Supplementary Fig. 17c). A deficiency of E-cadherin in the mouse intestinal epithelium worsens the clinical symptoms and histological characteristics of colitis induced by DSS46. Therefore, we performed immunofluorescent staining for E-cadherin and observed a severe loss of E-cadherin in DSS-treated mice. However, inositol potently restored E-cadherin expression during colitis, suggesting that inositol could protect intestinal epithelial cells from breakdown induced by DSS (Fig. 8f and Supplementary Fig. 17d). To further, we found that the mRNA expressions of IL-1β, IL-6, and TNF-α were strikingly decreased in the colon tissues of inositol-treated mice compared with those of DSS-treated mice (Fig. 8g). It is noteworthy that no significant differences in weight loss, DAI, colon length, and pathological changes were observed between the group treated with inositol alone and the CON groups.

The protective effects of inositol on anxious-like behaviors were evaluated by OFT, and EPM assays. As shown in Fig. 8h, i, the results of OFT demonstrated that DSS-treated mice exhibited a shorter path length, less distance of center region and lower movement speed. On the contrary, inositol supplementation evidently mitigated these behavioral abnormalities induced by colitis. We then examined the effect of inositol in the EPM test (Fig. 8j, k). Compared to the DSS group, behavioral symptoms improved in the DSS+inositol group, which exhibited an obvious increase in the percentage of time spent in the open arms and percentage of times entering the open arms. Mice treated with inositol also exhibited a higher total distance traveled than DSS-treated mice (Supplementary Fig. 18). Notably, the differences in behavioral tests between the CON group and the inositol alone group did not reach statistical significance. As shown in Fig. 8l and Supplementary Fig. 17e, in the DG region, hippocampal cells were clearly and uniformly stained and neatly arranged in the CON group. Compared with the CON group, the DSS group exhibited loosely arranged cells and obvious nuclear consolidation. However, in the DSS+inositol group, the number of cells was augmented, and nuclear condensation was significantly improved. Remarkably, no significant alterations in hippocampal pathology were detected in the inositol alone group compared to the CON group. Besides, Nissl staining results confirmed that following inositol treatment, DSS-induced pathological changes in the hippocampal DG region, such as the reduction of Nissl bodies, pyknosis, and neuronal shrinkage, were notably restored (Fig. 8m and Supplementary Fig. 17f). To demonstrate whether inositol treatment played a significant role in mitigating neuroinflammation, RT-qPCR assay revealed that DSS intervention increased mRNA expression of the pro-inflammatory factor IL-6, IL-1β, and TNF-α in the hippocampus, which was reversed by inositol supplementation. Meanwhile, inositol elevated anti-inflammatory factor IL-10 mRNA expression (Fig. 8n). We then investigated whether inositol administration affected the activation of microglia. Results of immunohistochemical staining revealed that an up-regulated area of Iba-1 positive cells was observed in the DSS group compared to the CON group in the hippocampal DG and CA1 regions; however, inositol supplementation dramatically diminished the percentage of Iba-1+ cells (Fig. 8o, and Supplementary Fig. 17g).

A previous study reported that inositol undergoes oxidation to fructose and a series of enzymatic reactions to L-glutamate, which is then catalyzed by GAD to produce GABA47. We supposed that inositol may improve anxiety-like behavior by promoting GABA synthesis. To verify this conjecture, we detected the content of GABA in the hippocampus by ELISA. In the DSS-induced mice, there was a marked reduction in the concentration of GABA in the hippocampus, in contrast, the GABA level was appreciably enhanced following supplementation with inositol (Fig. 8p). To further evaluate the effect of inositol on GABA signaling, the expression of two GABA receptor subunits (GABA Rα1 and GABA R2) was assayed in the hippocampus. Interestingly, they were both significantly up-regulated after inositol supplementation (Fig. 8q, r and Supplementary Fig. 17h). The activity of GABA at the synapse is terminated by reuptake into nerve terminals and astrocytes, mediated by membrane-bound GABA transporters (GATs), which thereby modulate GABAergic neurotransmission48. With that in mind, we performed immunofluorescence staining for GABA transporters 1 (GAT1) and GFAP. As depicted in Fig. 8s and Supplementary Fig. 17i, the large processes of GFAP-immunopositive astrocytes were encompassed by the GAT1-expressing area in DG and CA1 regions, and the merged images showed occasional co-localization of GAT1- and GFAP-immunostaining. It’s worth noting that the number of GFAP+ astrocytes was dramatically reduced after DSS treatment, which could be overturned by inositol supplementation. In addition, the density of GAT1+ astrocytes appeared to be higher in the DSS+inositol group compared to the DSS group. Interestingly, no apparent intestinal pathological and behavioral alterations were observed in the inositol alone group, compared to the CON group. Collectively, these data suggest that inositol might serve as a critical candidate metabolite associated with the beneficial effects exerted by SP.

Inositol plays an essential role in the action of SP against anxious-like behaviors in DSS-induced mice
To next assess whether inositol is required for the positive effects of SP on anxiety-like symptoms, we treated mice with the inositol synthesis inhibitor, L-690330 (Fig. 9a). Behavioral analysis indicated that the anti-anxiety effects of SP were reversed by L-690330 treatment. Specifically, in the EPM test, colitis mice treated with both SP and L-690330 showed decreased percentage of time spent and distance traveled in the open arms, as well as lower percentage of times entering the open arms and total distance traveled, compared to SP-treated colitis mice (Fig. 9b, c and Supplementary Fig. 19a). In the EZM test, following L-690330 treatment, both the percentage of time spent in the open zones and the percentage of times entering the open zones were significantly reduced relative to SP-treated colitis mice (Fig. 9d, e). Although the total distance traveled showed a decreasing trend, this change was not statistically significant (Supplementary Fig. 19b). In the LDB test, colitis mice treated with SP and L-690330 exhibited lower percentages of time spent in the light box, distance traveled in the light box and total distance traveled, compared to those treated with SP alone (Fig. 9f and Supplementary Fig. 19c). However, no differences were observed in the transition frequencies between the light and dark chambers among the three groups. To evaluate microscopic variations in the hippocampus, we stained hippocampal sections with H&E (Fig. 9g and Supplementary Fig. 20a). Representative images revealed that the administration of SP repaired DSS-induced pathological characteristics, including shrunken nerve cells, pyknotic or indistinct nuclei, and an increase in injured neurons in the DG region. Intriguingly, L-690330 treatment markedly reversed the protective influence of SP on the hippocampus. Additionally, no statistical differences were observed in the CA1 and CA3 regions among the three groups. We further investigated the impact of inositol inhibition on neuronal survival in the DG region using Nissl staining. SP mitigated the loss of Nissl bodies, pyknotic and indistinct nuclei in the DG region, resulting in a significant increase in the number of surviving neurons. However, the administration of L-690330 effectively reversed these changes (Fig. 9h and Supplementary Fig. 20b). These findings substantiate the role of inositol in the neuroprotective properties of SP.

In light of the important role of inositol, we detected whether inositol level increased following FMT from DSS+SP-treated mice using ELISA. As expected, the concentration of inositol in the hippocampus of the FMT-DSS group was markedly lower than that in the FMT-CON group. However, a significant upregulation of inositol was detected in the FMT-DSS+SP group in comparison with the FMT-DSS group (Fig. 9i). Similar trends were observed in the serum (Fig. 9j). In light of the substantial increase in inositol concentration in both the hippocampus and serum following FMT from DSS+SP-treated mice, we hypothesized that inositol might exert a direct impact on the brain. To investigate this, we assessed the functional activity of inositol by culturing mouse microglial cell line (BV-2) and mouse astrocyte cell line (C8D1A) in vitro. BV-2 cells were stimulated with LPS at a concentration of 1μg/mL to mimic a neuroinflammatory environment. Cytotoxicity of inositol was evaluated using a CCK-8 assay. As shown in Supplementary Fig. 21a, BV-2 cells treated with different concentrations of inositol (25, 50, and 100μM) for 24 h exhibited no discernible cytotoxic effects. Further RT-qPCR analysis indicated that mRNA levels of TNF-α, IL-1β, and IL-6 were down-regulated at concentrations of 25 and 50μM in the inositol groups, although these levels were not significantly different from those observed in the LPS group (Fig. 9k-m). Importantly, treatment with 100μM inositol significantly reduced the levels of these inflammatory mediators. Additionally, the mRNA expression of iNOS in all inositol groups was lower than that observed in the LPS group, although no statistically significant differences were noted (Fig. 9n). Meanwhile, C8D1A cells were cultured with inositol for 24h. The results revealed that inositol exhibited no significant toxicity to C8D1A cells at concentrations of 50, 100 and 200μM (Supplementary Fig. 21b). Subsequently, C8D1A cells were treated with inositol under stimulation of LPS for 24h. Compared to the LPS exposure alone, inositol treatment increased the mRNA levels of Gabra3 and Camk2d, with the most pronounced effects observed at a concentration of 200μM (Fig. 9o, p). Consistent with these findings, western blot analysis illustrated that LPS exposure reduced the protein expression of GABA R1, CaMKII and GABA R in C8D1A cells. In contrast, supplementation with inositol at both 100 and 200μM markedly suppressed the downregulation of these protein expressions (Fig. 9q and Supplementary Fig. 21c). The above results suggest that inositol, as a crucial intermediate metabolite, plays an important role in the protective effects of SP against DSS-induced anxiety-like behavioral disorders by enhancing GABAergic neurotransmission signaling in astrocytes and modulating neuroinflammatory responses triggered by activated microglia.

Methods
Study procedure
A flowchart of the experimental design in this study is shown in Supplementary Fig. 1.

Animals
All animal experiments have been approved by the Institutional Animal Care and Use Committee of China Agricultural University (AW92303202-2-1, A0090214202-2-02). 7- to 8-week-old specific pathogen-free (SPF) male C57BL/6J mice (Vital River Laboratory Animal Technology, Beijing, China) were housed under a standard SPF facility in an environment (23–25°C, 45–55% humidity and 12-h light/12-h dark cycle). All mice were adapted to the environment and allowed to receive a standard diet and water for one week. At the end of the experiment, mice were euthanized humanely by CO2 asphyxiation followed by cervical dislocation. All procedures adhered to the principle of minimizing distress.

Dextran sodium sulfate (DSS)-induced colitis and experimental design
Acute colitis was induced by administering 4% DSS (molecular weight 36–50kDa; Yeasen, Shanghai, China) dissolved in drinking water for 6 days. Mice were monitored daily for body weight and assessed for the disease activity index (DAI). According to previous reports, DAI is considered a standard measurement of UC severity and prognosis, which comprises three indicators: weight loss (scored from 0 to 4 based on a loss of 0 to 15%), stool consistency (0 points for normal, 2 points for loose stool, and 4 points for diarrhea) and hematochezia (0 points for normal, 2 points for positive occult blood, and 4 points for over hemorrhage)117.

Study 1: treatment with SP
The experimental design is shown in Fig. 1a. The mice were randomly divided into four groups (ten mice in each group) as follows: normal control (CON), DSS only, DSS+SP (5nmol/kg), and SP (5nmol/kg) alone. The DSS group was provided free access to 4% DSS, and injected with sterile saline daily for 6 days. The DSS+SP groups received 4% DSS for 6 days, and SP (5nmol/kg/day in sterile saline) was administrated by injection. The SP alone group was given normal water and SP (5nmol/kg/day in sterile saline) for 6 days. All mice underwent a series of behavioral tests. The feces status and weight changes were daily recorded. Furthermore, the hippocampal tissues and colon tissues, colonic contents, and fecal samples were collected were harvested for additional analysis.

Study 2: antibiotic treatment
To explore the potential role of gut microbiota in mitigating DSS-induced colitis through SP, a comprehensive experimental design was employed. Initially, all mice underwent a 14-day ABX treatment of a mixture of antibiotics (Meilune, Dalian, China) including 1g/L ampicillin, 1g/L metronidazole, 0.5g/L vancomycin, 0.5g/L neomycin and 0.1g/L gentamycin in drinking water to effectively deplete the gut microbiome118. The antibiotics were changed every other day. After 2 weeks of ABX treatment, mice were randomly assigned to three groups: ABX(CON), ABX (DSS), and ABX(DSS+SP) groups. The SP treatment and DSS-induced colitis remained consistent with Study 1 of this study. The samples collected were consistent with those collected in the above study. All mice were euthanized on day 20 following the protocol outlined in Fig. 4a.

Study 3: fecal microbiota transplantation (FMT)
To further validate the ameliorating effect of SP on DSS-induced colitis in a gut microbiota-dependent manner, we performed an FMT experiment following the protocol established in a previous study119. Initially, we collected the feces of CON, DSS, and DSS+SP groups according to Study 1 as the donors of FMT. Collected fresh feces samples were quickly conserved in 40% sterile glycerol under −80°C. Before FMT, for substantial depletion of the microbiota, recipient mice have undergone a 2-week ABX treatment as outlined in Study 2 of the protocol. Subsequently, the mice were randomized into three groups: FMT-CON, FMT-DSS, and FMT-DSS+SP groups. Each sample of 100mg feces was suspended in 1mL of sterile phosphate-buffered saline (PBS) and centrifuged at 800× g for 5min to separate the supernatant. The bacterial suspension was filtered twice and then gavaged to the recipient mice (150μl per mouse) for 7d. Afterward, the mice in the FMT-DSS and FMT-DSS+SP groups were treated with 4% DSS. The samples collected were consistent with those collected in the above study. The experimental timeline is shown in Fig. 5a.

Study 4: inositol intervention
To investigate the protective effects of inositol on anxious behavior in colitis mice, mice were assigned to four groups. For the control group (CON), mice received normal water for 14 days throughout the study. For the group treated with DSS (DSS), mice received normal water for 8 days, and then 4% DSS was administrated in their drinking water for 6 days. For the group treated with inositol and DSS (DSS+inositol), mice were treated with 2% (w/v) inositol (CAT#87-89-8; MACKLIN, China) in the drinking water for 8 days prior to 4% DSS administration and continued to receive treatment for 6 days during DSS treatment. For the group treated with inositol alone, mice received drinking water with 2% (w/v) inositol for 14 days. Behavioral tests were performed on the last day. The samples collected were consistent with those collected in the above study. The experimental procedure is comprehensively illustrated in Fig. 8a.

Study 5: administration of inositol synthesis inhibitor
To investigate the role of inositol in the action of SP against anxious-like behaviors in DSS-induced mice. The mice were divided into three groups: (1) DSS group, (2) DSS+SP group, and (3) DSS+SP+L-690330 (inositol synthesis inhibitor) group. DSS and SP administration design was the same as in Study 1. L-690330 was diluted in saline and injected intraperitoneally administered at a dose of 0.1μmol 1 h prior to SP injection. Saline was treated as a vehicle control. The experimental design is shown in Fig. 9a.

Hematoxylin and Eosin (H&E) staining
The fresh colon and brain tissues were randomly sampled from each experimental group and subsequently fixed with a 4% paraformaldehyde solution for a duration of 48h. Following the fixation step, the tissues were dehydrated through a graded series of ethanol solutions, then cleared with xylene, and finally embedded in paraffin. The paraffin-embedded samples were sectioned into 6μm-thick slices. These sections were then subjected to a routine deparaffinization process. Following this, tissue sections were stained with hematoxylin and eosin (Beijing Solarbio Science & Technology Co., Ltd.). Then, the sections were rapidly differentiated, and dehydrated using 95% ethanol. The samples were then cleared with xylene and finally sealed using a neutral gum sealant. After H&E staining, colon crypt depth was assessed using ImageJ software (version 2.14.0). Histological scores were evaluated by an investigator blinded to the experimental groups according to inflammatory infiltration, mucosal damage, and destruction of the intestinal epithelium, as previously reported in Table S2120,121. For H&E staining of brain tissue, the tissue morphology of the hippocampal DG, CA1, and CA3 regions was examined under a light microscope. Image Pro Plus 6.0 software was used to quantify the number of cells with lesions in the hippocampal DG, CA1, and CA3 regions.

Transmission electron microscope
To visualize the ultrastructure of tissue, colonic segments were cut into 1mm3 samples and fixed in 2.5% glutaraldehyde and 1% osmium tetroxide at 4°C overnight. Then, the samples were dehydrated in ethanol and embedded in epoxy resin, and finally were sliced into ultrathin sections. The slices were stained with uranyl acetate and lead citrate, and observed under a TEM-1400 Plus electron microscope (JEOL JEM-1400 Plus, Tokyo, Japan). Representative images illustrating tight junction structures and microvilli were chosen.

Alcian Blue-Periodic Acid-Schiff (AB-PAS) staining
The 6-μm-thick colon slices obtained by the above method also were stained with an AB-PAS kit (Solarbio, G1285, Beijing Solarbio Science & Technology Co., Ltd., Beijing, China) following the instructions. After dewaxing and hydration, the slides were incubated in AB for 20min and then were washed with water, followed by incubation in 1% periodic acid for 10min. After the appellate step, the slides were incubated in Schiff’s reagent for 10min. The slides were finally re-stained with hematoxylin for 30s, washed and dehydrated, and then fixed with Pertex. The stained sections were observed under an optical microscope (Leica). The number of goblet cells per crypt was counted based on the typical morphological structure using Image Pro Plus 6.0 software.

Immunofluorescence staining
Briefly, after deparaffinization, rehydration, and antigen retrieval, slides were blocked with donkey serum for 30min at room temperature following incubation with primary antibodies against GFAP (1:1000, A8335, 1:200, NB100-53809, USA), Iba-1 (DF6442, 1:200, Affinity, China), E-cadherin (PB9561, 1:200, Boster, China), Mucin2 (bsm-60016R, 1:100, Bioss, China), GABA Rα1 (1:200, 12708-1-AP, Proteintech, China), calcium/calmodulin-dependent protein kinase II (CaMKII) (1:50, ab76703, Abcam, UK) and p-IKBα (1:100, WLH3930, Wanleibio, China) at 4C overnight. Then, slides were washed three times in PBS and incubated with biotinylated secondary antibodies (705-065-147, Jackson, USA) at 4C overnight. After incubation with the fluorescent antibody at 37°C for 2h slides were washed three times in PBS, dried, and treated with 4′,6-diamidino-2-phenylindole (DAPI) (0100-20, SouthernBiotech, Birmingham, AL, USA). The images were collected under a confocal laser-scanning microscope (Nikon, Tokyo, Japan). Additionally, image Pro Plus software was used to calculate positive cell count and average fluorescence intensity in the hippocampal DG, CA1 and CA3 region.

Intestinal permeability assay
Intestinal permeability was evaluated by using fluorescein isothiocyanate-conjugated dextran (FITC-dextran, molecular weight: 4kD). Briefly, all mice were fasted for 6h, and then FITC-dextran solution (Chondrex, Redmond, WA, USA) at the dose of 20mL/kg was delivered via oral gavage. After 4h, blood samples from the eyeballs were collected and the fluorescence intensity in serum was determined on a fluorescence spectrophotometer (excitation, 485nm; emission, 528nm).

The Luminex multiplex bead array
The Luminex analysis was conducted by LabEx (Shanghai, China). Utilizing the Bio-Plex Pro Mouse Cytokines Grp I Panel 23-plex kit, the expression levels of 23 cytokines were quantified, adhering strictly to the manufacturer’s instructions. In brief, homogenates from mouse colon and hippocampal tissues were gathered and incubated in 96-well plates along with diluent microbeads for half an hour in a darkened setting at ambient temperature. Following the removal of supernatants, the samples underwent exposure to detection antibodies for an additional 30min in a room-temperature environment. Afterward, Streptavidin-PE was introduced to each well and allowed to incubate for 10min at room temperature.

Open-field test (OFT)
The OFT was conducted to assess exploratory activity and spontaneous locomotor function in mice122. Behavioral experiments were conducted in an enclosed, opaque chamber with a square layout by the open-field equipment. Mice were gently deposited in the central zone of a white table platform, with the testing conducted under conditions of low lighting and minimal ambient noise. All mice underwent an acclimatization to the environment for 5min and moved freely, during which the total moving distance of mice, the time spent in the central region, distance of the central region, as well as moving speed in the central region.

Elevated plus-maze (EPM)
EPM is commonly used in rodents to assess exploratory and locomotor activity, unconditioned fear, and potential anxiety116. The elevated plus-maze (EPM) consists of open arms and closed arms that extend perpendicularly from a central platform, which measures 35cm in length and 5cm in width, with the closed arms standing 20cm high. The maze was positioned 60cm above the ground in a quiet room. Each mouse was placed at the center of the plus-maze, facing one of the closed arms. During a 5-min observation period, the movement distance, number of entries, and duration spent in the open arms were recorded.

Light-dark box (LDB)
In the LDB test, the light-dark box consisted of the light chamber (27×30cm) with a bright light in the center, whereas the dark chamber (27×30cm) had black walls and no light source. The two chambers were interconnected by a rectangular passage measuring 15cm. The mouse was initially placed in the bright chamber, facing away from the opening, and was given the freedom to explore the entire box. Each mouse’s behavior, including the frequency of entries into each area and the time spent in the light box or the dark box, was recorded during a 5-min session.

Elevated zero maze (EZM)
We used the EZM test because of its established predictive validity. Maze has four quadrants (two open zones with 1.5cm high translucent edges that were separated by two closed zones with 20cm high walls; platform width was 6cm wide). Briefly, mice were put in the maze and recorded for 5min, then immediately removed from the maze. Time spent and distance traveled in the closed and opened zones, as well as the number of entries into each quadrant were interpreted as a reduction of anxiety-like behavior.

Nissl staining
The brain tissue was fixed with 4% polyformaldehyde solution, dehydrated with gradient ethanol, cleared with xylene, embedded in paraffin, and sliced continuously at 6μm. The sections were stained at 56°C for 50min using Nissl reagents (Wuhan Servicebio Technology Co., Ltd.), complying with the manufacturer’s guidelines. Next, they were dehydrated with gradient alcohol and transparent with xylene. Finally, the slices were fixed with neutral resin. In the hippocampal tissue of each group, Nissl bodies and pathological changes in neurons were carefully examined under an optical microscope. Slices were randomly selected for each group to count the number of positive neurons (Nissl+ cell) by Image Pro Plus 6.0 software.

Western blotting
The brain and colon tissues were collected after the mice were sacrificed. Proteins were isolated and quantified by a protein assay kit (Beyotime Biotechnology, Shanghai, China), followed by electrophoretic separation. Afterward, the protein was transferred to polyvinylidene difluoride (PVDF) membranes, and then the membranes were blocked in 5% skim milk for 2h at 37°C. Next, primary antibody against GFAP (1:1000, A8335, 1:200, NB100-53809, USA), GABA Rα1 (1:1000, 12708-1-AP, Proteintech, China), GABA R2 (1:1000, 27567-1-AP, Proteintech, China), CaMKII (1:1000, 12716, Cell Signaling Technology, USA), GAD65 (1:1000, ab239372, Abcam, USA), CD206 (1:1000, ab64693, Abcam, USA), iNOS (1:1000, 18985-1-AP, Proteintech, China), CD80 (1:1000, 66406-1, Proteintech, China), p-p65 (1:1000, A00284-1, Boster, China), p-IKBα (1:1000, WLH3930, Wanleibio, China), and β-actin (50201, 1:1000, Kemei Borui Science and Technology, China) were incubated with the membranes overnight at 4°C. After incubation with goat-anti-rabbit IgG secondary antibody (115-035-071, Beyotime Biotechnology, Shanghai, China) for 1h at 37°C. The ECL chemiluminescence kit (Tanon Science and Technology Co., Ltd) was used to visualize protein bands, and were observed by a luminometer (Tanon® 5200 Multi). β-actin was used as an internal control. ImageJ software (version 2.14.0) was used for analysis of Western blots.

Feces culture and colony-forming unit (CFU) measurement
Feces were collected in a sterile tube and diluted with 10-weight volume of PBS. The tube was vigorously vortexed. Then, the samples were centrifuged at 100× g for 10min. The supernatant was serially diluted to 102 to 108 folds and then was streaked with a cell spreader on an LB agar plate. After incubation at 37°C for 24h, CFUs were measured.

Fecal DNA extraction and quantification
After the experiment, fecal samples were collected to assess fecal DNA content. DNA was extracted from fecal samples (100mg) of mice, using the QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany) based on the manufacturer’s instructions. The DNA purity and concentration were measured with a NanoDrop 2000C spectrophotometer (Thermo Scientific).

16S rRNA sequencing analysis
Microbiome bioinformatic analysis was performed by the Biomarker Technologies Co., Ltd (Beijing, China). Briefly, total microbial DNA was extracted from colonic contents. The V3-V4 region of the 16S rRNA gene is amplified from the extracted DNA using universal bacterial primers that target conserved regions of the gene. PCR products were quantified by the Quant-iT PicoGreen dsDNA Assay Kit. The 16S rRNA gene amplicons are then prepared for high-throughput sequencing using the Illumina Novaseq platform (Biomarker Technologies, Beijing, China). The raw reads from sequencing were filtered using Trimmomatic v0.33 and cutadapt 1.9.1 to obtain high-quality clean reads. Afterwards, chimera sequences were detected and then removed using the DADA2 Algorithm123. Finally, the qualified sequences were clustered at a 97% similarity level using USEARCH (version 10.0), and 0.005% of the total sequences were identified as quality-filtered sequences to generate the amplicon sequence variants (ASVs). Taxonomy was assigned to the ASVs using the classify-sklearn at a 70% confidence threshold. The representative sequence for each ASV was screened for further annotation. Alpha diversity indices, including the Chao1, Shannon, and Simpson indices, were calculated using QIIME2 software (Version 2020.8)124. Statistical significance between groups was determined by one-way analysis of variance (ANOVA) using BH-FDR methods based on python2 software (scipy v0.17.1). Beta diversity analysis was performed to investigate the structural variation in microbial communities across samples using binary Jaccard distance metrics and visualized via principal coordinates analysis (PCoA). Permutational multivariate analysis of variance (PERMANOVA) based on the binary Jaccard distance was applied to identify statistical significance of beta diversity between groups by using the vegan package in R. The random forest analysis was performed by R v3.1.1 software (randomForest v4.6-10). PICRUSt2 software125 by referring to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database126.

Quantitative real-time polymerase chain reaction (qRT-PCR)
Colonic and hippocampal total RNA was isolated using TRI Reagent following the manufacturer’s guidelines and then was reverse transcribed using HiFiScript cDNA synthesis kit from Vazyme (Jiangsu, China). The qPCR was carried out on StepOnePlus Real-Time PCR System (Bio-Rad, INC, USA) using SYBR green dye (CWBIO, Beijing, China) following the standard program26. The gene expression was normalized to β-actin using the 2−ΔΔCT method. The primers were synthesized by Sangon Biotech and sequences of oligonucleotides are shown in Table S1.

Biochemical analysis
Colon tissues were homogenized in PBS and then centrifuged at 16,000× g for 15min to collect supernatant, which was measured the protein content by a BCA protein assay kit. The concentrations of cytokines was detected using IL-6 (H002-1-1) and TNF-α (H052-1-1) Enzyme-linked immunosorbent assays (ELISA) kits (Jiancheng Bioengineering Institute, Nanjing, China) in colon tissue. The concentration of SP was detected by an ELISA kit (Elabscience Biotechnology Co., Ltd., Wuhan, China) in hippocampal tissues. The level of MPO of colonic samples was determined using a commercially available kit (H508-1-1, Jiancheng Bioengineering Institute, Nanjing, China). The cytokines γ-Aminobutyric acid (GABA) from the hippocampus samples in each group of mice were assessed using ELISA kits (H168-1-2, NJJCBIO, Nanjing, China). All assays were in accordance with the manufacturer’s protocols. A microplate reader (Bio-Rad, Germany) was engaged to obtain optical density (OD) values. The results were calculated in reference to the standard curve.

Transcriptome sequencing and bioinformatics analysis
The samples are derived from the hippocampal tissue of mice as well as two types of cells sorted using flow cytometry. Transcriptome sequencing and data analysis were performed by OE Biotech Co. Ltd (Shanghai, China). Total RNA was extracted using TRIZOL reagent (Invitrogen, USA), including an additional DNase I digestion step. The concentration of the extracted RNA was measured using a NanoDrop ND-2000 Spectrophotometer (Thermo Scientific, Wilmington, USA). RNA quality was evaluated to ensure RNA integrity, as assessed by the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). RNA-seq libraries were constructed using TruSeq™ RNA Sample Preparation kits (Illumina, San Diego, CA, USA). Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA) was used for quality control and then libraries were sequenced by a NovaSeq 6000 platform (Illumina, San Diego, CA, USA). Following sequencing, raw reads in FASTQ format were processed using fastp software, which eliminated low-quality reads to yield clean reads. Spliced transcripts alignment to a reference (HISAT2) software was used for sequence alignment on the clean reads from each sample. Differential expression analysis was conducted with the DESeq2 (2012) R package127. Hierarchical clustering of differentially expressed genes (DEGs) was carried out to illustrate the expression patterns across different groups and samples. KEGG pathway enrichment analyses of the DEGs were performed separately using R, based on the hypergeometric distribution.

UALCAN database analysis
UALCAN (https://ualcan.path.uab.edu/) is open data of the cancer transcriptome128. UALCAN was used to estimate the mRNA expression of genes implicated in the NF-κB pathway and GABAergic/Ca2+ signaling in colon adenocarcinoma (COAD).

Cell sorting and flow cytometry analysis
Single-cell suspensions were prepared from the hippocampal tissue through enzymatic digestio and resuspended in 40% Percoll and centrifuged at 800× g for 20min at 15°C. The supernatant, which contained the myelin, was discarded, and the pellet was then resuspended in cold MACS buffer. Myelin Removal Beads II (130-96-733, Miltenyi Biotec) were utilized according to the manufacturer’s protocol. The cell pellets were treated with Fc receptor blocker to block nonspecific binding in the dark for 15min at 4°C. Then the cells were applied to a magnetic-activated cell sorting (MACS) system using Anti-ACSA2 and Anti-CD11b magnetic microbeads (Miltenyi Biotec, #130-097-679 and #130-093-634), according to the manufacturer’s instructions. After incubation, the cells were washed with PBS and the cells were resuspended in PB buffer. The cell suspension was applied to the LS column which was placed in the magnetic field of the MACS separator (Miltenyi Biotec). Then, the magnetically labeled cells were flushed into a tube using 5mL of PBS. Collect the separated target cells and resuspend them in an appropriate FACS buffer. MACS-pre-sorting cells were reacted with fluorescent-conjugated specific antibodies for 30min on ice in the dark, washed, and resuspended in FACS buffer. The following antibodies were used for immunofluorescence staining: APC-Cy7-labeled CD45 (103114, Biolegend), PE-labeled CD11b (101208, Biolegend), APC-labeled ACSA2 (130117386, Miltenyi Biotec). The unbound antibody was removed by washing with PBS and centrifuged at 400× g for 10min. The stained cells were analyzed using BD Aria III (BD Biosciences) and BD FACSDiva v8.0.1 software.

Ultrahigh performance liquid chromatography-mass spectrometry (UPLC-MS) analysis
Ultrahigh-performance liquid chromatography coupled with mass spectrometry analysis was conducted by Biomarker Technologies Co., Ltd (Beijing, China). Briefly, samples of colonic content and right brain hemispheres (100mg, ±1%) were weighed and dissolved in 100μL of acetonitrile/water (1:1, v/v), adequately vortexed, and then centrifuged (13,400× g, 4°C, 15min). The supernatants were collected and separated using a UPLC system (1290 Infinity LC, Agilent Technologies). The positive (+) and negative (−) ionization modes of the analytes eluted from the column were detected by electrospray ionization. Analyses were performed using UPLC coupled with quadrupole time-of-flight mass spectrometry (AB SCIEX TripleTOF 6600). During the experiment, quality control samples were utilized to evaluate system stability and data reliability. The original data collected by MassLynx V4.2 were used for peak extraction, peak alignment, and other data processing operations by Progenesis QI software. Based on the Progenesis QI software, online databases such as METLIN129 and self-built databases of BMG were used for metabolite identification according to sample types. The differential metabolites were identified by combining the P value of the t-test and the VIP value of the OPLS-DA model (FC>1, P value<0.05 and VIP>1). Differential metabolites were annotated and subjected to enrichment analysis using the KEGG database. Principal components analysis (PCA) and clustering heat maps were performed at MetaboAnalyst R130.

Measurement of inositol level
The levels of inositol in serum and hippocampal tissues were measured using an ELISA kit (Hengyuan Biotechnology Co., Ltd, Shanghai, China), based on the manual of the producer. Hippocampal tissues were blended with PBS (9:1) to homogenate, and then the supernatants were collected by centrifugation at 16,000× g for 15min. Total protein was analyzed with a bicinchoninic acid method (BCA) protein determination kit according to the manufacturer’s instructions.

Immunohistochemical staining
The brain samples were embedded in paraffin wax and then sectioned into 6-μm-thick coronal slices using an automated microtome. The brain tissue sections were carefully spread and baked. Subsequently, the sections underwent a dewaxing process using xylene, followed by rehydration. After rinsing with PBS, the endogenous peroxidases present in the tissue were blocked by treatment with 3% H2O2. The sections were blocked with donkey serum and then were incubated overnight at 4°C with anti-Iba1 antibody (1:200, DF6442, Affinity, China). After PBS washing, the biotinylated secondary antibodies (711-065-152, Jackson, USA) were added to incubate for 2h at room temperature. Then the slides were washed in PBS and incubated with the VECTASTAIN® ABC Reagent (ZD0201, Vector Laboratories, USA). After washing with PBS, the nuclei were stained using 3,3’-diamino-benzidine-tetrahydrochloride (DAB). Results are expressed as the mean average optical density (AOD) of Iba1-positive and GFAP-positive cells.

Cell culture and treatment
The mouse microglial cell line (BV-2) was purchased from the Chinese Academy of Sciences Cell Bank in Shanghai. BV-2 cells were cultured in Dulbecco’s Modified Eagle Medium supplemented with 10% fetal bovine serum (FBS, Gibco), and 1% penicillin-streptomycin in a humidified atmosphere at 37°C with 5% CO2. To mimic a neuroinflammatory cellular state, the cells were incubated with LPS at a dose of 1μg/mL for 24h. To determine the optimal dosage of inositol, BV-2 cells were pretreated with varying concentrations of inositol (25, 50, and 100μM) for 2h. Following this, the cells were continuously incubated with LPS (1μg/mL) for up to 24h. The cells were subsequently collected for further analysis.

The mouse neuronal astrocyte cell line (C8D1A) was purchased from Fuheng Biotechnology Co., Ltd (FH0837, Shanghai, China). C8D1A cells were cultured in Dulbecco’s Modified Eagle Medium supplemented with 10% fetal bovine serum (FBS, Gibco), 1% penicillin-streptomycin in a humidified atmosphere in a humidified incubator with 5% CO2 at 37°C. After reaching confluency, C8D1A cells were seeded for desired experiments. Subsequently, to study the action of inositol on astrocyte survival and GABAergic and Ca2+ signaling pathway, C8D1A cells were incubated with various concentrations of inositol (50, 100, 200μM) with or without 1μg/ml LPS for 24h. Subsequently, the cells were carefully collected for further assay.

Cell viability was determined by cell counting kit-8
Cell viability was evaluated by Cell Counting Kit-8 according to the manufacturer’s instructions (MedChemExpress, USA). Cells (1×105 cells/ml) growing at the exponential phase were seeded into 96-well plates. BV-2 cells and C8D1A cells were treated with varying concentrations of inositol for 24h, and the culture medium as control. At the end of treatment, 10μL CCK8 reagent was added to each well, and incubated with cells for 4h. Finally, the OD values were detected at 450nm using a microplate reader. Cell viability was expressed as a percentage of the number of surviving cells relative to the control groups.

Induction of trinitrobenzenesulfonic acid (TNBS) model of mice colitis and SP treatment
The experimental design has been revealed in Supplementary Fig. 23a. Acute UC model of mice was induced using the administration of 2,4,6-Trinitrobenzenesulfonic acid (TNBS) according to the method previously reported131. Briefly, mice were lightly anesthetized with ether. Then, 3.5mg of TNBS (Meilune, Dalian, China) in 100μL of a 50% ethanol solution was slowly administered into the colon, 3.0–3.5cm, from the anus, through a flexible catheter. Mice were held in a vertical position for 30s after the injection. SP was administered via the intraperitoneal route starting on the day of TNBS administration and continued daily until the end of the study at day 7. The mice were randomly divided into four groups: a control group given vehicle (CON), a colitis group given TNBS (TNBS), and two additional colitis groups receiving 5nmol/kg or 10nmol/kg of SP by injection (TNBS+SP-L and TNBS+SP-H, respectively). Each group consisted of six mice. Mice in the non-colitis group were treated with 0.1ml of 50% ethanol alone instead of TNBS. Mice were weighed and observed daily, and assessed for DAI, based on the above-described criteria.

Statistical analysis
Data was analyzed, and graphs were plotted in the figures using GraphPad Prism software (Version 8.0, GraphPad, San Diego, CA, USA). Data are presented as the mean ± standard deviation (SD). Spearman’s correlations among the phenotypes, colonic bacteria, metabolites, and brain gene expression were conducted utilizing the R software. Significant differences in more than two groups were analyzed using one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparisons test or two-way ANOVA with Sidak’s multiple comparisons test. For all statistical comparisons, *p<0.05, **p<0.01, and ***p<0.001.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary:\nLan and colleagues investigated whether substance P (SP) would protect mice against colitis and its associated anxiety-like behaviour via a microbial mechanism. The authors tested their hypothesis using a mouse model of colitis (DSS-induced colitis) followed by different microbiota manipulation approaches (antibiotics, FMT, microbial-associated molecule supplementation) and by applying a correlational multi-omics approach (16S rRNA seq, mass-spectrometry metabolomics, bulk-RNAseq transcriptome). The authors suggested that SP administration protects against colitis and its linked anxiety-like phenotype via the microbial-metabolite inositol. Although the authors elegantly explored the potential molecular mechanisms of the phenotype observed by using multiple techniques, the manuscript has a few points and concepts that could be addressed to improve its reproducibility and conclusions.\nMajor:\nThe manuscript and his title imply effects of substance P on the brain and on the gut and its microbiome. Since substance P appears to reduce inflammation it is unclear whether the changes in behavior and brain are secondary to the reduction in inflammation or a separate direct effect on the brain. This distinction is important as it would tell whether SP has an anti-inflammatory and/or anxiolytic role.\nThe manuscript and its title are ambiguous and lead the reader to expect to see evidence for an effective substance B on the microbiota. There are no data presented to support this and this ambiguity should be clarified. It is most likely mediated via the host based on the changes in the inflammatory process mediated by substance P. This is a major concern as this evidence supports the rest of the manuscript’s hypotheses.\nThe role of inositol in the responses to substance P is not proven simply by demonstrating the effects of administered inositol. The data presented simply show inositol is a candidate mediator but causality cannot be claimed. The same point applies to the inositol intervention: would the anxiolytic effect be linked to the treatment or the lower intestinal inflammation?\nThere is concern regarding the assessment of anxiety-like behavior. While there is a plethora of tests available, the authors chose the open field test which is primarily a test of movement and therefore malaise rather than anxiety. Indeed groups associated with anxiety-like behaviour also had a reduction in their overall mobility. More selective tests of anxiety are required to claim anxiety-like behavior.\nThere is no quantification or deeper characterization of the histological changes presented in the manuscript (H&E, globet-cell staining (no method described) and Nissl) to generate statistical significance or draw important conclusions regarding the treatments applied. This lack of quantification weakens the conclusions drawn and could be addressed to improve the data interpretation.\nMoreover, the authors claim that they observed an improvement in globet-cell population, which is linked to an improvement in the intestinal barrier. While mucus production is a component of the intestinal barrier, multiple other physical, chemical, and immunological components constitute the intestinal barrier. There are many available techniques suitable for specifically measuring the intestinal barrier. Thus, direct conclusions should be drawn only regarding the goblet-cell composition - provided a quantification is performed.\nIn Fig 4A, the authors explored the role of the microbiota in mediating the protective effects of SP. They treated the mice for 14 days prior to the DSS intervention to deplete the gut microbiota. However, it is not clear whether ABX treatment was stopped on day 14. If that is the case, the microbiota would be in the absence of the ABX treatment. The authors need to characterize the microbiota at day 14 and that day 20\nThe FMT experiment is seen as the most novel data in the manuscript, in which DSS+SP microbiota led to a reduced response to DSS-induced colitis. However, the authors did not explore the microbiota composition of the recipient mice, which leads to the question of which microbes were transferred that could have been beneficial to the host.\nMoreover, the authors link the beneficial microbial effects to inositol. Is inositol increased after the FMT treatment with DSS+SP microbiota? This information is key for the conclusion of the manuscript.\nMinor:\nTo improve the reproducibility of the data, a couple of points could be described in the methods section:\n-The bioinformatic pipeline used to analyze the 16S rRNA gene sequencing should be thoroughly described.", "reviewer_2": "This study demonstrates that the neuropeptide Substance P (SP) protects against colitis and anxiety-like behaviors in an IBD model. SP reduces intestinal damage, neuroinflammation, and restores hippocampal function by inhibiting microglia and supporting astrocytes. It also improves gut microbiota composition and modulates the NF-κB pathway. Additionally, SP increases the microbiota-derived metabolite inositol, which helps reduce colitis and anxiety, highlighting SP's therapeutic potential for gut-brain axis disorders in IBD.\nThis is a new finding with important implications, yet the study needs further development:\nMajor points\n1. In lines 160-163: The authors claim that DSS-induced intestinal inflammation, edema, and mucosal damage were reduced by SP based on tissue analysis. However, this claim is unclear based on the histological images alone. They should present quantitative results such as biochemical markers or other relevant data (imaging data etc.).\n2. In line 162, the authors state that goblet cell damage caused by DSS was restored by SP, but this is not clear from the histological images alone. At the very least, they should distinguish between enterocytes and goblet cells. The use of a goblet cell-specific protein marker is necessary to clarify their claim.\n3. In Figures 2A & B, the pathological changes mentioned by the authors need to be quantified.\n4. In Figures 4F & G, the results should be quantified using markers for the lesions.\n5. In Figure 5H, the authors should stain for appropriate markers or quantify the pathological changes they are asserting using biochemical methods.\n6. In Figure 5M, it is difficult to confirm the damaged nuclei or nuclear shrinkage mentioned by the authors in the provided tissue images. The authors should provide higher-resolution magnified images or use alternative methods to interpret these results.\n7. Generally, GFAP is known to increase due to astrocyte inflammation and often shows a similar expression pattern with iNOS. Moreover, previous studies indicate that GFAP mostly increases in DSS models. However, in the DSS animal model used by the authors, a decrease in GFAP was observed, which is debatable. The authors should clarify their perspective on this in the Discussion.\nReferences:\na. Xiao-fei He, et al., J Neuroinflamm, 2021\nb. J Do, et al., Clin Psychopharmacol Neurosci, 2018\nc. Mingyue Li, et al., Exp Neurol, 2024\n8. The authors claim that the NF-kB signaling pathway is important based on their RNA transcriptome analysis. However, given the prior results regarding neuroinflammation, it is difficult to consider NF-kB signaling as an interesting finding. Additionally, the correlation between SP and NF-kB is well-known, with SP being known to increase NF-kB, which contrasts with the authors’ claims. The novelty the authors seek to demonstrate with GFAP needs further explanation.\n9. The fact that SP regulates Ca2+ and GABA signaling is indeed intriguing. Although the authors used the hippocampus in the FMT model, it would be necessary to confirm via cellular experiments whether SP directly affects microglia and astrocytes. If there is no direct effect, it is possible that SP influences the hippocampus through the improvement of gut diseases. Otherwise, the discussion should consider bidirectional effects between the gut and brain.\n10. The authors used whole hippocampus tissue for RNA sequencing analysis. This method might obscure result interpretation due to cellular heterogeneity. Since the authors are focusing on microglia and astrocytes, it would be appropriate to isolate specific cell types for transcriptome analysis or to include a single-cell study.\n11. In Figures 8E, I & J, the authors should present quantitative results for the lesions they describe. Additionally, co-staining with marker genes, if necessary, should be performed to clearly identify these lesions.\n12. The DSS-induced colitis mouse model effectively mimics ulcerative colitis (UC), but it is insufficient to support the claim that Substance P effectively alleviates IBD through the microbiota-gut-brain axis. To expand on the efficacy of SP in improving IBD, further research using a Crohn’s disease (CD) animal model or validation in additional UC models is needed to more concretely demonstrate SP’s therapeutic effect.\n13. In lines 244~251, LEfSe is employed for differential abundance analysis. However, LEfSe is evaluated as ‘method should be avoided’ in the recent comparative analysis due to its high false discovery rate. (Please note https://www.nature.com/articles/s41467-022-28034-z). I recommend removing the result from LEfSe and focusing on results from other two analysis (RF and ANCOM).\n14. In lines 266~269, PICRUSt is employed for functional analysis but it is officially discontinued software since 2020. PICRUSt2 can be a good alternative. Also, selected functions are too broad so they are not informative. More specific metabolic function should be represented like Fig. 7E.\nMinor points\n1. Although many prior studies have been conducted on Substance P and psychiatric disorders (such as anxiety response, depression, etc.), there is a lack of mention of this in the current study. It would strengthen the argument to include this information.\n2. There are many mistakes in the figure legends that require overall review. For example, in Figure 1H, I, it says “Distance traveled (m)” and “Distance of center region (m),” but the figure legend is labeled differently. In figure 4B and C, it should be changed figure legend. Authors should use consistent abbreviations (p-IkB , p-pIkB , p-IkB- , and p-IkB etc.).\n3. In Figure 4D, the images are labeled as ABX-CON, ABX-DSS, ABX-DSS+SP, but the graph is labeled only as CON, DSS, DSS+SP.\n4. In Figures 4F and 4G, ABX-CON is missing.\n5. In Figures 5O & P, the DG is labeled, but C1 is missing. It is assumed that the image below DG is C1, and a corrected image should be included.\n6. There is a lack of overall consistency in the figures, reducing readability. For instance, C1 and CA1 are used interchangeably, and in Figure 6J, part of the image is cut off.\n7. There are too many redundant results in the main figures. It is recommended to move some to the supplementary figures.\n8. Some of the Western blot images need to be replaced. Overexposure and excessive cropping could cause confusion in interpreting the results.\n9. In Figure 3, the text sizes in F, G, and L are too small, making them difficult to read. Removing insignificant terms or focusing on important terms (like in Figure 7F) is recommended.\n10. In Figure 6A, it is difficult to distinguish the three separate groups by PCA. The groups should be outlined or shaded. Also, the 3D PCA figure is hard to interpret, so a 2D figure is recommended if the third PC is unnecessary.\n11. In lines 383–384, the mouse gene ‘CaMKII’ should be written as ‘Camk2d.’ ‘CaMKII’ refers to the protein nomenclature.\n12. In Figures 6D and E, the gene sets are identical. It is unclear whether these are common differential genes or manually selected. The statistical criteria for selecting genes should be indicated in the figure legend, and statistically insignificant genes should be removed.\n13. In Figures 9E and F, it is difficult to read the labels due to overlap between the diagrams and labels.\n14. A more detailed explanation of the bioinformatics analysis should be provided in the methods section, especially the software used for RNA-seq and metabolomics analyses.", "reviewer_3": "The gut-brain axis mediates the bidirectional communications between gut inflammation and psychiatric comorbidities in inflammatory bowel disease (IBD). This study investigated the protective effects of neuropeptide substance P (SP) on colitis and anxious-like behaviors induced by dextran sodium sulfate (DSS) in mice. This study showed that the beneficial effects of SP were achieved by modulating the gut microbiome composition and subsequently upregulating the production of microbiota-derived metabolite, inositol, implying an intestinal flora-dependent mechanism. Furthermore, the antianxiety activities of SP were attributable to the suppression of the NF-κB signaling pathway in microglia as well as the activation of GABAergic neurotransmission and downstream Ca2+ signaling within astrocytes in the hippocampus. There are some points need to be clarified:\n1. In the study, it appears that the comparison of the control + SP group is not included among the experimental groups. This makes it challenging to observe the effect of SP on both the disease activity index and quality in normal mice.\n2. In the results (Figure 1): It appears that the central zone stay time for the control + SP group is higher than that of the control group in the J plot. Would it be possible to conduct a statistical analysis for these two groups? Additionally, could you please provide an explanation for this observation?\n3. In the results (Figure 2): I noticed a similar issue in the I plot as seen in Figure 1. The GFAP value in the control + SP group appears to be significantly higher than that in the control group. Could you please help clarify and explain this observation?\n4. In the results (Figure 4): The text says that SP leads to a smaller spleen/body weight ratio in mice, but we don’t see this happening in the E plot of Figure 4 for the ABX-DSS and ABX-DSS + SP groups. You can actually spot this change in the J plot of Figure 5. Should we explain this difference?\n5. I noticed that the titles of Figure 4 and Figure 5 are identical. Could you please confirm this?\n6. In the results (Figure 8), it appears that the comparison of the control + Inositol group is not included among the experimental groups. Would it be possible to address this?\n7. To enhance readers' understanding of the article's design and key findings, it would be greatly appreciated if the authors could consider including a flowchart that outlines the experimental groups and primary objectives.\n8. The abbreviation for CNS has been mentioned earlier; you can refer to it directly in line 84. Please check the entire text.\n9. In line 144, would it be more suitable to replace “correct” with “improve”?\n10. This study suggests that SP influences the expression of hippocampal genes by modulating the gut microbiome, which involves the NF-κB and GABAergic/Ca2+ signaling pathways. Please discuss the causal relationships in the discussion section.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "RESPONSE TO REVIEWERS’COMMENTS\nDear Editor and Reviewers:\nWe sincerely appreciate your constructive comments and suggestions for the\nmanuscript entitled “Neuropeptide SP protects against colitis and linked anxiety-like\nbehavior via gut microbiota and metabolite inositol:Amulti-omics analysis of the gut-\nbrain axis” (Manuscript ID: NCOMMS-24-56603). These valuable comments are\nhelpful for revising and strengthening our paper. To fully address them, we have tried\nour best to perform additional experiments to improve the quality and clarity of our\nwork. In this response letter, the reviewer’s comments are presented in italicized font,\nand specific concerns have been numbered. Our responses are provided in normal font,\nandanychangesoradditionstothemanuscriptarehighlightedinbluetext.Furthermore,\nwe have undertaken a comprehensive revision of the entire manuscript, with revised\nportions marked in red. We hope that the revised manuscript can satisfy the\nrequirements. In the following, we summarize the reviewers’comments, and describe\npoint-by-point how we have addressed them.\nReviewers’Comments:\nReviewer #1 (Remarks to theAuthor):\nSummary:\nLanandcolleaguesinvestigatedwhethersubstanceP(SP)wouldprotectmiceagainst\ncolitis and its associated anxiety-like behaviour via a microbial mechanism. The\n1\nauthors tested their hypothesis using a mouse model of colitis (DSS-induced colitis)\nfollowed by different microbiota manipulation approaches (antibiotics, FMT,\nmicrobial-associated molecule supplementation) and by applying a correlational\nmulti-omics approach (16S rRNA seq, mass-spectrometry metabolomics, bulk-\nRNAseq transcriptome). The authors suggested that SP administration protects\nagainst colitis and its linked anxiety-like phenotype via the microbial-metabolite\ninositol. Although the authors elegantly explored the potential molecular\nmechanisms of the phenotype observed by using multiple techniques, the manuscript\nhas a few points and concepts that could be addressed to improve its reproducibility\nand conclusions.\nResponse: We thank the reviewer for their positive and constructive comments. The\nissues raised have strengthened the manuscript considerably. To fully address them, we\nthoroughly considered all points raised by the reviewer and performed additional\nexperiments. Below, we summarize the reviewers’ comments, and describe point-by-\npoint how we have addressed them.\nMajor:\n1. The manuscript and his title imply effects of substance P on the brain and on the\ngut and its microbiome. Since substance P appears to reduce inflammation it is\nunclear whether the changes in behavior and brain are secondary to the reduction in\ninflammation or a separate direct effect on the brain. This distinction is important as\nit would tell whether SP has an anti-inflammatory and/or anxiolytic role.\nResponse: We would like to thank the reviewer for your valuable feedback. SP is a\n2\nrelatively large peptide with a molecular weight of 1346.63 Da. A study has shown that\nSP’s ability to penetrate the blood-brain barrier (BBB) is limited, with only small\namounts crossing this barrier, which hinders effective delivery to brain regions [1]. To\ninvestigate whether SP can cross the BBB, we evaluated SP expression in the\nhippocampus using ELISA. Our results indicated no significant differences in SP\nconcentration among the CON, DSS, and DSS+SP groups (Supplementary Fig. 3a).\nFurthermore, RT-qPCR analysis demonstrated that after administering SP, the mRNA\nlevels of SP in the hippocampus of the DSS+SP group were not significantly different\nfrom those in the DSS group (Supplementary Fig. 3b). These findings suggested that\nSP administration does not effectively cross the BBB to directly influence brain activity.\nConsidering the emerging role of the microbiota, a new gut-brain pathway has come to\nlight, regulating various neuropsychiatric disorders [2], we hypothesized and further\ndemonstrated that SP could regulate the composition of gut microbiota, thereby\neffectively improving gut dysbiosis. The pathways through which the microbiota\nmodulate bidirectional communication between the brain and the gut are currently\nbelieved to include: (1) the vagus nerve, (2) derived metabolites, (3) endocrine\nsignaling, and (4) immune responses [3]. Specifically, gut dysbiosis can trigger\nperipheral immune responses, leading to the release of inflammatory factors into the\nbloodstream and resulting in neuroinflammation. Thus, it can be inferred that SP may\nmitigate anxiety by reducing peripheral inflammatory factors, and the restoration of\nmicrobial community is part of the reason for SP’s neuroprotective effects. Importantly,\nrecent research has indicated that a substantial portion of the metabolites found in\n3\nmammalian blood originates from the intestinal microbial community, which\ncommunicates with the brain [4]. We found that SP could enhance the production of\ninositol metabolites, thereby contributing to the alleviation of anxiety-like behavior,\nwhich influenced the transcription of hippocampal genes related to the NF-κB,\nGABAergic neurotransmission, and Ca²⁺ signaling pathway. We have incorporated\nthese additional results into the revision (Result section: Page 14, Line 296-303) and\nthe figures are presented below.\nReferences：\n1. Ermisch A, Rühle H J, Landgraf R, et al. Blood-brain barrier and peptides[J]. Journal of Cerebral\nBlood Flow & Metabolism, 1985, 5(3): 350-357.\n2. Kim Y K, Shin C. The microbiota-gut-brain axis in neuropsychiatric disorders:\npathophysiological mechanisms and novel treatments[J]. Current neuropharmacology, 2018, 16(5):\n559-573.\n3. Góralczyk-Bińkowska A, Szmajda-Krygier D, Kozłowska E. The microbiota-gut-brain Axis in\npsychiatric disorders[J]. International journal of molecular sciences, 2022, 23(19): 11245.\n4. Ahmed H, Leyrolle Q, Koistinen V, et al. Microbiota-derived metabolites as drivers of gut–brain\ncommunication[J]. Gut microbes, 2022, 14(1): 2102878.\na b\nELISA. b The mRNA expression of SP was tested in hippocampus tissue by qRT-PCR.\n2. The manuscript and its title are ambiguous and lead the reader to expect to see\nevidence for an effective substance B on the microbiota. There are no data presented\nto support this and this ambiguity should be clarified. It is most likely mediated via\nthe host based on the changes in the inflammatory process mediated by substance P.\nThis is a major concern as this evidence supports the rest of the manuscript’s\nhypotheses.\nResponse: We thank the reviewer for the opportunity to further clarify this important\npoint. To investigate whether SP exerts direct effects on the composition of gut\nmicrobiota, we conducted additional 16S rDNA sequencing analysis in healthy mice\nwithout intestinal inflammation. α-diversity analysis, as assessed by the Shannon index,\nrevealed no significant differences in bacterial richness and diversity between the two\ngroups (Supplementary Fig. 3c). The overall structure of the gut microbiota\ninvestigated using UniFrac-based PCoA indicated that the SP group displayed a shift\nclustering of bacterial composition, which was distinct from the CON group, suggesting\nthat SP treatment induced changes in gut microbiota composition (Supplementary Fig.\n3d). We then analyzed the gut bacterial composition at the genus levels. Results\nrevealed that SP significantly reduced the relative abundance of Limosilactobacillus\nand Bacteroides relative to control mice. Conversely, the abundance of\nunclassified_Muribaculaceae, Lachnospiraceae_NK4A136_group, and\n5\nunclassified_Lachnospiraceae was significantly increased in SP-treated mice compared\nto control mice (Supplementary Fig. 3e). The differences in the relative abundance of\ngut microbiota at the genus level between the CON group and the DSS group were\ndetermined using ANOVA (Supplementary Fig. 3f). These results suggest that SP has\nthe potential to modulate the composition of gut microbiota. Additionally, we carefully\nreferred to several researches. A study showed that SP could play a role in the\npulmonary clearance of Pseudomonas aeruginosa in mild traumatic brain injury (mTBI)\n[1]. Kowalska K et al. suggested that SP possesses direct antimicrobial activity, highest\nagainst S. aureus [2]. We provided these additional results in the revision (Result\nsection: Page 16-17, Line 349-364). The Figures are listed below.\nReferences：\n1. Hsieh T, Vaickus M H, Stein T D, et al. The role of substance P in pulmonary clearance of bacteria\nin comparative injury models[J]. The American Journal of Pathology, 2016, 186(12): 3236-3245.\n2. Kowalska K, Carr D B, Lipkowski A W. Direct antimicrobial properties of substance P[J]. Life\nsciences, 2002, 71(7): 747-750.\nin DSS-induced mice. c α-diversity represented by the Chao 1 index. d β-diversity of principal\ncoordinate analysis (PCoA). e Taxonomic distributions of gut bacterial composition at the genus\nlevel. f The ANOVA analysis for gut microbiota.\n3. The role of inositol in the responses to substance P is not proven simply by\ndemonstrating the effects of administered inositol. The data presented simply show\ninositol is a candidate mediator but causality cannot be claimed. The same point\napplies to the inositol intervention: would the anxiolytic effect be linked to the\ntreatment or the lower intestinal inflammation?\nwhich have enabled us to further improve our research. We performed additional\nexperiment to answer the two concerns raised by the reviewers one by one.\n(1) Firstly, we have revised the title of Fig. 8 from “Gut metabolite inositol mediates\nthe protective effects of SP against colitis and anxious behaviors in DSS-induced mice”\nto “Administration of inositol ameliorates DSS-induced colitis and anxiety disorders”.\nIn the Result section, we revised “These findings demonstrate that inositol, a crucial\nintermediate metabolite, mediated the process of SP protecting against DSS-induced\ncolitis and anxiety-like behavioral disorders involving the enhancement of GABAergic\nneurotransmission signaling” to “Collectively, these data suggest that inositol might\nserve as a critical candidate metabolite associated with the beneficial effects exerted by\nSP”.\nSecondly, to investigate the role of inositol in the action of SP against anxious-like\nbehaviors, we treated mice with an antagonist of inositol synthesis (L-690330). The\nmice were divided into four groups: (1) DSS group, (2) DSS+SP group, (3) and\nDSS+SP+L-690330 (inositol synthesis antagonist) group. The experimental design is\nshown in Fig. 9a. Behavioral analysis indicated that the anti-anxiety effects of SP were\nreversed by L-690330 treatment. Specifically, in the EPM test, colitis mice treated with\nboth SP and L-690330 spent significantly less time in the open arms, traveled shorter\ndistances in the open arms, and entered the open arms less frequently compared to SP-\ntreated colitis mice (Fig. 9b, c). In the EZM test, following L-690330 treatment, both\nthe duration spent in the open zone and frequency of entries into the open zone were\n8\nsignificantly reduced relative to SP-treated colitis mice (Fig. 9d, e). In the LDB test,\ncolitis mice treated with SP and L-690330 spent markedly less time exploring the light\nchamber and traveled a shorter distance in the light box compared to SP-treated colitis\nmice (Fig. 9f). However, no differences were observed in the transition frequencies\nbetween light and dark chambers among the three groups. To evaluate microscopic\nvariations in the hippocampus, we stained hippocampal sections with H&E (Fig. 9g\nand Supplementary Fig. 9a). Representative images revealed that administration of\nSP repaired DSS-induced pathological characteristics, including shrunken nerve cells,\npyknotic or indistinct nuclei, and an increase in injured neurons in the DG region.\nIntriguingly, the L-690330 treatment markedly reversed the protective influence of SP\non the hippocampus. Nissl staining revealed that SP mitigated the loss of Nissl bodies,\npyknotic and indistinct nuclei in the DG region, resulting in a significant increase in the\nnumber of surviving neurons. However, the administration of L-690330 effectively\nreversed these changes (Fig. 9h and Supplementary Fig. 9b). These results suggest\nthat inositol, as a crucial intermediate metabolite, plays an important role in the\nprotective effects of SP against DSS-induced anxiety-like behavioral disorders. We\nhave incorporated these additional descriptions into the revision (Result section: Page\n32-33, Line 695-721. Discussion: Page 49, Line 1058-1061. Method section: Page 54,\nLine 1170-1177) and the figures are presented below.\n(2) To address this question of whether inositol’s anxiolytic effect is linked to its own\ntreatment, or it caused by a reduction in intestinal inflammation, we conducted\nadditional experiments. Firstly, we examined the concentration of inositol in both\n9\nhippocampal tissue and serum using ELISA. As expected, the inositol level in the\nhippocampus of the FMT-DSS group was markedly lower than that in the FMT-CON\ngroup. However, a significant upregulation of inositol was detected in the FMT-SP\ngroup in comparison with the FMT-DSS group (Fig. 9i). Similar trends were observed\nin the serum (Fig. 9j).\nSecondly, Furthermore, we performed additional in vitro experiments to explore\nthe functional activity of inositol by culturing mouse microglial cell lines (BV-2) and\nmouse astrocyte cell lines (C8D1A). Results revealed inositol improved LPS-induced\nneuroinflammatory responses in BV-2 cells, and repressed the down-regulation of the\nGABAergic/Ca2+ signaling pathway induced by LPS in C8D1A cells. More detailed\nresults are as follows:\nBV-2 cells treated with different concentrations of inositol (25, 50, and 100 μM)\nfor 24 h exhibited no discernible cytotoxic effects (Supplementary Fig. 9c). Further\nRT-qPCR analysis indicated that mRNA levels of TNF-α, IL-1β, and IL-6 were down-\nregulated at concentrations of 25 and 50 μM in the inositol groups, although these levels\nwere not significantly different from those in the LPS group (Fig. 9k-m). Importantly,\ntreatment with 100 μM inositol significantly reduced the levels of these inflammatory\nmediators. Additionally, the mRNA expression of iNOS in all inositol groups was lower\nthan that observed in the LPS group, although no statistically significant differences\nwere noted (Fig. 9n). C8D1A cells were cultured with inositol for 24 h. The results\nrevealed that inositol exhibited no significant toxicity to C8D1A cells at concentrations\nof 50, 100 and 200 μM (Supplementary Fig. 9d). Subsequently, C8D1A cells were\n10\ntreated with inositol under stimulation of LPS for 24 h. Compared to the LPS exposure\nalone, inositol treatment increased the mRNA levels of Gabara3 and Camk2d, with the\nmost pronounced effects observed at a concentration of 200 μM (Fig. 9o, p). Consistent\nwith these findings, western blot analysis illustrated that LPS exposure reduced the\nprotein expression of GABA R1, CaMKII and GABA R in C8D1A cells. In contrast,\nA B\nsupplementation with inositol at both 100 and 200 μM markedly suppressed the\ndownregulation of these protein expressions (Fig. 9q). We have incorporated these\nadditional descriptions into the revision (Result section: Page 33-35, Line 722-752.\nDiscussion: Page 49, Line 1061-1066. Method section: Page 67-68 Line 1459-1486)\nand the figures are presented below.\nNotably, in this study, we investigated the effects of inositol on intestinal\ninflammation and discovered that inositol possesses anti-inflammatory properties.\nPeripheral intestinal factors can exacerbate brain inflammation by crossing the blood-\nbrain barrier via the bloodstream, potentially triggering anxiety disorders. Thus, the\nanti-inflammatory effects of inositol may also play a role in alleviating anxiety. In\nsummary, inositol may exert anti-anxiety effects by directly influencing brain function,\nwhile also potentially reducing anxiety-like behaviors indirectly through the mitigation\nof intestinal inflammation.\ninduced mice. a Schematic illustration of experimental design. b,c Track plot of the elevated plus\nmaze, and statistical analysis including time spent in open arms, distance in open arms and times of\nopen arm entries. n=6/group. d, e Track plot of the elevated zero maze, and statistical analysis\nincluding time spent in the open zone and times of entries into open zone. n=6/group. f Statistical\nanalysis including distance in the light area, time spent in the light area, and times of transitions in\nthe light-dark box test. n=6/group. g Representative images of H&E-staining for the hippocampus\nsections. Scale bar = 100 μm. n=6/group. h Representative images of Nissl staining for the\nhippocampus sections. Scale bar = 100 μm. n=4/group. i The content of inositol in hippocampus\ntissue tested by ELISA. n=8/group. j The content of inositol in serum tested by ELISA. n=8/group.\nbehaviors in DSS-induced mice. a Quantification of cells with lesions in the hippocampus.\nn=6/group. b Quantification of the viable neurons in the hippocampus. n=4/group. c Viability of\nBV-2 cells. n=4/group. d Viability of C8D1A cells. n=4/group. Data are expressed as the mean ±\nSD. Statistical significance was determined using Student’s t test. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001.\nDSS-induced mice. i The content of inositol in hippocampus tissue tested by ELISA. j The\ncontent of inositol in serum tested by ELISA. k-n The mRNA expression of TNF-α, IL-1β, IL-6\nand iNOS in BV-2 cells. o, p The mRNA expression of Gabara3 and Camk2d in C8D1A cells. q\nThe protein level of GABA R, CaMKII, GABA R detected by western blot.\nA B\n4. There is concern regarding the assessment of anxiety-like behavior. While there is\na plethora of tests available, the authors chose the open field test which is primarily\na test of movement and therefore malaise rather than anxiety. Indeed groups\nassociated with anxiety-like behaviour also had a reduction in their overall mobility.\nResponse: Thank the reviewer for this important comment. Elevated plus-maze (EPM)\nand Light and dark box (LDB) are commonly used in rodents to assess the exploratory\nand locomotor activity, unconditioned fear, and potential anxiety [1]. We used the\nelevated zero maze (EZM) test since it as a measure of negative affective behavior and\nits predictive validity was demonstrated [2]. We conducted more behavioral tests,\nincluding EPM, LDB and EZM were used to detect the effect of SP and inositol on\nanxious-like behavioral disorders in DSS-induced mice. More detail, as shown below.\nThe results of EPM demonstrated that the DSS group exhibited a reduced distance\ntraveled, less time spent in the open arms, and fewer entries into the open arms\ncompared to the CON group. In contrast, SP treatment effectively reversed DSS-\ninduced behavioral alterations, increasing the distance traveled, time spent in the open\narms, and frequency of entries into the open arms (Fig. 1m, n). (Results section: Page\nSecondly, in the EZM test, the distance traveled, time spent in the open zone, and\nfrequency of entries into the open zone in the DSS group were significantly lower than\nthose in the CON group. However, the DSS+SP group showed a significantly greater\ndistance traveled and time spent in the open zone compared to the model group,\nalthough SP treatment did not significantly affect the number of entries into the open\nzone (Fig. 1o, p). These additional results have been added to the revision (Result\nsection: Page 11, Line 228-234; Method section: Page 59, Line 1278-1285). Figures\nare shown below.\ntraveled a shorter distance in the light box compared to the CON group, indicating that\nDSS exposure decreased the exploration desire of the mice (Fig. 1q). Conversely, SP\ntreatment significantly increased the time spent and distance traveled in the light box.\nAdditionally, the mice could freely move between the light and dark boxes, with the\nnumber of passages through the door shown in Fig. 1q. However, no differences were\nobserved in the transition frequencies between the light and dark boxes among the four\ngroups. These additional results have been added to the revision (Result section: Page\n11, Line 234-241; Method section: Page 58-59, Line 1269-1277). Figures are shown\nbelow.\nAdditionally, we also to further evaluate the influences of inositol on behaviors of\nmice using EPM test. The results showed that Compared to the DSS group, behavioral\nsymptoms showed an improvement in the DSS+inositol group, which exhibited an\nobvious increase in the time spent within the open arms. Mice treated with inositol also\nentered the open chamber more frequently than DSS-treated mice. Notably, the\ndifferences in behavioral tests between the CON group and the inositol alone group did\nnot reach statistical significance (Fig. 8j, k). These additional results have been added\nto the revision (Result section: Page 30, Line 644-650), and figures are shown below.\nReferences\n1. Zhou F, Jiang H, Kong N, et al. Electroacupuncture attenuated anxiety and depression‐like\nbehavior via inhibition of hippocampal inflammatory response and metabolic disorders in TNBS‐\ninduced IBD rats[J]. Oxidative Medicine and Cellular Longevity, 2022, 2022(1): 8295580.\n16\n2. Bloch S, Rinker J A, Marcus M M, et al. Absence of effects of intermittent access to alcohol on\nnegative affective and anxiety-like behaviors in male and female C57BL/6J mice[J]. Alcohol,\n2020"}]

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