Effects of Long-term Microgravity Exposure on Liver Activity and the Gut Microbiota as well as Gut-liver Axis Homeostasis

Effects of Long-term Microgravity Exposure on Liver Activity and the Gut Microbiota as well as Gut-liver Axis Homeostasis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of Long-term Microgravity Exposure on Liver Activity and the Gut Microbiota as well as Gut-liver Axis Homeostasis Pu Chen, Junli Chen, Nan Xu, Weiran Wang, Lingwei Hou, Bowen Sun, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4190281/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2026 Read the published version in BMC Microbiology → Version 1 posted 13 You are reading this latest preprint version Abstract Background: Recent advances in understanding gut‒liver axis homeostasis have been made because of the promising beneficial effects of these systems on health maintenance and performance promotion. However, little is known about the effects of long-term microgravity exposure on the gut-liver axis or about effective countermeasures to prevent disruptions in gut-liver axis homeostasis. Hence, we conducted a well-controlled study to determine the effects of long-term microgravity exposure on liver activity, the gut microbiota and gut-liver axis homeostasis via a hindlimb suspension rat model. Results: Interestingly, long-term microgravity exposure increased lipid deposition, oxidative stress and inflammation in the liver; increased proportions of opportunistic enteric pathogens; and disrupted intestinal barrier integrity, paralleling with dysregulation of gut-liver axis homeostasis, which especially underlined portal influx of secondary bile acid (mainly ursodeoxycholic acid and lithocholic acid). Notably, metabolites (mostly prostaglandins, kynurenine and derivatives) derived from the liver reflected the aggravating oxidative stress and inflammation and were strongly associated with those from the colon. In addition, the gut microbiota played a vital role in cometabolism pathways of aminoacyl-tRNA biosynthesis, vitamin B6 metabolism, alanine, and aspartate and glutamate metabolism, which may emphasize the critical role of microbial homeostasis in maintaining liver activities as well as intestinal barrier integrity upon microgravity. Conclusions: Taken together, our findings suggest that enteric microorganism is an effective target for maintaining gut-liver axis homeostasis as well as protecting astronauts from inflammation when deal with microgravity exposure in further long-term manned space mission. Microgravity exposure gut-liver axis intestinal barrier hindlimb suspension rat model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background For decades, huge developments in spaceflight have been made owing to the use of more accurate monitoring technology and systematic human health maintenance strategies. However, the effects of long-term space travel on physiological and pathological processes and the resulting impacts on crew health and operational performance have not been fully elucidated. Recently, spaceflight-associated changes in human health due to complex arrays of environmental stressors (mostly weightlessness, radiation, and isolation) were transcendentally characterized through multiomics approaches in which one of two pairs of monozygotic twin astronauts executed a 1-year mission. This integrated and multiomics analysis revealed both transient and persistent metabolic profiles, immunomodulatory effects and gut microbiota shifts that constitute spaceflight-dependent physiological changes during the 1-year follow-up[ 1 ]. Another spaceflight study provided evidence that intestinal microflora was defined by increased abundance of opportunistic pathogens and reduced abundance of beneficial bacteria for astronauts whom executed missions aboard the space vehicles Salyut/Soyuz and Mir for up to 96 days[ 2 ]. In addition, studies of Mice Flowening Aboard the Space Transportation System-135 have shown that a 13-day flight duration caused activation of PPARα-mediated pathways and potentially hepatic stellate cell activation, both of which may be coincident with increased bile acids and early signs of liver injury, increasing the risk for nonalcoholic fatty liver disease[ 3 ]. Among the spaceflight-associated factors, microgravity is the primary factor experienced during spaceflight and can affect a number of physiological processes in various organs, and rise changes in microbial complexities and diversity to astronaut [ 4 ]. In particular, the National Aeronautics Institute (NASA)-led rodent research 5 mission demonstrated that constant microgravity exposure processed elevated gut microbial diversity and alters the concentrations of the metabolite lactic acid, leucine/isoleucine, and glutathione, subsequently promoting osteoblastogenic activation[ 5 ]. Moreover, considerable alterations in the growth rate and secondary metabolism of highly evolved microbes have been viewed in spaceflight and ground-based microgravity analog experiments[ 6 ]. Although prospective studies in regard to humans in space never stop increasing, we have limited knowledge of the full range of underlying molecular mechanisms, physiological processes, and integrated crosstalk that occurring during long-term spaceflight. Growing evidences has proven that intestinal microbiota dysbiosis sets the stage for impairments in intestinal epithelial barrier function, bile acid signaling, and intestinal immunity and consequently promotes liver insufficiency, intestinal hypomotility and interrelated enterohepatic problems[ 7 ]. In fact, microbial physiological effects mostly rely on microbial fermentation, and gut-derived products exert beneficial (such as short-chain fatty acids) or proinflammatory (such as lipopolysaccharide) effects on the host, which can reach the liver through the portal vein. In turn, in the bidirectional communication of the gut-liver axis, the liver shapes intestinal microbial communities through the enterohepatic circulation of bile acid[ 8 ]. Interestingly, recent evidence indicates that shifting levels of microbial intermediate metabolites lead to changes in molecular activities, for example, intestinal farnesoid X receptor (FXR) signaling, which contributes to host physiology[ 7 – 9 ]. Taken together, these findings led us to speculate that spaceflight-dependent microgravity exposure may cause disorder to gut microbiota and its secondary metabolism, which subsequently contribute to gut barrier disruption and shape liver insufficiency. To confirm this speculation, we conducted metabolic profiling of both the liver and colon, as well as 16S ribosomal RNA gene sequencing of resident microbes in specimens from rats whom suffering microgravity exposure via hindlimb suspension-a well-established microgravity analog model. Interestingly, our viewpoints were further verified by observing hepatic lipid accumulation, oxidative stress and inflammation, altered enteric microorganisms and intestinal barrier damage, paralleling with dysregulated portal metabolic flux, thus providing insight into the importance of maintaining gut-liver axis homeostasis in future manned spaceflight. Methods Animals and reagents Sprague‒Dawley rats aged 6 weeks and male with specific pathogen-free (SPF) and sterile reproduction feed (SPF-F01) were purchased from SPF (Beijing) Biotechnology Co., Ltd. (license number: SCXK(京)2019-0010). Vancomycin hydrochloride (#V820413) was obtained from MACKLIN. Chloral hydrate (#23100), sodium chloride (#S0817) and paraformaldehyde (#158127) were obtained from Sigma Aldrich. Rat lipopolysaccharide (LPS) ELISA kit (#JM-10910R1), Rat lipopolysaccharide binding protein (LBP) ELISA kit (#JM-10507R1), Rat D-Lactate ELISA kit (#JM-10501R1), Rat diamine oxidase (DAO) ELISA kit (#JM-02209R1), Rat secretory immunoglobulin A (sIgA) ELISA kit (#JM-01836R1), Rat calprotectin (CALP) ELISA kit (#JM-02086R1), Rat chromograninA (CgA) ELISA kit (#JM-10970R1), Rat total cholesterol (TC) ELISA kit (#JM-02017R1), Rat triglyceride (TG) ELISA kit (#JM-02016R1), Rat alanine transaminase (ALT) ELISA kit (#JM-10921R1), Rat aspartate aminotransferase (AST) ELISA kit (#JM-10641R1), Rat malondialdehyde (MDA) ELISA kit (#JM-10323R1), Rat glutathione peroxidase (GSH-Px) ELISA kit (#JM-02173R1), Rat superoxide dismutase (SOD) ELISA kit (#JM-01793R1), Rat cytokines TNF-α ELISA kit (#JM-01587R1), Rat cytokines IL-6 ELISA kit (#JM-01597R1), and Rat cytokines IL-1β ELISA kit (#JM-01454R1) were from JINGMEI Biotechnology Co., Ltd. (JiangSu, China). Animal modeling A total of 72 Sprague‒Dawley rats (200–250 g) aged 6 weeks were subjected to 12 h day/night cycles at a constant temperature of 22 ± 2°C and 60% humidity. After 1 week of acclimation to the animal facilities and behavioral test regimens, the rats were randomly assigned to six groups (n = 12 per group), and the microgravity rats were generated using hindlimb unloading approaches as described previously[ 10 ]. Briefly, all the groups were fed a normal diet and had free access to water. Separately, (1) control group (Con group), wherein rats were fed without treatment, (2) vehicle group (GSW group), wherein rats were gave with sterile water via intragastric administration, (3) intestinal microbiota damaged group (Van group), wherein rats were gave with antibiotic water containing vancomycin hydrochloride at dose 0.05 g/kg/d via intragastric administration, (4) microgravity exposure group (TSS), wherein rats were treated with tail-suspension to the extent of hindlimb unloading, (5) intestinal microbiota damaged combined with microgravity exposure group (Van + TSS), wherein rats were treated with tail-suspension and vancomycin at dose 0.05 g/kg/d via intragastric administration, and (6) microgravity exposure combined with vehicle group, wherein rats were treated with tail-suspension and sterile water via intragastric administration. The intragastric volume was maintained at 1 mL. Body weight was measured at baseline and at 7-day intervals until euthanasia. After 35 days feeding, all SD rats was intraperitoneal 8.0% sodium pentobarbital with a dose of 800 mg/kg body weight for euthanasia and the injection was intermittent. All the procedures were performed in accordance with the Guidelines in the Care and Use of Animals and approved by the China Astronaut Research and Training Center Animal Welfare Committee (Permit Number: ACC-LACUC-2021-014). After 35 days of feeding and modeling, fresh feces were collected into microtubes. Then, the rats were sacrificed, and blood was collected via cardiac puncture into EDTA and heparin sodium blood collection vessels. Next, the blood was transferred to 4°C for 2 hours. Subsequently, the blood was centrifuged at 1000×g and 4°C for 20 min to obtain serum and plasma samples, and all the samples were immediately stored at -20°C for 30 min and subsequently transferred to -80°C before biochemical evaluation. Furthermore, liver and colon tissue samples were dissected and rapidly frozen. A portion of the tissue was washed with 0.85% sodium chloride and sufficiently fixed in 4% paraformaldehyde (10x). H&E Staining The liver and colon hematoxylin-eosin (H&E) staining protocol was performed according to previous manuals[ 11 ]. Briefly, liver and colon tissues were sliced and washed with a gradient of ethyl alcohol (#459836) starting from 50%, followed by 70%, 85%, 95% and HPLC to remove residual paraformaldehyde, each for 1 h. The remaining alcohol was subsequently exchanged for xylene (#214736) for 1 h. The tissues were subsequently infused in a mixture of isopyknic liquid paraffin and xylene for 1 h, followed by incubation in paraffin for 3 h. Subsequently, the embedded colon blocks were sliced into “Swiss Rolls” on the vertical side, and the liver blocks were sliced into 12-µm-thick sections. Later, the “Swiss Rolls” and liver sections were stained with hematoxylin solution for 3–5 min, differentiated with hydrochloric acid aqueous solution, blue-reacted with ammonia aqueous solution, and washed with distilled water. Adjacently, the tissues were washed with dehydration ethyl alcohol at a gradient of 85% and 95% alcohol and subsequently stained with eosin for 5 minutes. After sealing with neutral resins, microstructure scanning was performed on a 3D HISTECH (version 2.3.2), and images were acquired on a CaseViewer (version 2.4). Oil red O staining The morphology of the lipids and lipids in the liver was detected using oil red O staining according to previous protocols [ 12 ], which included tissue collection, sectioning, staining, and imaging. Briefly, liver tissues were sectioned into 12-µm-thick sections, after which ~ 1 ml of ORO working solution was added (as specified in the protocol) to cover the sections. Next, the sections were incubated with ORO working solution for 5 min and then rinsed with the sections in a slide holder, while the biopsies were facing away from the running water. Then, coverslips were placed on the slide holder, and the sections were checked by using a scanning microscope for image capture. ELISA Enzyme-linked immunosorbent assay (ELISA) analysis of rodent serum LPS, LBP, DAO, D-lactate, ALT, AST, plasma sIgA, calprotein and CgA as well as MDA, GSH-Px, SOD, TNF-α, IL-6, and IL-1β in liver homogenates was completed using an ELISA kit and a SYNERGY H1 microplate reader (BioTek, USA) according to the manufacturer’s operation manual. Briefly, serum, plasma and liver tissue homogenates were thawed on ice, assayed in triplicate and diluted 5-fold according to the manufacturer’s recommendations. After that, the samples, controls, and calibrators were incubated, coated, washed, colored and read step by step according to the manufacturer’s protocols. 16S rRNA gene Sequencing Fecal DNA was extracted via Fast DNA SPIN extraction kits (MP Biomedicals, Santa Ana, CA, USA), and its quantity and quality were verified using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively. The V3–V4 region of the bacterial 16S rRNA gene was amplified via PCR for 16S rDNA amplicon pyrosequencing on the Illumina MiSeq platform[ 13 ]. Afterwards, the sequencing data were analyzed by Quantitative Insights Into Microbial Ecology (QIIME, v1.8.0) as previously described[ 14 ]. Finally, statistical analysis, including alpha diversity indices, differences, and similarities, was performed using the QIIME and R packages. Metabolic analysis Liver and colon tissues (100 mg) were individually ground with liquid nitrogen, and the homogenate was resuspended in prechilled 80% methanol by vortexing. These samples were incubated on ice for 5 min and then centrifuged at 15,000 × g and 4°C for 20 min. A portion of the supernatant was diluted to the final concentration with LC‒MS grade water containing 53% methanol. The samples were subsequently transferred to a fresh Eppendorf tube and then centrifuged at 15000 × g and 4°C for 20 min[ 15 ]. Finally, the supernatant was injected into the UHPLC‒MS/MS system for analysis using a Vanquish UHPLC system (Thermo Fisher, Germany) coupled with an Orbitrap Q Exactive ™ HF mass spectrometer (Thermo Fisher, Germany) as described previously[ 16 ]. The raw data generated by UHPLC‒MS/MS were processed using Compound Discoverer 3.1 (CD3.1; Thermo Fisher) to perform peak alignment, peak picking, and quantitation for each metabolite. Then, metabolite identification was conducted using the KEGG database ( https://www.genome.jp/kegg/pathway.html ), HMDB ( https://hmdb.ca/metabolites ) and LIPID Maps database ( http://www.lipidmaps.org/ ). Data processing was performed step by step with metaX[ 17 ] (a flexible and comprehensive software package for processing metabolomics data) according to the documented protocol[ 17 ]. Statistical analyses were performed using the statistical software R (version R-3.4.3), Python (version Python 2.7.6) and CentOS (release 6.6). Results Body weight The body weights (BWs) of the subjects constantly increased during the intervention. Notably, the hindlimb unloading groups (TSS, Van + TSS, and GSW + TSS) exhibited a lower growth rate than their reference groups (Con, Van, and GSW). In addition, after 35 days feeding, the gap between BWs of rats from group Van + TSS and that from group Van had significantly increased when compared with the gap between BWs of rats from group TSS and from group Con(Fig. 1 ). Microgravity exposure induced lipid deposition and inflammation within the liver The liver is the central metabolic organ and is the first bypass organ through which nutrients are absorbed in the human body and rodents; moreover, the liver is susceptible to and prone to complications from microgravity when stimulated by TSS. As characterized by liver hematoxylin-eosin (H&E) staining, TSS treatment and vancomycin administration resulted in a loose tissue structure and a decreased total cell area, and vancomycin administration aggravated the liver tissue dispersion in rats treated with the tail suspension (Fig. 2 a and c). Moreover, liver oil red O staining indicated that TSS treatment promoted lipid deposition in the liver of rats from group TSS, GSW + TSS, and Van + TSS. As expected, vancomycin administration also accentuated liver lipid deposition in the group Van + TSS (Fig. 2 b and d). In agreement with the above findings, the levels of total cholesterol (TC) in liver tissue homogenates were significantly greater in group which combined TSS treatment with vancomycin administration (Fig. 2 e), and total triglyceride (TG) was significantly greater in all TSS-related treatment groups (including the TSS, GSW + TSS, and Van + TSS groups) (Fig. 2 f). To further evaluate the degree of liver damage, the concentrations of serum alanine transaminase (ALT) and aspartate aminotransferase (AST) were measured. Unexpectedly, both the two serum biomarkers did not significantly increase in TSS-related groups (Fig. 2 g, 2 h). Oxidative-related factors in the liver were subsequently evaluated, including malondialdehyde (MDA), glutathione peroxidase (GSH-Px), and superoxide dismutase (SOD). As shown in Fig. 2 i, the levels of liver MDA in group Van, TSS, Van + TSS and GSW + TSS were significantly greater than those in their comparable groups. Moreover, compared with corresponding controls, the concentrations of GSH-Px in liver homogenates from rats of group Van, Van + TSS and GSW + TSS were also obviously greater (Fig. 2 j). In contrast, the levels of SOD in liver homogenates from group GSW + TSS were markedly lower than it from group GSW (Fig. 2 k). Furthermore, the concentrations of proinflammatory cytokines (TNF-α, IL-6, and IL-1β) in liver homogenates were also detected. The results showed that the levels of TNF-α in group Van were greater than that in group GSW, and were lower in both of group Van + TSS and GSW + TSS than those in their comparable group Van and GSW (Fig. 2 l). Moreover, the levels of IL-6 in group TSS, Van + TSS, and GSW + TSS were significantly greater compared with their control groups (Fig. 2 m). Simultaneously, the levels of IL-1β exhibited a significant increase in the GSW + TSS group, whereas the other TSS-related groups (TSS and Van + TSS) did not exhibit any changes (Fig. 2 n). Integrality of the colonic mucosa The colon mucosa is composed of the intestinal epithelium, lamina propria, muscle and so on. As shown in H&E images of colon, the rats subjected to TSS treatment and vancomycin administration owned a colon which presented with sparse and narrow mucosa, irregular crypt bases, rough epithelium surfaces, and damaged lamina propria in some niches whereas their control rats did not (Fig. 3 a). In contrast to rats who were dealt with vancomycin administration, rats suffering from TSS treatment had better colon mucosa, who only damaged in partial segment, whereas vancomycin administration damaged the whole mucosa. Moreover, we detected 7 serum/plasma biomarkers associated with the permeability of intestinal barrier function, namely, lipopolysaccharide (LPS), lipopolysaccharide binding protein (LBP), D-lactate (D-Lac), diamine oxidase (DAO), secretory immunoglobulin A (SIGA), calprotectin (CALP) and chromogranin A (CGA) (Fig. 3 b). Interestingly, the serum microbial endotoxin LPS was significantly increased in group TSS ( p = 0.00014), and the serum D-lactate was increased in group TSS ( p = 0.013) and GSW + TSS ( p = 0.012). In response to the endotoxin, the concentration of serum LBP was obviously lower in group TSS ( p = 0.04), Van ( p = 2.86×10 − 4 ), Van + TSS ( p = 0.038) and GSW + TSS ( p = 4.98×10 − 7 ). Moreover, the concentrations of serum intracellular enzyme DAO whom oxidizes diamines (histamine, putrescine and cadaverine) were also markedly lower in group TSS ( p = 0.0036) and Van + TSS ( p = 5.92×10 − 4 ) than their comparable groups. Unexpectedly, the SIGA did not significantly change during TSS or vancomycin treatment, except for a mild decrease in group TSS ( p = 0.16). CALP is a calcium-containing protein derived from neutrophils and macrophages which plays a role in anti-inflammatory and immunologic enhancement. Interestingly, the plasma CALP was expressed in TSS-dependent manner. Namely, the levels of plasma CALP were consistently lower in rats among group TSS ( p = 4.38×10 − 5 ), Van + TSS ( p = 8.37×10 − 6 ) and GSW + TSS ( p = 1.04×10 − 5 ). In addition to CALP, another immune related protein-CGA, had increased concentrations in plasma from rats among group TSS ( p = 0.024) and GSW + TSS ( p = 0.044). Dysfunction of the gut microbiota caused by microgravity exposure The relative abundance of fecal microbiota in group TSS was characterized by decreasing proportions of genus Lactobacillus , Prevotella and increasing genus Shigella , Ruminococcus , Desulfovibrio , and Coprococcus when compared with group Con. Reference rats treated with vancomycin can destroy microbiota. Consistent with these findings, when rats in the Van group were treated with vancomycin, the intestinal microbiota exhibited a remarkable increase in the abundance of the detrimental genera Shigella , Enterococcus , Morganella , Desulfovibrio , and Phascolarctobacterium and few comparable beneficial genera Ruminococcus , Oscillospira and Coprococcus compared with those in the GSW group. Furthermore, when the plants were overlaid with microgravity, the Van + TSS group presented a lower proportion of Lactobacillus and greater abundances of Shigella and Enterococcus than did the Van group (Fig. 4 a). The taxonomic classification tree evolved with tail-suspension and vancomycin treatment, during which the phylum Gammaproteobacteria evolved into the group Van and Van + TSS, whereas TSS treatment influenced mainly gut microorganisms at the genus level; these changes included the evolution of Lactobacillus , Streptococcus, Coprococcus, Oscillospira, and Shigella (Fig. 4 b). The microbial α diversity measures the richness, diversity, and evenness of taxonomy, of which the Chao1 estimator reflects the microbial richness by calculating “Singleton” together with “Doubleton” in the operational taxonomic unit (OTU). According to the sparse curves, the Chao1 indices increased with sequencing depth and equilibrated at nearly 20,000 depths, which indicated that the obtained OTUs were sufficient for feature extraction. In particular, the Van and Van + TSS groups had markedly lower α diversity indices than did the other groups, and the Con group had the highest α diversity index (Fig. 4 c). In contrast to that of α diversity, the microbial β diversity differed according to treatment. Except for the Van and Van + TSS groups, which were strongly different from the control group, the other groups exhibited similar β diversity in terms of microbial community structure (Fig. 4 d). The core and unique OTUs were counted in a Venn diagram. Herein, the TSS group had markedly fewer unique OTUs than did the Con group (from 4076 to 1419). Similarly, the number of unique OTUs in the Van combined with Van + TSS group also decreased compared with that in the GSW or GSW + TSS groups (Fig. 4 e). Finally, biomarkers of the microbiota from each group were screened via linear discriminant analysis (LDA) effect size (LEfSe) analysis, which integrates the nonparametric Kruskal‒Wallis test and Wilcoxon rank sum test with linear discriminant analysis (LDA). According to the LDA scores, the intestinal microbial communities of rats in the TSS group were characterized by the genera Streptococcus, Candidatus_Solibacter, Tepidimicrobium, Butyrivibrio, Anaerotruncus and some other unknown genera. Furthermore, with increasing vancomycin concentration, the microbial ecology of rats in the Van + TSS group was enriched for the genera Bilophila, Sutterella, Akkermansia, and Enterococcus and some other unknown genera (Fig. 4 f). Metabolic profiling on liver and colon The supernatants extracted from rats’ liver and colon were subjected to metabolic profiling via ultrahigh-performance liquid chromatography coupled with Orbitrap Exploris mass spectrometry (UPLS-MS 2 ) to reveal changes in the gut-liver axes. Notably, the TSS treated groups, including group TSS, GSW + TSS and Van + TSS, exhibited similar projections in hepatic metabolic profiles and processed distinguishable distances far away from their reference groups (Con, GSW, and Van) which distributed along with the x-axis in the score projection space which was modeled by orthogonal signal correction-orthogonal partial least squares-discriminant analysis (O 2 PLS-DA). Furthermore, vancomycin treatment induced distinct clustering which distributed along with the y-axis for liver metabolism when combined with TSS (Fig. 5 a). In contrast to liver metabotyples, colon metabolic profiles had shown to be strongly related to the impactions of TSS and sensitive to vancomycin treatment as characterized by distinct clustering which distributed along with x-axis (Fig. 5 e). Liver and colon metabolic shifts were further verified by heatmaps of metabolite intensity in which liver metabolic profiles appeared in according to TSS associated clustering whereas the colon presented vancomycin related clustering (Fig. 5 b, f). Furthermore, the characteristic metabolites were screened by the VIPs (predictive variable importance in the projection from O 2 PLS-DA), FC (fold change), and P value (one-way ANOVA with standard Bonferroni correction) (Table 1 ). As shown by the set of liver- and colon-specific metabolites, a total of 1265 metabolites (907 from the positive ion mass spectrum and 358 from the negative ion mass spectrum) were identified in the liver and colon, among which 164 metabolites from liver were significantly upregulated and 111 downregulated along with TSS treatment. Yet, 270 upregulated and 74 downregulated liver metabolites were associated with treatment which combined TSS with vancomycin administration. In contrast, few metabolites (a total of 17) from colon shown to be sensitive to TSS treatment, but many (62 upregulated and 99 downregulated in the Van + TSS group versus the Van group) changed with vancomycin treatment which could destroy the microbiota. Table 1 Overview of significant metabolites between differential treatment groups according to VIP, FC and P value Compared Treatments 1 Tissue Num. of Total Ident. 2 Num. of Total Sig. 3 Num. of Sig.Up 4 Num. of Sig.down 5 Positive Negative Positive Negative Positive Negative Positive Negative Con.vs.GSW Liver 907 358 41 31 15 22 26 9 Van.vs.GSW 907 358 69 33 40 19 29 14 Van + TSS.vs.Van 907 358 211 133 151 119 60 14 TSS.vs.Con 907 358 199 75 117 47 82 28 GSW + TSS.vs.GSW 907 358 319 113 179 90 140 23 GSW + TSS.vs.Van + TSS 907 358 136 75 44 19 92 56 Con.vs.GSW Colon 907 358 48 10 23 3 25 7 Van.vs.GSW 907 358 241 104 181 74 60 30 Van + TSS.vs.Van 907 358 111 50 40 22 71 28 TSS.vs.Con 907 358 14 3 6 2 8 1 GSW + TSS.vs.GSW 907 358 206 62 77 28 129 34 GSW + TSS.vs.Van + TSS 907 358 246 110 73 30 173 80 Table 1 Overview of significant metabolites between Compared Treatments screening by VIP, FC and P-value Compared Treatments 1 Tissue Num. of Total Ident. 2 Num. of Total Sig. 3 Num. of Sig.Up 4 Num. of Sig.down 5 Positive Negative Positive Negative Positive Negative Positive Negative Con.vs.GSW Liver 907 358 41 31 15 22 26 9 Van.vs.GSW 907 358 69 33 40 19 29 14 Van + TSS.vs.Van 907 358 211 133 151 119 60 14 TSS.vs.Con 907 358 199 75 117 47 82 28 GSW + TSS.vs.GSW 907 358 319 113 179 90 140 23 GSW + TSS.vs.Van + TSS 907 358 136 75 44 19 92 56 Con.vs.GSW Colon 907 358 48 10 23 3 25 7 Van.vs.GSW 907 358 241 104 181 74 60 30 Van + TSS.vs.Van 907 358 111 50 40 22 71 28 TSS.vs.Con 907 358 14 3 6 2 8 1 GSW + TSS.vs.GSW 907 358 206 62 77 28 129 34 GSW + TSS.vs.Van + TSS 907 358 246 110 73 30 173 80 The significant metabolites were screened by VIP (Variable Importance in the Projection), FC(Fold Change), and P-value, VIPs were from O 2 PLS-DA (orthogonal signal correction-orthogonal partial least squares-discriminant analysis);P-value was calculated by one-way ANOVA with standard Bonferroni correction. The threshold values were set as VIP > 1.0, FC > 1.2 or FC < 0.833 and P-value < 0.05. 1. Compared Samples: compared treatments which compare the former treatment with the latter; 2. Num of Total Ident: total numbers of identified metabolites from MS༛3. Num of Total Sig: total numbers of significant metabolites; 4. Num of Sig Up: total numbers of significant up-regulated metabolites; 5. Num of Sig down: total numbers of significant down-regulated metabolites。 The significant metabolites were screened by variable importance in projection (VIP), fold change (FC), and P value. VIPs were identified via orthogonal signal correction-orthogonal partial least squares-discriminant analysis (O2PLS-DA). The P value was calculated via one-way ANOVA with standard Bonferroni correction. The threshold values were set as VIP > 1.0, FC > 1.2 or FC < 0.833 and P value < 0.05. 1. Comparison of Samples: Comparisons of the former treatment with the latter; 2. Num of total identity: total number of identified metabolites from MS; 3. Num of total Sig: total number of significant metabolites; 4. Num of Sig Up: total number of significantly upregulated metabolites; 5. Num of Sig: total number of significantly downregulated metabolites. Metabolite pathway enrichment analysis (MPEA) was performed for physiological interpretation base on metabolites screening from various treatments. The pathways mostly enriched for TSS related metabolites derived from liver were defined as aminoacyl-tRNA biosynthesis; mineral absorption; serotonergic synapse; valine, leucine and isoleucine biosynthesis; tryptophan metabolism; and vitamin B6 metabolism. And, the colon-derived metabolites were associated with the pathway of gastric acid secretion (Table 2 ). Furthermore, the rats whom received vancomycin administration in combination with TSS treatment enriched pathways which hit many proinflammatory metabolites. Namely, the liver exhibited changes in arachidonic acid metabolism, the oxytocin signaling pathway, and renin secretion, and the colon exhibited changes in glycerophospholipid metabolism (Table S1 ). Table S1 KEGG enrichment of characteristic metabolites between the Van + TSS group and the Van group. Tissue MapID MapTitle Pvalue MetaIDs Liver map00590 Arachidonic acid metabolism 0.015 Prostaglandin E2; Prostaglandin H2; Lipoxin B4; Thromboxane B2; Prostaglandin D2; 16(R)-HETE; Arachidonic acid; Prostaglandin J2; 5-OxoETE map04924 Renin secretion 0.044 Prostaglandin E2; Adenosine; Adenosine 5'-monophosphate Colon map00564 Glycerophospholipid metabolism 0.009 O-Phosphorylethanolamine; Phosphoethanolamine; Phosphocholine; Cytidine 5'-diphosphocholine MapID: map ID of the enriched KEGG pathways. MapTitle: title of enriched KEGG pathways. P value : Overrepresentation analysis was implemented using a hypergeometric test to evaluate whether a particular metabolite set was represented more than expected by chance within the given compound list. One-tailed p values are provided after adjusting for multiple testing. MetaIDs: List of the input metabolites that participated in the KEGG pathway. Table 2 KEGG enrichment of liver metabolites derived from the liver and colon between the TSS and Con treatment groups Tissue MapID MapTitle Pvalue MetaIDs Liver map00970 Aminoacyl-tRNA biosynthesis 0.006 L-Asparagine; L-Tyrosine; O-Phospho-L-serine; L-Threonine; L-Phenylalanine; L-Glutamic acid; Methionine map04978 Mineral absorption 0.023 L-Asparagine; L-Threonine; L-Phenylalanine; Methionine map04726 Serotonergic synapse 0.028 Prostaglandin E2; Thromboxane B2; Prostaglandin D2; Prostaglandin J2; Prostaglandin A2 map00290 Valine, leucine and isoleucine biosynthesis 0.035 3-Methyl-2-oxobutanoic acid; L-Threonine; 2-Isopropylmalic acid map00380 Tryptophan metabolism 0.044 Tryptamine; Kynurenic acid; Xanthurenic acid; L-Kynurenine; Quinolinic acid; Indole-3-acetic acid map00750 Vitamin B6 metabolism 0.045 Pyridoxamine; Pyridoxine; D-Erythrose 4-phosphate; 4-Pyridoxic acid Colon map04971 Gastric acid secretion 0.007 Histamine Table 2 KEGG Enrichment of characteristic metabolites derived from liver and colon between compared treatments TSS vs Con Tissue MapID MapTitle Pvalue MetaIDs Liver map00970 Aminoacyl-tRNA biosynthesis 0.006 L-Asparagine; L-Tyrosine; O-Phospho-L-serine; L-Threonine; L-Phenylalanine; L-Glutamic acid; Methionine map04978 Mineral absorption 0.023 L-Asparagine; L-Threonine; L-Phenylalanine; Methionine map04726 Serotonergic synapse 0.028 Prostaglandin E2; Thromboxane B2; Prostaglandin D2; Prostaglandin J2; Prostaglandin A2 map00290 Valine, leucine and isoleucine biosynthesis 0.035 3-Methyl-2-oxobutanoic acid; L-Threonine; 2-Isopropylmalic acid map00380 Tryptophan metabolism 0.044 Tryptamine; Kynurenic acid; Xanthurenic acid; L-Kynurenine; Quinolinic acid; Indole-3-acetic acid map00750 Vitamin B6 metabolism 0.045 Pyridoxamine; Pyridoxine; D-Erythrose 4-phosphate; 4-Pyridoxic acid Colon map04971 Gastric acid secretion 0.007 Histamine (1) MapID: map ID of enriched KEGG Pathway. (2) MapTitle༚title of enriched KEGG Pathway. (3) Over Representation Analysis was implemented using the hypergeometric test to evaluate whether a particular metabolite set is represented more than expected by chance within the given compound list. One-tailed p values are provided after adjusting for multiple testing. (4) MetaIDs༚list of those input metabolites that participated in this KEGG Pathway. (1) MapID: Map ID of the enriched KEGG pathways. (2) MapTitle: title of enriched KEGG pathways. (3) Overrepresentation analysis was implemented using a hypergeometric test to evaluate whether a particular metabolite set was represented more than expected by chance within the given compound list. One-tailed p values are provided after adjusting for multiple testing. (4) MetaIDs: List of those input metabolites that participated in this KEGG pathway. Moreover, microbial products that passed into the liver via the portal vein were significantly up- or downregulated associating with TSS treatment. Specifically, the relative abundances of salicylic acid, N-phenylacetylglutamine, N-acetyl-D-galactosamine, 2-isopropylmalic acid, pimelic acid and L-ergothioneine were significantly increased, while the relative abundances of Glu-Glu, glycoursodeoxycholic acid, glycolithocholic acid, theophylline, o-toluic acid, 5-aminopentanoate, pseudouridine and D-cysteine were significantly decreased (Table S2 ). Apart from microbial products, 38 metabolites changed with TSS treatment and were cometabolized by the microbiota and its host. Metabolites derived from Liver were significantly correlated with those from colon The effects of TSS on substance metabolism among liver and colon were characterized by a number of metabolites whom significantly changed in response to the TSS or antibiotics treatments (Fig. 6 a), especially for the comparable groups TSS vs Con, Van + TSS vs Van, and Van vs GSW treatments. Microbial fermentation of dietary items could produce a myriad of metabolites which might later be absorbed by enterocytes and reach the liver via the portal vein (Fig. 6 d). As a result, liver-derived metabolites are strongly correlated with colon-derived metabolites. Consistent with this view, we observed that colon-derived metabolites APPC, PMP, 4-MCD, TTPD, and 10-hydroxydecanoic acid were strongly associated with liver-derived metabolites D-proline, SDMA, spermidine, hexanoylcarnitine, proline, Ala-Leu, ADTF, OIELTD, Pro-Leu, and 1-methylxanthine (Fig. 6 b). Moreover, the correlation network was dramatically altered by TSS when combined with vancomycin treatment (Fig. 6 e). In particular, the liver-derived metabolite APPC, which was originally present in the colon metabolic fluxes, was positively correlated with creatine, β-nicotinamide mononucleotide, and phenylacetylglycine whereas was negatively correlated with N-oleoyl dopamine and sakuranetin. Additionally, the signaling pathways enriched by liver- and colon-derived biomarkers partially reflect the important impactions of TSS treatment on the microbiota and its host. TSS treatment had the most differential effects on metabolic pathways, paralleling with some effects on neuroactive ligand–receptor interactions and protein digestion and absorption (Fig. 6 c). Expectedly, these TSS-related pathways were further extended by group Van + TSS which combined TSS with vancomycin treatment, and these impactions were defined by phenylalanine metabolism, purine metabolism, pyrimidine metabolism, taurine and hypotaurine metabolism, vitamin B6 metabolism, etc. (Fig. 6 f). The impact of TSS treatment on microbial cross-talking with their host Among liver biomarkers that were obviously associated with TSS treatment, 5 metabolites were from the host, 15 were microbiota derived, 48 were cometabolized by both agents, and the others were specifically from the drug, food, environment or unknown sources (Fig. 7 a). When we searched for the most enriched metabolic pathways according to metabolites derived from liver, bacteria, or both through metabolite pathway enrichment analysis (MPEA), we found that some cometabolism pathways shared by the gut microbiota and its host were characterized by aminoacyl-tRNA biosynthesis and vitamin B6 metabolism (Fig. 7 b). Moreover, a cluster of bacteria, mainly including Coriobacteriaceae, Butyricicoccus pullicaecorum , Coprococcus , Mucispirillum schaedleri , and Anaerotruncus ; unidentified F16 ; Lactobacillus vaginalis ; Christensenella ; Desulfovibrio ; unidentified Flexispira ; and Ruminococcus had positive correlation with hepatic metabolites. and another cluster had negative correlation with these hepatic metabolites, which included bacteria Corynebacterium stationis , Lactobacillales , Clostridium celatum , Veillonellaceae, unidentified rc4-4 , Pasteurellaceae, Turicibacter , Pseudomonas , Peptostreptococcaceae, Enterobacteriaceae, Veillonella parvula , Barnesiella intestinihominis , Streptococcaceae, Prevotella , and Sutterella (Fig. 7 c). The liver-derived metabolites D-proline, SDMA, spermidine, hexanoylcarnitine, proline, Ala-Leu, ADTF, OIELTD, prolylleucine, and 1-methylxanthine were the top 10 metabolites that were significantly correlated with the top 5 colon-derived metabolites (Fig. 6 b). As mentioned above, the most enriched cometabolism pathway was aminoacyl-tRNA biosynthesis. Afterwards, all the microbiota that participated in liver metabolites production were drawn by the BIO-Sankey network. As shown, the phylum Euryarchaeota and Candidatus Bathyarchaeota were associated with the production of O -phospho- L -serine, demonstrating that they were significantly downregulated (Fig. 7 d). Furthermore, an STA-Sankey network was drawn to reveal the microbial taxonomy accounting for metabolic changes. As shown, the enteric microorganism had a critical role in aminoacyl-tRNA biosynthesis, which included upregulated genus Coprococcus downregulated genus Veillonellaceae , unidentified Peptostreptococcaceae , unidentified Enterobacteriaceae , and Corynebacterium (Fig. 7 e). There were fewer metabolites derived from the colon had significantly altered with TSS than from the liver (Fig. S2 a), and ethylbenzene degradation was the only metabolic pathway enriched in the microbiota (Fig. S2 b). Moreover, the colon-derived metabolites N-methylisoleucine, 5-hydroxytryptophan, PMP, acetophenone and 15-HETE were positively correlated with microbe Delftia , coprococcus , Morganella , Corynebacterium stationis , Christensenella , Desulfovibrio , Flexispira , Elusimicrobium , Coriobacteriaceae , Rikenellaceae , Ruminococcus , Lactobacillus vaginalis , and unidentified F16 , and were negatively correlated with Streptococcaceae , Prevotella , Sutterella , Lactobacillales , Enterobacteriaceae , and Peptostreptococcaceae . However, another group of metabolites, namely dihydrothymine, PNK, BEP, 10-hydroxydecanoic acid, 4-MCD and TTPD, had opposite correlations with the microbiota (Fig. S2 c). Furthermore, according to the Sankey network (Fig. S2 d, e), the abundance of significant downregulated microbes Streptococcaceae, Conchiformibius , Sutterella , and Enterobacteriaceae were negatively correlated with the abundance of acetophenone, which is involved in ethylbenzene degradation whereas the microbes Jeotgallcoccus and Ruminococcus , which were significantly upregulated, were positively correlated with acetophenone. When intestinal microbes were disrupted by vancomycin, the cometabolic pathways and correlation maps were predominantly altered (Fig. S3 a). Notably, the number of liver metabolites as well as enriched metabolic pathways (Fig. b) were obviously increased which were originally from both the microbiota and host-microbe cometabolism (Fig. S3 a). And, the most effective pathway was the cometabolism of purines. Moreover, compared with TSS treatment, vancomycin administration changed greater numbers of liver metabolites that related to enteric microorganisms (Fig. S3 c). Furthermore, we observed that the abundances of the genera Actinomyces , unidentified Comamonadaceae , and unidentified Xanthomonadaceae had significantly decreased and were positively correlated with the downregulated adenosine 5-monophosphate which was involved in purine cometabolism pathway (Fig. S3 d, e). Moreover, when considering vancomycin treatment, the number of colon metabolites originating from the microbiota, host, and cometabolism was largely increased (Fig. S4 a), as well as the number of significantly enriched metabolic pathways (Fig. S4 b). Moreover, the total number of metabolites associated with microflora abatement increased markedly (Fig. S4 c). Finally, analysis of the cometabolic pentose phosphate pathway demonstrated that the significantly increasing metabolite D-ribulose 5-phosphate was positively related to a significant increase in the abundance of the bacterial genus unidentified Burkholderiales and a decrease in the abundance of unidentified Sinobacteraceae (Fig. S4 d). Discussion Microgravity exposure induced lipid deposition, oxidative stress and inflammation in the liver Microgravity exposure syndrome rats established by tail suspension to the extent of hindlimb unloading can replicate the physiological effects that occur after long-term microgravity exposure; therefore, these rats have become one of the main animal models mimicking the physiological effects resulting from microgravity exposure during spaceflight[ 10 ]. In the present study, simulated microgravity exposure expanded the interstitial space of liver tissue and significantly increased the level of lipid accumulation, which was intuitively reflected by H&E and oil red O staining of pathological tissue sections (Fig. 2 a and b). Furthermore, lipid deposition was verified by increased concentrations of total cholesterol (TC) and triglycerides (TGs) in liver tissue homogenates. Although the serum AST and ALT levels, which are indicators of liver injury, were not significantly increased by TSS treatment, the levels of oxidative stress-related markers (MDA) and inflammatory factors (IL-6) in liver tissue homogenates were markedly increased, which might reflect the increase in oxidative stress and inflammation in the liver resulting from long-term microgravity exposure (Fig. 2 i-n). To explore the pivotal role of gut microbes in reversing the effects of microgravity exposure, an antibiotic usually used for gut microbial elimination, vancomycin, was selected as a positive control for comparison. The results indicated that a large imbalance in gut microbes could give rise to more severe lipid deposition and higher levels of oxidative stress as well as inflammation, indicating that enteric microorganisms markedly participate in the formation of liver lesions during long-term microgravity exposure. Furthermore, metabolic profiles from liver tissue homogenates also reflected the proinflammatory effects of long-term microgravity exposure. Notably, bioactive lipids, such as 13,14-dihydro-15-keto-tetranor Prostaglandin D2, Prostaglandin B1, Prostaglandin E2, 2,3-Dinor-11β-prostaglandin F2α, bicyclo prostaglandin E2, 15-deoxy-Δ12,14-prostaglandin A1,6-kketoprostaglandin F1α, prostaglandin J2, prostaglandin D2 and prostaglandin A2, were simultaneously raised in liver homogenates from TSS-treated rats compared with those from control rats. As documented by previous reports, these bioactive lipids have pronounced effects on ameliorating or aggravating oxidation, apoptosis and inflammation. These results indicated that liver lipid metabolic disturbances resulting from microgravity exposure via TSS treatment might enhance the formation of proinflammatory products such as PGE2 and trigger inflammation and its consequences. Taken together, these findings indicated that TSS treatment could promote the accumulation of lipids and trigger oxidative stress and inflammation in the liver, which are similar to previous spaceflight mice who has also been observed elevated fat accumulation in the liver[ 18 ] and induced liver injury and inflammation associated with apoptosis and oxidative stress[ 18 , 19 ]. Herein, complementary to the abovementioned mechanisms, we propose an array of metabolic disorders that involve mainly PGE2-based lipid metabolism and tryptophan metabolism and may account for liver inflammation and damage during microgravity exposure. Although the duration of 30-day tail suspension is too short for liver injury to develop, the increases in markers of oxidative stress and inflammation have raised the concern that longer microgravity exposure to the space environment may result in progressive liver damage. Simulated microgravity damaged the gut microflora and the intestinal epithelium Using a TSS rat model, we observed that microgravity exposure resulted in mild alterations in the gut microflora, manifested by an increase in opportunistic pathogens, such as Shigella and Desulfovibrio , and a decrease in Lactobacillus and Prevotella (Fig. 4 a). The intestinal mucosa is organized into the intestinal epithelium, lamina propria, muscle and so on[ 20 ]. Colon H&E staining revealed broken intestinal mucosa in certain niches from TSS-treated SD rats. Additionally, from these images, we observed irregular surface evenness, a sparse filamentous plexus, and narrow muscularis mucosa (Fig. 3 a). Coincidentally, damage to the defense of the intestinal barrier was further confirmed by increased absorption of LPS and D-Lac as well as inadequate response activities, for example, insufficient secretion of LBP, CALP and DAO (Fig. 3 b). In addition to microbiota changes and structural depletion, changes in functionality were inferred by alterations in liver mammalian-microbial cometabolite levels. For example, TSS treatment caused a significant decrease in the liver-derived metabolites glycolithocholic acid and glycoursodeoxycholic acid (Fig. 3 d), which are conjugated secondary bile acids with glycine, and agonists to the nuclear receptor farnesoid X receptor (FXR)-a bile acid receptor. These reductions have been proven to contribute to the low activity of FXR and impaired intestinal barrier function. Finally, liver metabolites related to tryptophan metabolism, for example, tryptamine, kynurenic acid, xanthurenic acid, L -kynurenine, and indole-3-acetic acid, were markedly increased (Table S2 ), among which L -kynurenine and indole-3-acetic acid are important for the intestinal immunological barrier via their roles in the regulation of immunity and inflammation. In addition, the concentration of 5-hydroxytryptophan in the colon coordinately increased (Table S3 ), which may self-adjust barrier homeostasis. Overall, microgravity exposure gave rise to gut microbiota shifts and colon barrier damage in a hindlimb unloading model. The microbial communities residing in the mammalian gut lumen play pivotal roles in regulating intestinal barrier function, and alterations in microbiome composition and function have been linked to impaired defense of the intestinal barrier at different levels[ 21 ]. The NASA twins’ study revealed that the gastrointestinal (GI) microbiota from inflight faces had no significant differences in terms of richness or the Shannon index relative to that of preflight and postflight samples, and the metabolite 3-indole propionic acid, which has beneficial effects on barrier integrity, was observed at lower levels in abording astronauts throughout the duration of the study[ 1 ]. In contrast, other intestinal microflora in inflight faces from astronauts aboard Salyut/Soyuz and Mir were shown to be infected by increasing opportunistic pathogens[ 2 ]. Compensatory for these results, we demonstrated that the microbiota and biomarkers related to intestinal barrier damage were regulated by abnormalities in TSS-treated rats (Fig. 3 and Fig. 4 ), especially for intestinal permeability, as an expression of gut barrier disruption was established by increased absorption of LPS and D -Lac. In summary, these results give rise to prevent the disruption of intestinal barrier function during future space travel. Microbiota abnormalities resulting from microgravity exposure likely account for four physiological pathways. First, microgravity exposure changes the cellular activity of genes in molecular pathways, which may impact the production of secondary metabolites and influence microbial constitution[ 22 ]. Second, microgravity exposure alters liver activity, which in particular induces changes in bile acid metabolism and synthesis[ 23 ], shaping the intestinal microbial phenotype. Third, microgravity exposure can change the differentiation and proliferation of intestinal stem cells[ 24 , 25 ], thus impairing the integrity of the intestinal mucosa. Finally, microgravity exposure causes changes in the immune response, thereby challenging the growth of pathogens residing in the intestinal mucosa[ 26 ]. Interestingly, we observed changes in bile acid metabolism and intestinal barrier injury in rats after 30 days of microgravity exposure, which might explain the mechanism of intestinal flora disturbance during long-term spaceflight. Liver insufficiency is associated with gut-liver axis disruption upon microgravity Current data demonstrate that microbiota abnormalities can impair the intestinal barrier, facilitating the portal influx of microbe-derived metabolites, such as trimethylamine, LPS and secondary bile acids, ultimately worsening inflammation and metabolic abnormalities. Moreover, the host shapes the gut microbiome through the hepatoenteric circulation of bile acids and the regulation of antibody secretion, maintaining gut barrier homeostasis via FXR signaling in the intestinal epithelium[ 27 ]. Thus, the microbiota and intestinal mucosa are interrelated, and both are connected to the host through bile and portal blood. As displayed by correlation networks between metabolites derived from the liver and colon (Fig. 6 b and e) as well as associating maps among liver metabolites and fecal microbiota (Fig. 7 c), liver metabolites that changed with TSS treatment were strongly correlated with colon metabolites as well as the residing microbiome. The correlation maps were further interpreted by cometabolic pathway analysis (Fig. 7 d and e), which revealed that gut microbes play important roles in the synthesis and transformation of amino acids in the liver and in vitamin metabolism. Furthermore, those effects of the gut microbiota on metabolic flux from gut-liver axis were further verified by depleting the gut microbes via vancomycin administration (Fig. S1 and Fig. S4 ). Moreover, liver metabolites from the portal influx of microbial products significantly changed their relative abundance (Table S2 ), especially for increased N-phenylacetylglutamine and decreased secondary bile acids (glycoursodeoxycholic acid and glycolithocholic acid). Among these microbially derived metabolites, N-phenylacetylglutamine, which impairs the firing rate and induces axonal damage in cultured neurons[ 28 ], is highly expressed in the sepsis group[ 29 ] and is elevated in concentrations associated with phenylketonuria[ 30 ]. In addition, conjugated secondary bile acids (BAs), glycoursodeoxycholic acid (glyco-UDCA) and glycolithocholic acid (glyo-LCA) are reduced along with the microbial genera Lactobacillus (Firmicutes) and Prevotella (Bacteroidetes), in which deconjugated UDCA is oxidized and epimerized of bile acids by Actinobacteria, Proteobacteria, Firmicutes and Bacteroidetes[ 31 ], and LCA is involved in dihydroxylation of bile acids by Firmicutes (Clostridium and Extibacter spp.)[ 32 ]. Both UDCA and LCA can bind to both FXR and G protein-coupled receptors (TGR5)[ 33 ] which exhibit many beneficial effects, including accelerating bile acid enterohepatic circulation[ 34 ], treating cholestatic liver diseases[ 35 , 36 ], exerting cytoprotective and anti-inflammatory actions, and preventing colorectal adenoma recurrence 28 . Specifically, treatment with UDCA can dampen mucosal inflammatory responses, prevent epithelial apoptosis, promote restitution, and restore a more “normal” microbial composition[ 37 , 38 ]. Thus, the alterations in the metabolites mentioned above suggested that microgravity exposure may affect the production of microbial secondary metabolites which further cause insufficient liver activity. In actual spaceflight and animal models, the liver was regarded as an early sensor and frequent target of microgravity exposure and gradually developed into disturbances of hepatic homeostasis which resulted in liver injury and inflammation. Otherwise, this liver inflammation was accompanied by cell apoptosis and oxidative stress, compromised carbohydrate metabolism, accumulated lipid droplets in the liver which ultimately alter hepatic biotransformation capacity[ 18 ]. Furthermore, proteomics analysis of liver tissue from mice aboard a biosatellite for 30 days revealed regeneration of bile acid secretion as well as reconstitution of transporter proteins and CYP enzymes[ 23 ]. In this study, we proposed another mechanism accounting for liver insufficiency, which is well defined by gut-liver axis disruption, further compensating for the mechanism of liver damage during space travel. Overall, microgravity exposure simulated in a hindlimb animal model can alter the portal influx of microbial secondary metabolites in two ways. On the one hand, microgravity elevated the influx of harmful metabolites. On the other hand, it reduced the population of secondary BAs, which might account for microbiota abnormities, increasing intestinal permeability, and subsequently give rise to liver insufficiency in metabolism. Cometabolism pathways of the microbiota with its host contribute to hepatic synthesis and metabolism of amino acids upon microgravity The liver is a highly active metabolic organ that plays vital roles in the biosynthesis and metabolism of carbohydrates, lipids, proteins, vitamins and so on. Inherent to its unique location and function in the body, the liver is generally an early sensor and frequent target of stress from microgravity exposure[ 18 ]. To explore the roles of the microbiota in host metabolism, we constructed Sankey networks among liver metabolites and fecal microbes. The pathway enrichment results (Fig. 7 ) revealed that the metabolic pathways associated with TSS treatment were: aminoacyl-tRNA biosynthesis; vitamin B6 metabolism; alanine, aspartate, and glutamate metabolism; glutathione metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; D-glutamine and D-glutamate metabolism; glycine, serine, and threonine metabolism; biosynthesis of siderophore group nonribosomal peptides; tryptophan metabolism; taurine and hypotaurine metabolism; glyoxylate and dicarboxylate metabolism; citrate cycle (TCA cycle); cysteine and methionine metabolism; arginine biosynthesis; valine, leucine, isoleucine biosynthesis; arginine and proline metabolism; histidine metabolism; and lysine biosynthesis. Moreover, the fecal microbes exhibited obvious correlations with liver metabolites. In view, the enriched pathways indicated the impact of microgravity exposure on hepatic synthesis and metabolism of amino acids. Interestingly, several specific metabolic pathways, including tryptophan metabolism, valine, leucine, isoleucine metabolism, glycine, serine, threonine metabolism, glutathione metabolism, glycerophospholipid metabolism, and tricarboxylic acid (citric acid) cycle metabolism, are dysregulated in liver fibrosis[ 39 ]. In the largest cohort of astronauts, spatial data flown revealed that hepatic fluxes related to these fibrosis-related pathways were significantly increased[ 40 ]. Therefore, it is reasonable to speculate that dysregulation in microbial cometabolism of amino acids with its host aggravated the liver lipid deposition as well as upregulated fibrosis-related pathway. Additionally, we also discovered abnormities in amino acid biosynthesis and vitamin B6 metabolism. Vitamin B6 is a coenzyme involved in the metabolism of amino acids and amino acid biosynthesis, and vitamin B6 metabolism might contribute to protein loss in astronauts[ 41 ] and immune system weakening[ 42 ]. Hence, microbial homeostasis is critical for maintaining hepatic synthesis and metabolism of amino acids upon microgravity. Conclusions The liver and gut, as major metabolic hubs of the human body, play vital roles in health maintenance and performance premotion for astronauts, but known little because of enormous limitations in researches due to the unavailability of organic samples and ethical reasons. In this work, we highlighted gut-liver axis disturbances upon microgravity exposure via a well-established hindlimb unloading rat model. Overall, microgravity exposure can induce lipid deposition, oxidative stress and inflammation in the liver and increase the proportion of opportunistic pathogens, following by intestinal barrier function damage. Consequently, these abnormalities result in marked dysregulation of enterohepatic metabolic communication associated with liver insufficiencies. These results provide insight into the health effects of gut-liver axis hemostasis on long-term human-crewed space missions and raise new targets to maintain and improve health as well as performance for astronaut inflight. Abbreviations H&E hematoxylin-eosin staining ELISA Enzyme-linked immunosorbent assay TC Total cholesterol TG triglyceride ALT alanine transaminase AST aspartate aminotransferase MDA malondialdehyde GSH-Px glutathione peroxidase SOD superoxide dismutase LPS lipopolysaccharide LBP lipopolysaccharide binding protein D-Lac D-lactate DAO diamine oxidase SIGA secretory immunoglobulin A CALP calprotectin CGA chromograninA OTUs operational taxonomic units O 2 PLS-DA orthogonal signal correction-orthogonal partial least squares-discriminant analysis VIPs predictive variable importance in the projection from O 2 PLS-DA FC fold change MPEA metabolite pathway enrichment analysis DPGA1 15-deoxy-Δ 12,14-prostaglandin A1 TBDCA trans-2-butene-1,4-dicarboxylic Acid TCA thiazolidine-4-carboxylic acid HDHC 7-hydroxy-3,4-dihydrocarbostyril DPCBA N1-(1,3-diphenyl-1H-pyrazol-5-yl)-2-chlorobenzamide HM (+/-)-CP 47,497-C7-hydroxy metabolite HNPP 3-hydroxy-2-(3-nitro-4-piperidinobenzyl)propanenitrile AMBA 2-(acetylamino)-4-(methylthio)butanoic acid TYMP 2-[(1H-1,2,4-triazol-3-ylimino)methyl]phenol BAMC tert-butyl N-[1-(aminocarbonyl)-3-methylbutyl]carbamate HMMMNA N-[(4-hydroxy-3-methoxyphenyl)methyl]-8-methylnonanamide FIPM 2-furyl[4-(1H-indol-4-yl)piperazino]methanone 5-HT b5-Hydroxytryptophan PMP 1-Phenyl-3-methyl-5-pyrazolone APPC 5-amino-1-phenyl-1H-pyrazole-4-carbonitrile MOPD 3-(2-methylpropyl)-octahydropyrrolo[1,2-a]pyrazine-1,4-dione TTPD 1,3,7-trimethyl-2,3,6,7-tetrahydro-1H-purine-2,6-dione BEP 2-(1,3-benzodioxol-5-yl)-7-ethylimidazo[1,2-a]pyridine. APPC:5-amino-1-phenyl-1H-pyrazole-4-carbonitrile PMP, 1-phenyl-3-methyl-5-pyrazolone 4-MCD 4-methoxycinnamaldehyde TTPD 1,3,7-trimethyl-2,3,6,7-tetrahydro-1H-purine-2,6-dione OIELTD 4-[2-(2-oxo-1-imidazolidinyl)ethyl]-1lambda ~ 6,4-thiazinane-1,1-dione ADTF 3-acetyl-2,5-dimethylfuran SDMA N3,N4-dimethyl-L-arginine DPGA1 15-deoxy-Δ 12,14-prostaglandin A1 COEA 4-(4-cyclohexylphenyl)-4-oxobut-2-enoic acid DPA (2R)-2,3-dihydroxypropanoic acid SDMA N3,N4-Dimethyl-L-arginine FIPHA, 2-furyl[4-(1H-indol-4-yl)piperazino]methanone OIELTD 4-[2-(2-oxo-1-imidazolidinyl)ethyl]-1lambda ~ 6~,4-thiazinane-1,1-dione Pro-Hyp Proline-hydroxyproline TBAMBC tert-Butyl N-[1-(aminocarbonyl)-3-methylbutyl]carbamate GluAA (5-L-Glutamyl)-L-Amino Acid APPCD 3-amino-2-phenyl-2H-pyrazolo[4,3-c]pyridine-4,6-diol 4-MCD 4-Methoxycinnamaldehyde PAG N-Phenylacetylglutamine DPGA1 15-Deoxy-Δ 12,14-prostaglandin A1 ADTF 3-Acetyl-2,5-dimethylfuran TU Testosterone undecanoate AAPAA 2-(acetylamino)-3-[4-(acetylamino)phenyl]acrylic acid DKTPGD2 13,14-dihydro-15-keto-tetranor Prostaglandin D2 APPD 3-amino-1H-pyrazolo[4,3-c]pyridine-4,6-diol. Declarations Ethics approval and consent to participate All the procedures involved in our animal studies were performed in accordance with the Guidelines in the Care and Use of Animals and approved by the China Astronaut Research and Training Center Animal Welfare Committee (Permit Number: ACC-LACUC-2021-014). Consent for publication Not applicable Availability of data and materials Sequence files for all samples used in this study have been deposited in the Genome Sequence Archive (GSA) (https://ngdc.cncb.ac.cn/gsub/submit/gsa/list) with serial number subCRA025472. And raw MZ data for all samples used in this study have been deposited in the MetaboLights (https://www.ebi.ac.uk/metabolights/editor/console) with unique identifier MTBLS9924. The metadata, microbial sequencing data, and ELISA data have been included in Additional files 3, 4 and 5, respectively. Competing interests No potential conflicts of interest were reported by the authors. Authors’ contributions Pu Chen implemented the experiment, created the Graphical Abstract, analyzed and interpreted the data and wrote and edited the manuscript. Junli Chen participated in the experiment and collected the samples. Nan Xu conducted the H&E staining, generated the figures, and edited the manuscript. Weiran Wang created the correlation network, generated the figures, and edited the manuscript. Lingwei Hou analyzed the cometabolomic pathways, generated the figures, and edited the manuscript. Bowen Sun analyzed the metabolomic flux, generated the figures, and edited the manuscript. Haiyun Lan analyzed and interpreted the microbiome data and edited the manuscript. Wei Liu conducted the experiments and edited the manuscript. Qibing Shen analyzed the metabolic data, interpreted the data, and generated the figures. Yanbo Yu participated in the exchange of ideas, created the figures, and edited the manuscript. Peng Zang supervised the project and paper, analyzed and interpreted the data and summary results, and wrote and edited the manuscript. Acknowledgments This research was funded by the State Key Lab of Space Medicine Fundamentals and Application, Astronaut Center of China (SMFA19B04) and Science and Technology Planning Project of Shenzen Municipality (JCYJ20180507182854651). References Garrett-Bakelman FE, Darshi M, Green SJ, Gur RC, Lin L, Macias BR, McKenna MJ, Meydan C, Mishra T, Nasrini J et al. The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight. Science 2019, 364(6436). Ilyin VK. Microbiological status of cosmonauts during orbital spaceflights on Salyut and Mir orbital stations. Acta Astronaut. 2005;56(9–12):839–50. Jonscher KR, Alfonso-Garcia A, Suhalim JL, Orlicky DJ, Potma EO, Ferguson VL, Bouxsein ML, Bateman TA, Stodieck LS, Levi M, et al. Spaceflight Activates Lipotoxic Pathways in Mouse Liver. PLoS ONE. 2016;11(4):e0152877. Tesei D, Jewczynko A, Lynch AM, Urbaniak C. Understanding the Complexities and Changes of the Astronaut Microbiome for Successful Long-Duration Space Missions. Life (Basel) 2022, 12(4). Bedree JK, Kerns K, Chen T, Lima BP, Liu G, Ha P, Shi J, Pan HC, Kim JK, Tran L et al. Specific host metabolite and gut microbiome alterations are associated with bone loss during spaceflight. Cell Rep 2023:112299. Huang B, Li D-G, Huang Y, Liu C-T. Effects of spaceflight and simulated microgravity on microbial growth and secondary metabolism. Military Med Res. 2018;5(1):18. Albillos A, de Gottardi A, Rescigno M. The gut-liver axis in liver disease: Pathophysiological basis for therapy. J Hepatol. 2020;72(3):558–77. Milosevic I, Vujovic A, Barac A, Djelic M, Korac M, Radovanovic Spurnic A, Gmizic I, Stevanovic O, Djordjevic V, Lekic N et al. Gut-Liver Axis, Gut Microbiota, and Its Modulation in the Management of Liver Diseases: A Review of the Literature. Int J Mol Sci 2019, 20(2). Collins SL, Stine JG, Bisanz JE, Okafor CD, Patterson AD. Bile acids and the gut microbiota: metabolic interactions and impacts on disease. Nat Rev Microbiol. 2023;21(4):236–47. Morey-Holton E, Globus RK, Kaplansky A, Durnova G. The hindlimb unloading rat model: literature overview, technique update and comparison with space flight data. Adv Space Biol Med. 2005;10:7–40. Feldman AT, Wolfe D. Tissue processing and hematoxylin and eosin staining. Methods Mol Biol. 2014;1180:31–43. Mehlem A, Hagberg CE, Muhl L, Eriksson U, Falkevall A. Imaging of neutral lipids by oil red O for analyzing the metabolic status in health and disease. Nat Protoc. 2013;8(6):1149–54. Wang B, Jiang X, Cao M, Ge J, Bao Q, Tang L, Chen Y, Li L. Altered Fecal Microbiota Correlates with Liver Biochemistry in Nonobese Patients with Non-alcoholic Fatty Liver Disease. Sci Rep. 2016;6:32002. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6. Want EJ, Masson P, Michopoulos F, Wilson ID, Theodoridis G, Plumb RS, Shockcor J, Loftus N, Holmes E, Nicholson JK. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc. 2012;8(1):17–32. Yuan M, Breitkopf SB, Yang X, Asara JM. A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat Protoc. 2012;7(5):872–81. Wen B, Mei Z, Zeng C, Liu S. metaX: a flexible and comprehensive software for processing metabolomics data. BMC Bioinformatics. 2017;18(1):183. Vinken M. Hepatology in space: Effects of spaceflight and simulated microgravity on the liver. Liver Int. 2022;42(12):2599–606. da Silveira WA, Fazelinia H, Rosenthal SB, Laiakis EC, Kim MS, Meydan C, Kidane Y, Rathi KS, Smith SM, Stear B, et al. Comprehensive Multi-omics Analysis Reveals Mitochondrial Stress as a Central Biological Hub for Spaceflight Impact. Cell. 2020;183(5):1185–e12011120. Gehart H, Clevers H. Tales from the crypt: new insights into intestinal stem cells. Nat Reviews Gastroenterol Hepatol. 2019;16(1):19–34. Leibovitzh H, Lee S-H, Xue M, Garay JAR, Hernandez-Rocha C, Madsen KL, Meddings JB, Guttman DS, Espin-Garcia O, Smith MI. Altered gut microbiome composition and function are associated with gut barrier dysfunction in healthy relatives of patients with Crohn’s disease. Gastroenterology. 2022;163(5):1364–76. e1310. Sharma G, Curtis PD. The impacts of microgravity on bacterial metabolism. Life. 2022;12(6):774. Anselm V, Novikova S, Zgoda V. Re-adaption on earth after spaceflights affects the mouse liver proteome. Int J Mol Sci. 2017;18(8):1763. Topal U, Zamur C. Microgravity, stem cells, and cancer: A new Hope for cancer treatment. Stem Cells International 2021, 2021. Grimm D, Wehland M, Corydon TJ, Richter P, Prasad B, Bauer J, Egli M, Kopp S, Lebert M, Krüger M. The effects of microgravity on differentiation and cell growth in stem cells and cancer stem cells. Stem cells translational Med. 2020;9(8):882–94. Siddiqui R, Akbar N, Khan NA. Gut microbiome and human health under the space environment. J Appl Microbiol. 2021;130(1):14–24. Pabst O, Hornef MW, Schaap FG, Cerovic V, Clavel T, Bruns T. Gut–liver axis: barriers and functional circuits. Nat Reviews Gastroenterol Hepatol 2023:1–15. Ntranos A, Park H-J, Wentling M, Tolstikov V, Amatruda M, Inbar B, Kim-Schulze S, Frazier C, Button J, Kiebish MA. Bacterial neurotoxic metabolites in multiple sclerosis cerebrospinal fluid and plasma. Brain. 2022;145(2):569–83. Li Y, Wang C, Chen M. Metabolomics-based study of potential biomarkers of sepsis. Sci Rep. 2023;13(1):585. Wild J, Shanmuganathan M, Hayashi M, Potter M, Britz-McKibbin P. Metabolomics for improved treatment monitoring of phenylketonuria: urinary biomarkers for non-invasive assessment of dietary adherence and nutritional deficiencies. Analyst. 2019;144(22):6595–608. Funabashi M, Grove TL, Wang M, Varma Y, McFadden ME, Brown LC, Guo C, Higginbottom S, Almo SC, Fischbach MA. A metabolic pathway for bile acid dehydroxylation by the gut microbiome. Nature. 2020;582(7813):566–70. Guzior DV, Quinn RA. Microbial transformations of human bile acids. Microbiome. 2021;9(1):1–13. Kiriyama Y, Nochi H. Physiological role of bile acids modified by the gut microbiome. Microorganisms. 2022;10(1):68. Zhang Y, Jiang R, Zheng X, Lei S, Huang F, Xie G, Kwee S, Yu H, Farrar C, Sun B. Ursodeoxycholic acid accelerates bile acid enterohepatic circulation. Br J Pharmacol. 2019;176(16):2848–63. Molinaro A, Marschall H-U. Bile acid metabolism and FXR-mediated effects in human cholestatic liver disorders. Biochem Soc Trans. 2022;50(1):361–73. Hasegawa S, Yoneda M, Kurita Y, Nogami A, Honda Y, Hosono K, Nakajima A. Cholestatic liver disease: current treatment strategies and new therapeutic agents. Drugs. 2021;81(10):1181–92. Ward JB, Lajczak NK, Kelly OB, O’Dwyer AM, Giddam AK, Ní Gabhann J, Franco P, Tambuwala MM, Jefferies CA, Keely S. Ursodeoxycholic acid and lithocholic acid exert anti-inflammatory actions in the colon. Am J Physiology-Gastrointestinal Liver Physiol. 2017;312(6):G550–8. Lajczak-McGinley NK, Porru E, Fallon CM, Smyth J, Curley C, McCarron PA, Tambuwala MM, Roda A, Keely SJ. The secondary bile acids, ursodeoxycholic acid and lithocholic acid, protect against intestinal inflammation by inhibition of epithelial apoptosis. Physiological Rep. 2020;8(12):e14456. Chang M-L, Yang S-S. Metabolic signature of hepatic fibrosis: from individual pathways to systems biology. Cells. 2019;8(11):1423. Afshinnekoo E, Scott RT, MacKay MJ, Pariset E, Cekanaviciute E, Barker R, Gilroy S, Hassane D, Smith SM, Zwart SR. Fundamental biological features of spaceflight: advancing the field to enable deep-space exploration. Cell. 2020;183(5):1162–84. Heer M, Titze J, Smith SM, Baecker N. Nutrition physiology and metabolism in spaceflight and analog studies. In.: Springer; 2015. Shi L, Tian H, Wang P, Li L, Zhang Z, Zhang J, Zhao Y. Spaceflight and simulated microgravity suppresses macrophage development via altered RAS/ERK/NFκB and metabolic pathways. Cell Mol Immunol. 2021;18(6):1489–502. Supplementary. tables. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Additional file 1: Supplementary tables mentioned in this manuscript. TableS2.xlsx Additional file 1: Supplementary tables mentioned in this manuscript. TableS3.xlsx Additional file 1: Supplementary tables mentioned in this manuscript. FigureS1.png Additional file 2: Supplementary figures mentioned in this manuscript. FigureS2.png Additional file 2: Supplementary figures mentioned in this manuscript. FigureS3.png Additional file 2: Supplementary figures mentioned in this manuscript. FigureS4.png Additional file 2: Supplementary figures mentioned in this manuscript. Additionalfile316srRNASequencing.xls Additional file 3: Taxonomic assignments and richness for all operational taxonomic units used in this study. Additionalfile4Metabolicprofilingofliverandcolon.xlsx Additional file 4: Metadata associated with all samples used in this study. Additionalfile5ELISAanalysis.xlsx Additional file 5: ELISA data used in this study. Cite Share Download PDF Status: Published Journal Publication published 11 Feb, 2026 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 15 Jul, 2025 Reviews received at journal 15 Jul, 2025 Reviews received at journal 01 Jul, 2025 Reviewers agreed at journal 30 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviews received at journal 24 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers agreed at journal 26 Sep, 2024 Reviewers invited by journal 24 Sep, 2024 Editor invited by journal 22 May, 2024 Editor assigned by journal 22 May, 2024 Submission checks completed at journal 13 May, 2024 First submitted to journal 29 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4190281","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301761986,"identity":"4208ddde-1658-4238-9fa9-4b03b6dd168c","order_by":0,"name":"Pu Chen","email":"","orcid":"","institution":"China Astronaut Research and Training Center","correspondingAuthor":false,"prefix":"","firstName":"Pu","middleName":"","lastName":"Chen","suffix":""},{"id":301761987,"identity":"85047747-fe95-4493-a834-35c7353e60f5","order_by":1,"name":"Junli Chen","email":"","orcid":"","institution":"China Astronaut Research and Training Center","correspondingAuthor":false,"prefix":"","firstName":"Junli","middleName":"","lastName":"Chen","suffix":""},{"id":301761988,"identity":"836350a3-4be7-4b08-b037-4652e35eb06e","order_by":2,"name":"Nan Xu","email":"","orcid":"","institution":"China Astronaut Research and Training Center","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Xu","suffix":""},{"id":301761989,"identity":"ee45a48c-c69d-46c9-be70-542ad786b611","order_by":3,"name":"Weiran Wang","email":"","orcid":"","institution":"China Astronaut Research and Training Center","correspondingAuthor":false,"prefix":"","firstName":"Weiran","middleName":"","lastName":"Wang","suffix":""},{"id":301761990,"identity":"497283a0-a655-415e-84ac-798db1157134","order_by":4,"name":"Lingwei Hou","email":"","orcid":"","institution":"China Astronaut Research and Training Center","correspondingAuthor":false,"prefix":"","firstName":"Lingwei","middleName":"","lastName":"Hou","suffix":""},{"id":301761991,"identity":"0122c08f-f872-4723-a311-2af028ad7f6e","order_by":5,"name":"Bowen Sun","email":"","orcid":"","institution":"China Astronaut Research and Training Center","correspondingAuthor":false,"prefix":"","firstName":"Bowen","middleName":"","lastName":"Sun","suffix":""},{"id":301761992,"identity":"e78055a0-cf85-44e4-ba78-c6ad4c42dc75","order_by":6,"name":"Haiyun Lan","email":"","orcid":"","institution":"China Astronaut Research and Training Center","correspondingAuthor":false,"prefix":"","firstName":"Haiyun","middleName":"","lastName":"Lan","suffix":""},{"id":301761993,"identity":"1452174e-6241-406e-a945-1f1bfa507c78","order_by":7,"name":"Wei Liu","email":"","orcid":"","institution":"China Astronaut Research and Training Center","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Liu","suffix":""},{"id":301761994,"identity":"b7ee5f1e-43ae-408f-889f-528f80c6a505","order_by":8,"name":"Qibing Shen","email":"","orcid":"","institution":"Space Science and Technology Institute (Shenzhen)","correspondingAuthor":false,"prefix":"","firstName":"Qibing","middleName":"","lastName":"Shen","suffix":""},{"id":301761995,"identity":"b15ebe2b-7a61-4290-8a75-4776f6697b42","order_by":9,"name":"Yanbo Yu","email":"","orcid":"","institution":"Space Science and Technology Institute (Shenzhen)","correspondingAuthor":false,"prefix":"","firstName":"Yanbo","middleName":"","lastName":"Yu","suffix":""},{"id":301761996,"identity":"f476eefd-dfb9-447b-9f6a-50f4b29e82ca","order_by":10,"name":"Peng Zang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIie3ROwrCQBCA4V0WtppoO4IQjzASiE1uYrNVKhXExkJQCKyNkFriIXKEwICV4hVyhJR2PkpR2C0t9q/nK2ZGiFDoL1PNuKNsWyrFrSfRZoXrXB73OidPAqMOLizrG4zQC8TVQdYDq1TCIEhssqmTyNOVaWm1TjlqWnHO5zsXUbjIaWABUu4Zkjt2E42zyT2yiEkBhF4EcEYEFyJSvgSHZ5Pg2hjk15GNzy5xVbxf+TD9krntNpmbiI9fGOf4FwmFQqHQr56E6TvnrSxF8wAAAABJRU5ErkJggg==","orcid":"","institution":"China Astronaut Research and Training Center","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zang","suffix":""}],"badges":[],"createdAt":"2024-03-30 02:44:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4190281/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4190281/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12866-025-04616-x","type":"published","date":"2026-02-11T15:59:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56925194,"identity":"343a9b4f-ad77-4561-bc72-bf7a7a9794b8","added_by":"auto","created_at":"2024-05-22 08:33:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":397523,"visible":true,"origin":"","legend":"\u003cp\u003eGrowth curves of the various treatment groups.\u003cstrong\u003e \u003c/strong\u003eCon: control group; GSW: vehicle group; Van: rats were given antibiotic water containing vancomycin hydrochloride at a dose of 0.05 g/kg/d via intragastric administration; TSS: rats were treated with tail suspension to the extent of hindlimb unloading; Van+TSS: rats were treated with tail suspension along with vancomycin treatment at a dose of 0.05 g/kg/d via intragastric administration; GSW+TSS: rats were treated with tail suspension combined with sterile water via intragastric administration. 0.01\u0026lt;*\u003cem\u003ep\u003c/em\u003e \u0026lt;0.05; **\u003cem\u003ep\u003c/em\u003e \u0026lt;0.01.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/76895c731270f4110fdb603b.png"},{"id":56925209,"identity":"ccb59bc2-fe7b-4855-909a-8af752c4fac6","added_by":"auto","created_at":"2024-05-22 08:33:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70013570,"visible":true,"origin":"","legend":"\u003cp\u003eTSS treatment aggravated lipid deposition and inflammation in the liver. \u003cstrong\u003ea)\u003c/strong\u003e Hematoxylin-eosin (H\u0026amp;E) staining of liver tissue sections from rats subjected to the six treatments. b) Oil red O staining of liver tissue sections from rats subjected to the six treatments. \u003cstrong\u003ec) and d)\u003c/strong\u003e Total cholesterol (TC) and triglyceride (TG) levels in liver tissue homogenates from rats treated with TSS, vancomycin or their combination. \u003cstrong\u003ee) and f)\u003c/strong\u003e Serum alanine transaminase (ALT) and aspartate aminotransferase (AST) activity. \u003cstrong\u003eg) to i)\u003c/strong\u003e show the levels of oxidative-related factors, including malondialdehyde (MDA), glutathione peroxidase (GSH-Px), and superoxide dismutase (SOD), in liver homogenates. \u003cstrong\u003ej) to l) \u003c/strong\u003eConcentrations of proinflammatory cytokines (TNF-α, IL-6, and IL-1β).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/78028e435941aa3f0faab54c.png"},{"id":56925198,"identity":"fb921a50-5a0f-4763-9e14-ca7dfb791d80","added_by":"auto","created_at":"2024-05-22 08:33:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":12069079,"visible":true,"origin":"","legend":"\u003cp\u003eIntestinal barrier function. \u003cstrong\u003ea)\u003c/strong\u003e H\u0026amp;E staining of tissues from each treatment group. Representative H\u0026amp;E images of the colon are shown in the upperpanel at 500 μm, and amplified images areshown in the lower panel at 50 μm (except for theCon group and the Van+TSS group, which were 20 μm in length for appropriate viewing). The black arrows indicate the thickness of the intestinal epithelium. Red arrows indicate the thickness of the muscularis mucosa. The blue circles indicate damaged intestinal epithelium. \u003cstrong\u003eb)\u003c/strong\u003e Biomarkers for the permeability of intestinal barrier function. Abbreviations: LPS, lipopolysaccharide; LBP, lipopolysaccharide binding protein; D-Lac, D-lactate; DAO, diamine oxidase; SIGA, secretory immunoglobulin A; CALP, calprotectin; CGA, chromogranin A.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/9282c25040fca7d53b1224c9.png"},{"id":56925589,"identity":"d092b701-2bda-4119-97ed-da52c8f7bb65","added_by":"auto","created_at":"2024-05-22 08:41:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1414724,"visible":true,"origin":"","legend":"\u003cp\u003eGut microbiota abnormalities in the TSS group.\u003cstrong\u003e a)\u003c/strong\u003e Fecal microbiota constitution of the taxonomic genus in the 6 treatment groups. \u003cstrong\u003eb)\u003c/strong\u003e Evolution of the taxonomic tree in packed circles with respect to 6 treatments; the front size decreased at thephylum to genus level. \u003cstrong\u003ec)\u003c/strong\u003e The Chao1 index curve revealed the microbial α diversity among the various groups. \u003cstrong\u003ed)\u003c/strong\u003e β diversity was determined by PCoA, and the distance was measured by weighted UniFrac distances. e) Unique or common significant OTUs among the 6 groups are shown in the Venn diagram. f) Biomarkers of microbiota screened via linear discriminant analysis (LDA) effect size (LEfSe) analysis.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/bc2b3e3588c2901ccd7c713a.png"},{"id":56925593,"identity":"fdcc9a85-412d-495c-9107-36d02cc9259a","added_by":"auto","created_at":"2024-05-22 08:41:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1502859,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic changes in the gut-liver axes\u003cstrong\u003e a) \u003c/strong\u003eand\u003cstrong\u003e e) \u003c/strong\u003eO\u003csub\u003e2\u003c/sub\u003ePLS-DA score projections of metabolite intensities from the liver and colon. \u003cstrong\u003eb) \u003c/strong\u003eand\u003cstrong\u003e f) \u003c/strong\u003eHeatmaps of clusters and relative abundances of normalized metabolites from the liver and colon, respectively. Distances were calculated by the Euclidean distance matrix. \u003cstrong\u003ec) \u003c/strong\u003eand\u003cstrong\u003e g) \u003c/strong\u003eare unique and common biomarkers of identified metabolites produced by the liver and colon, respectively. The numbers of biomarkers are presented in a Venn diagram, and biomarkers were screened by a VIP \u0026gt; 1.0, FC \u0026gt; 1.2 or FC \u0026lt; 0.833, and P value \u0026lt; 0.05. \u003cstrong\u003ed) \u003c/strong\u003eand\u003cstrong\u003e h) \u003c/strong\u003eare the top 20 upregulated and downregulated characteristic metabolites from the liver and colon, respectively, as determined by matchstick analysis. Abbreviation: DPGA1, 15-deoxy-Δ 12,14-prostaglandin A1; TBDCA, trans-2-butene-1,4-dicarboxylic Acid; TCA, thiazolidine-4-carboxylic acid; HDHC, 7-hydroxy-3,4-dihydrocarbostyril; DPCBA, N1-(1,3-diphenyl-1H-pyrazol-5-yl)-2-chlorobenzamide; HM, (+/-)-CP 47,497-C7-hydroxy metabolite; HNPP, 3-hydroxy-2-(3-nitro-4-piperidinobenzyl)propanenitrile; AMBA, 2-(acetylamino)-4-(methylthio)butanoic acid; TYMP, 2-[(1H-1,2,4-triazol-3-ylimino)methyl]phenol; BAMC, tert-butyl N-[1-(aminocarbonyl)-3-methylbutyl]carbamate; HMMMNA, N-[(4-hydroxy-3-methoxyphenyl)methyl]-8-methylnonanamide; FIPM, 2-furyl[4-(1H-indol-4-yl)piperazino]methanone; 5-HT, 5-Hydroxytryptophan; PMP, 1-Phenyl-3-methyl-5-pyrazolone; APPC, 5-amino-1-phenyl-1H-pyrazole-4-carbonitrile; MOPD, 3-(2-methylpropyl)-octahydropyrrolo[1,2-a]pyrazine-1,4-dione; TTPD, 1,3,7-trimethyl-2,3,6,7-tetrahydro-1H-purine-2,6-dione; BEP, 2-(1,3-benzodioxol-5-yl)-7-ethylimidazo[1,2-a]pyridine.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/1598b072559d5a283969f05d.png"},{"id":56925201,"identity":"f7c2d4c8-4e3b-4f08-87a8-6b342b739dc3","added_by":"auto","created_at":"2024-05-22 08:33:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2554970,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation network between liver-derived metabolites and colon-derived metabolites and their metabolic pathways.\u003cstrong\u003e a)\u003c/strong\u003e Overview of significant up- and downregulated metabolites derived from the liver and colon. \u003cstrong\u003eb)\u003c/strong\u003e and \u003cstrong\u003ee) \u003c/strong\u003eCorrelation network between the top 10 liver-derived metabolites and the top 5 colon-derived metabolites. Separately, \u003cstrong\u003eb) \u003c/strong\u003eshows the main effects of TSS treatment determined by comparing the TSS group with the Con group, and \u003cstrong\u003ec)\u003c/strong\u003e shows the associated effects of TSS and vancomycin treatment. \u003cstrong\u003ed)\u003c/strong\u003e Schematic diagram of the gut‒liver axes in which microbial metabolites are absorbed from the intestinal barrier to the liver through the portal vein. \u003cstrong\u003ec)\u003c/strong\u003e and \u003cstrong\u003ef)\u003c/strong\u003e are the numbers of metabolites that shared the same metabolic pathway derived from the liver and colon, respectively. Abbreviations: APPC, 5-amino-1-phenyl-1H-pyrazole-4-carbonitrile; PMP, 1-phenyl-3-methyl-5-pyrazolone; 4-MCD, 4-methoxycinnamaldehyde; TTPD, 1,3,7-trimethyl-2,3,6,7-tetrahydro-1H-purine-2,6-dione; OIELTD, 4-[2-(2-oxo-1-imidazolidinyl)ethyl]-1lambda~6,4-thiazinane-1,1-dione; ADTF, 3-acetyl-2,5-dimethylfuran; SDMA, N3,N4-dimethyl-L-arginine; DPGA1, 15-deoxy-Δ 12,14-prostaglandin A1; COEA, 4-(4-cyclohexylphenyl)-4-oxobut-2-enoic acid; DPA, (2R)-2,3-dihydroxypropanoic acid.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/b33cd0a6d588c440d743748f.png"},{"id":56925206,"identity":"560ef008-bb1d-41f9-b696-dec23fc049fc","added_by":"auto","created_at":"2024-05-22 08:33:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1952264,"visible":true,"origin":"","legend":"\u003cp\u003eCometabolism of liver-derived metabolites by the gut microbiota and its host after TSS treatment\u003cstrong\u003ea)\u003c/strong\u003e Overview of the numbers of significantly changed liver metabolites that came from the host, microbiota, cometabolism, and other sources. \u003cstrong\u003eb)\u003c/strong\u003e Enriched metabolic pathways from metabolite pathway enrichment analysis (MPEA) according to the differentially abundant metabolites from bacteria, the host, or both. \u003cstrong\u003ec)\u003c/strong\u003e Heatmap of Spearman correlation coefficients between screened metabolites and microbes.\u003cstrong\u003e d)\u003c/strong\u003e Bio-Sankey network for the top metabolic pathway of cometabolism of selected metabolic pathways, in which the network included all the microbiota that participated in the network. \u003cstrong\u003ee)\u003c/strong\u003e The STA-Sankey network for the top metabolic pathways associated with the cometabolism of selected metabolic pathways; the network shows theidentified microbiota from the TSS treatment. The red or green color of the bars and bands indicates up- or downregulation of bacteria and metabolites or positive or negative correlations between them. The shades of color (dark or light) indicate the statistical significance of bacteria/metabolites and their correlations, respectively. 0.01\u0026lt;*\u003cem\u003ep\u003c/em\u003e \u0026lt;0.05; **\u003cem\u003ep\u003c/em\u003e \u0026lt;0.01. For more details about MetOrigin, please visit \u003ca href=\"http://metorigin.met-bioinformatics.cn/\"\u003ehttp://metorigin.met-bioinformatics.cn\u003c/a\u003e. Abbreviations: SDMA, N3,N4-Dimethyl-L-arginine; FIPHA, 2-furyl[4-(1H-indol-4-yl)piperazino]methanone; OIELTD, 4-[2-(2-oxo-1-imidazolidinyl)ethyl]-1lambda~6~,4-thiazinane-1,1-dione; Pro-Hyp, Proline-hydroxyproline; TBAMBC, tert-Butyl N-[1-(aminocarbonyl)-3-methylbutyl]carbamate; GluAA, (5-L-Glutamyl)-L-Amino Acid; APPCD, 3-amino-2-phenyl-2H-pyrazolo[4,3-c]pyridine-4,6-diol; 4-MCD, 4-Methoxycinnamaldehyde; PAG, N-Phenylacetylglutamine; DPGA1, 15-Deoxy-Δ 12,14-prostaglandin A1; ADTF, 3-Acetyl-2,5-dimethylfuran; TU, Testosterone undecanoate; AAPAA, 2-(acetylamino)-3-[4-(acetylamino)phenyl]acrylic acid; DKTPGD2, 13,14-dihydro-15-keto-tetranor Prostaglandin D2; APPD, 3-amino-1H-pyrazolo[4,3-c]pyridine-4,6-diol.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/f2b677cb0a35d1ee0d282e44.png"},{"id":102785722,"identity":"72c925e9-cad4-4cd3-88cf-8d50d341c0d7","added_by":"auto","created_at":"2026-02-16 16:09:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":109629575,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/0563f3af-c306-4b40-82d3-b35b1a39f4e7.pdf"},{"id":56925588,"identity":"13e8e6a9-1078-4709-84a1-86c3ce39b527","added_by":"auto","created_at":"2024-05-22 08:41:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18311,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: Supplementary tables mentioned in this manuscript.\u003c/p\u003e","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/23850d4cf62ef0296a623375.docx"},{"id":56925192,"identity":"2c6e3448-3e18-4eac-b211-4f6ad1c669c8","added_by":"auto","created_at":"2024-05-22 08:33:42","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":46849,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: Supplementary tables mentioned in this manuscript.\u003c/p\u003e","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/ebe5b25b8a58bec70f9b9644.xlsx"},{"id":56926333,"identity":"3abbc377-df29-4e5b-987c-c01376d77dbb","added_by":"auto","created_at":"2024-05-22 08:49:42","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4456448,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: Supplementary tables mentioned in this manuscript.\u003c/p\u003e","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/76d5692e102f60642847b280.xlsx"},{"id":56926334,"identity":"65ede06e-8d3a-4201-965b-e12c71521cb6","added_by":"auto","created_at":"2024-05-22 08:49:42","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":631667,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Supplementary figures mentioned in this manuscript.\u003c/p\u003e","description":"","filename":"FigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/1b0ed4e41f9def5881324c76.png"},{"id":56925591,"identity":"65fc03ae-9d8a-4a63-98e8-770efbe49f2d","added_by":"auto","created_at":"2024-05-22 08:41:42","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":981210,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Supplementary figures mentioned in this manuscript.\u003c/p\u003e","description":"","filename":"FigureS2.png","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/1e7fac27e9e6920fb8bf6670.png"},{"id":56925208,"identity":"e42f04bd-df3e-4383-a18c-c960592be70a","added_by":"auto","created_at":"2024-05-22 08:33:43","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2221113,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Supplementary figures mentioned in this manuscript.\u003c/p\u003e","description":"","filename":"FigureS3.png","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/a747cfd2604f6a9c3849fcf9.png"},{"id":56925203,"identity":"49dfd1ac-2d05-452f-bc8e-ead91bd86d2b","added_by":"auto","created_at":"2024-05-22 08:33:42","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1353810,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Supplementary figures mentioned in this manuscript.\u003c/p\u003e","description":"","filename":"FigureS4.png","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/28c4d9cc45118161ba7482a7.png"},{"id":56925207,"identity":"98971d53-ff9f-4594-a49a-7c01401ac733","added_by":"auto","created_at":"2024-05-22 08:33:43","extension":"xls","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":4456448,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3: Taxonomic assignments and richness for all operational taxonomic units used in this study.\u003c/p\u003e","description":"","filename":"Additionalfile316srRNASequencing.xls","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/7a5c658177f3b884565165d1.xls"},{"id":56925204,"identity":"cda5ac86-82ea-43c5-960a-836af0d2c2ea","added_by":"auto","created_at":"2024-05-22 08:33:43","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":3077694,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 4: Metadata associated with all samples used in this study.\u003c/p\u003e","description":"","filename":"Additionalfile4Metabolicprofilingofliverandcolon.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/1d19355cbcc673ad4f91c1a0.xlsx"},{"id":56925196,"identity":"d084ffd3-c51e-41d4-a986-74d70a5d71dc","added_by":"auto","created_at":"2024-05-22 08:33:42","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":25045,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 5: ELISA data used in this study.\u003c/p\u003e","description":"","filename":"Additionalfile5ELISAanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4190281/v1/fb103ad7d1af7b66e8b7fdc7.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of Long-term Microgravity Exposure on Liver Activity and the Gut Microbiota as well as Gut-liver Axis Homeostasis","fulltext":[{"header":"Background","content":"\u003cp\u003eFor decades, huge developments in spaceflight have been made owing to the use of more accurate monitoring technology and systematic human health maintenance strategies. However, the effects of long-term space travel on physiological and pathological processes and the resulting impacts on crew health and operational performance have not been fully elucidated. Recently, spaceflight-associated changes in human health due to complex arrays of environmental stressors (mostly weightlessness, radiation, and isolation) were transcendentally characterized through multiomics approaches in which one of two pairs of monozygotic twin astronauts executed a 1-year mission. This integrated and multiomics analysis revealed both transient and persistent metabolic profiles, immunomodulatory effects and gut microbiota shifts that constitute spaceflight-dependent physiological changes during the 1-year follow-up[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Another spaceflight study provided evidence that intestinal microflora was defined by increased abundance of opportunistic pathogens and reduced abundance of beneficial bacteria for astronauts whom executed missions aboard the space vehicles Salyut/Soyuz and Mir for up to 96 days[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition, studies of Mice Flowening Aboard the Space Transportation System-135 have shown that a 13-day flight duration caused activation of PPARα-mediated pathways and potentially hepatic stellate cell activation, both of which may be coincident with increased bile acids and early signs of liver injury, increasing the risk for nonalcoholic fatty liver disease[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the spaceflight-associated factors, microgravity is the primary factor experienced during spaceflight and can affect a number of physiological processes in various organs, and rise changes in microbial complexities and diversity to astronaut [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In particular, the National Aeronautics Institute (NASA)-led rodent research 5 mission demonstrated that constant microgravity exposure processed elevated gut microbial diversity and alters the concentrations of the metabolite lactic acid, leucine/isoleucine, and glutathione, subsequently promoting osteoblastogenic activation[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, considerable alterations in the growth rate and secondary metabolism of highly evolved microbes have been viewed in spaceflight and ground-based microgravity analog experiments[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although prospective studies in regard to humans in space never stop increasing, we have limited knowledge of the full range of underlying molecular mechanisms, physiological processes, and integrated crosstalk that occurring during long-term spaceflight.\u003c/p\u003e \u003cp\u003eGrowing evidences has proven that intestinal microbiota dysbiosis sets the stage for impairments in intestinal epithelial barrier function, bile acid signaling, and intestinal immunity and consequently promotes liver insufficiency, intestinal hypomotility and interrelated enterohepatic problems[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In fact, microbial physiological effects mostly rely on microbial fermentation, and gut-derived products exert beneficial (such as short-chain fatty acids) or proinflammatory (such as lipopolysaccharide) effects on the host, which can reach the liver through the portal vein. In turn, in the bidirectional communication of the gut-liver axis, the liver shapes intestinal microbial communities through the enterohepatic circulation of bile acid[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Interestingly, recent evidence indicates that shifting levels of microbial intermediate metabolites lead to changes in molecular activities, for example, intestinal farnesoid X receptor (FXR) signaling, which contributes to host physiology[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTaken together, these findings led us to speculate that spaceflight-dependent microgravity exposure may cause disorder to gut microbiota and its secondary metabolism, which subsequently contribute to gut barrier disruption and shape liver insufficiency. To confirm this speculation, we conducted metabolic profiling of both the liver and colon, as well as 16S ribosomal RNA gene sequencing of resident microbes in specimens from rats whom suffering microgravity exposure via hindlimb suspension-a well-established microgravity analog model. Interestingly, our viewpoints were further verified by observing hepatic lipid accumulation, oxidative stress and inflammation, altered enteric microorganisms and intestinal barrier damage, paralleling with dysregulated portal metabolic flux, thus providing insight into the importance of maintaining gut-liver axis homeostasis in future manned spaceflight.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimals and reagents\u003c/h2\u003e \u003cp\u003eSprague‒Dawley rats aged 6 weeks and male with specific pathogen-free (SPF) and sterile reproduction feed (SPF-F01) were purchased from SPF (Beijing) Biotechnology Co., Ltd. (license number: SCXK(京)2019-0010). Vancomycin hydrochloride (#V820413) was obtained from MACKLIN. Chloral hydrate (#23100), sodium chloride (#S0817) and paraformaldehyde (#158127) were obtained from Sigma Aldrich. Rat lipopolysaccharide (LPS) ELISA kit (#JM-10910R1), Rat lipopolysaccharide binding protein (LBP) ELISA kit (#JM-10507R1), Rat D-Lactate ELISA kit (#JM-10501R1), Rat diamine oxidase (DAO) ELISA kit (#JM-02209R1), Rat secretory immunoglobulin A (sIgA) ELISA kit (#JM-01836R1), Rat calprotectin (CALP) ELISA kit (#JM-02086R1), Rat chromograninA (CgA) ELISA kit (#JM-10970R1), Rat total cholesterol (TC) ELISA kit (#JM-02017R1), Rat triglyceride (TG) ELISA kit (#JM-02016R1), Rat alanine transaminase (ALT) ELISA kit (#JM-10921R1), Rat aspartate aminotransferase (AST) ELISA kit (#JM-10641R1), Rat malondialdehyde (MDA) ELISA kit (#JM-10323R1), Rat glutathione peroxidase (GSH-Px) ELISA kit (#JM-02173R1), Rat superoxide dismutase (SOD) ELISA kit (#JM-01793R1), Rat cytokines TNF-α ELISA kit (#JM-01587R1), Rat cytokines IL-6 ELISA kit (#JM-01597R1), and Rat cytokines IL-1β ELISA kit (#JM-01454R1) were from JINGMEI Biotechnology Co., Ltd. (JiangSu, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAnimal modeling\u003c/h2\u003e \u003cp\u003eA total of 72 Sprague‒Dawley rats (200\u0026ndash;250 g) aged 6 weeks were subjected to 12 h day/night cycles at a constant temperature of 22\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C and 60% humidity. After 1 week of acclimation to the animal facilities and behavioral test regimens, the rats were randomly assigned to six groups (n\u0026thinsp;=\u0026thinsp;12 per group), and the microgravity rats were generated using hindlimb unloading approaches as described previously[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Briefly, all the groups were fed a normal diet and had free access to water. Separately, (1) control group (Con group), wherein rats were fed without treatment, (2) vehicle group (GSW group), wherein rats were gave with sterile water via intragastric administration, (3) intestinal microbiota damaged group (Van group), wherein rats were gave with antibiotic water containing vancomycin hydrochloride at dose 0.05 g/kg/d via intragastric administration, (4) microgravity exposure group (TSS), wherein rats were treated with tail-suspension to the extent of hindlimb unloading, (5) intestinal microbiota damaged combined with microgravity exposure group (Van\u0026thinsp;+\u0026thinsp;TSS), wherein rats were treated with tail-suspension and vancomycin at dose 0.05 g/kg/d via intragastric administration, and (6) microgravity exposure combined with vehicle group, wherein rats were treated with tail-suspension and sterile water via intragastric administration. The intragastric volume was maintained at 1 mL. Body weight was measured at baseline and at 7-day intervals until euthanasia. After 35 days feeding, all SD rats was intraperitoneal 8.0% sodium pentobarbital with a dose of 800 mg/kg body weight for euthanasia and the injection was intermittent. All the procedures were performed in accordance with the Guidelines in the Care and Use of Animals and approved by the China Astronaut Research and Training Center Animal Welfare Committee (Permit Number: ACC-LACUC-2021-014).\u003c/p\u003e \u003cp\u003eAfter 35 days of feeding and modeling, fresh feces were collected into microtubes. Then, the rats were sacrificed, and blood was collected via cardiac puncture into EDTA and heparin sodium blood collection vessels. Next, the blood was transferred to 4\u0026deg;C for 2 hours. Subsequently, the blood was centrifuged at 1000\u0026times;g and 4\u0026deg;C for 20 min to obtain serum and plasma samples, and all the samples were immediately stored at -20\u0026deg;C for 30 min and subsequently transferred to -80\u0026deg;C before biochemical evaluation. Furthermore, liver and colon tissue samples were dissected and rapidly frozen. A portion of the tissue was washed with 0.85% sodium chloride and sufficiently fixed in 4% paraformaldehyde (10x).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eH\u0026amp;E Staining\u003c/h2\u003e \u003cp\u003eThe liver and colon hematoxylin-eosin (H\u0026amp;E) staining protocol was performed according to previous manuals[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Briefly, liver and colon tissues were sliced and washed with a gradient of ethyl alcohol (#459836) starting from 50%, followed by 70%, 85%, 95% and HPLC to remove residual paraformaldehyde, each for 1 h. The remaining alcohol was subsequently exchanged for xylene (#214736) for 1 h. The tissues were subsequently infused in a mixture of isopyknic liquid paraffin and xylene for 1 h, followed by incubation in paraffin for 3 h. Subsequently, the embedded colon blocks were sliced into \u0026ldquo;Swiss Rolls\u0026rdquo; on the vertical side, and the liver blocks were sliced into 12-\u0026micro;m-thick sections. Later, the \u0026ldquo;Swiss Rolls\u0026rdquo; and liver sections were stained with hematoxylin solution for 3\u0026ndash;5 min, differentiated with hydrochloric acid aqueous solution, blue-reacted with ammonia aqueous solution, and washed with distilled water. Adjacently, the tissues were washed with dehydration ethyl alcohol at a gradient of 85% and 95% alcohol and subsequently stained with eosin for 5 minutes. After sealing with neutral resins, microstructure scanning was performed on a 3D HISTECH (version 2.3.2), and images were acquired on a CaseViewer (version 2.4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOil red O staining\u003c/h2\u003e \u003cp\u003eThe morphology of the lipids and lipids in the liver was detected using oil red O staining according to previous protocols [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which included tissue collection, sectioning, staining, and imaging. Briefly, liver tissues were sectioned into 12-\u0026micro;m-thick sections, after which\u0026thinsp;~\u0026thinsp;1 ml of ORO working solution was added (as specified in the protocol) to cover the sections. Next, the sections were incubated with ORO working solution for 5 min and then rinsed with the sections in a slide holder, while the biopsies were facing away from the running water. Then, coverslips were placed on the slide holder, and the sections were checked by using a scanning microscope for image capture.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eELISA\u003c/h2\u003e \u003cp\u003e Enzyme-linked immunosorbent assay (ELISA) analysis of rodent serum LPS, LBP, DAO, D-lactate, ALT, AST, plasma sIgA, calprotein and CgA as well as MDA, GSH-Px, SOD, TNF-α, IL-6, and IL-1β in liver homogenates was completed using an ELISA kit and a SYNERGY H1 microplate reader (BioTek, USA) according to the manufacturer\u0026rsquo;s operation manual. Briefly, serum, plasma and liver tissue homogenates were thawed on ice, assayed in triplicate and diluted 5-fold according to the manufacturer\u0026rsquo;s recommendations. After that, the samples, controls, and calibrators were incubated, coated, washed, colored and read step by step according to the manufacturer\u0026rsquo;s protocols.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e16S rRNA gene Sequencing\u003c/h2\u003e \u003cp\u003eFecal DNA was extracted via Fast DNA SPIN extraction kits (MP Biomedicals, Santa Ana, CA, USA), and its quantity and quality were verified using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively. The V3\u0026ndash;V4 region of the bacterial 16S rRNA gene was amplified via PCR for 16S rDNA amplicon pyrosequencing on the Illumina MiSeq platform[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Afterwards, the sequencing data were analyzed by Quantitative Insights Into Microbial Ecology (QIIME, v1.8.0) as previously described[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Finally, statistical analysis, including alpha diversity indices, differences, and similarities, was performed using the QIIME and R packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic analysis\u003c/h2\u003e \u003cp\u003eLiver and colon tissues (100 mg) were individually ground with liquid nitrogen, and the homogenate was resuspended in prechilled 80% methanol by vortexing. These samples were incubated on ice for 5 min and then centrifuged at 15,000 \u0026times; g and 4\u0026deg;C for 20 min. A portion of the supernatant was diluted to the final concentration with LC‒MS grade water containing 53% methanol. The samples were subsequently transferred to a fresh Eppendorf tube and then centrifuged at 15000 \u0026times; g and 4\u0026deg;C for 20 min[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Finally, the supernatant was injected into the UHPLC‒MS/MS system for analysis using a Vanquish UHPLC system (Thermo Fisher, Germany) coupled with an Orbitrap Q Exactive\u003csup\u003e\u0026trade;\u003c/sup\u003e HF mass spectrometer (Thermo Fisher, Germany) as described previously[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The raw data generated by UHPLC‒MS/MS were processed using Compound Discoverer 3.1 (CD3.1; Thermo Fisher) to perform peak alignment, peak picking, and quantitation for each metabolite. Then, metabolite identification was conducted using the KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/pathway.html\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/pathway.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), HMDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hmdb.ca/metabolites\u003c/span\u003e\u003cspan address=\"https://hmdb.ca/metabolites\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and LIPID Maps database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lipidmaps.org/\u003c/span\u003e\u003cspan address=\"http://www.lipidmaps.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data processing was performed step by step with metaX[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] (a flexible and comprehensive software package for processing metabolomics data) according to the documented protocol[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Statistical analyses were performed using the statistical software R (version R-3.4.3), Python (version Python 2.7.6) and CentOS (release 6.6).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBody weight\u003c/h2\u003e \u003cp\u003eThe body weights (BWs) of the subjects constantly increased during the intervention. Notably, the hindlimb unloading groups (TSS, Van\u0026thinsp;+\u0026thinsp;TSS, and GSW\u0026thinsp;+\u0026thinsp;TSS) exhibited a lower growth rate than their reference groups (Con, Van, and GSW). In addition, after 35 days feeding, the gap between BWs of rats from group Van\u0026thinsp;+\u0026thinsp;TSS and that from group Van had significantly increased when compared with the gap between BWs of rats from group TSS and from group Con(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMicrogravity exposure induced lipid deposition and inflammation within the liver\u003c/h2\u003e \u003cp\u003eThe liver is the central metabolic organ and is the first bypass organ through which nutrients are absorbed in the human body and rodents; moreover, the liver is susceptible to and prone to complications from microgravity when stimulated by TSS. As characterized by liver hematoxylin-eosin (H\u0026amp;E) staining, TSS treatment and vancomycin administration resulted in a loose tissue structure and a decreased total cell area, and vancomycin administration aggravated the liver tissue dispersion in rats treated with the tail suspension (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and c). Moreover, liver oil red O staining indicated that TSS treatment promoted lipid deposition in the liver of rats from group TSS, GSW\u0026thinsp;+\u0026thinsp;TSS, and Van\u0026thinsp;+\u0026thinsp;TSS. As expected, vancomycin administration also accentuated liver lipid deposition in the group Van\u0026thinsp;+\u0026thinsp;TSS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and d). In agreement with the above findings, the levels of total cholesterol (TC) in liver tissue homogenates were significantly greater in group which combined TSS treatment with vancomycin administration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee), and total triglyceride (TG) was significantly greater in all TSS-related treatment groups (including the TSS, GSW\u0026thinsp;+\u0026thinsp;TSS, and Van\u0026thinsp;+\u0026thinsp;TSS groups) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). To further evaluate the degree of liver damage, the concentrations of serum alanine transaminase (ALT) and aspartate aminotransferase (AST) were measured. Unexpectedly, both the two serum biomarkers did not significantly increase in TSS-related groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). Oxidative-related factors in the liver were subsequently evaluated, including malondialdehyde (MDA), glutathione peroxidase (GSH-Px), and superoxide dismutase (SOD). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei, the levels of liver MDA in group Van, TSS, Van\u0026thinsp;+\u0026thinsp;TSS and GSW\u0026thinsp;+\u0026thinsp;TSS were significantly greater than those in their comparable groups. Moreover, compared with corresponding controls, the concentrations of GSH-Px in liver homogenates from rats of group Van, Van\u0026thinsp;+\u0026thinsp;TSS and GSW\u0026thinsp;+\u0026thinsp;TSS were also obviously greater (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ej). In contrast, the levels of SOD in liver homogenates from group GSW\u0026thinsp;+\u0026thinsp;TSS were markedly lower than it from group GSW (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ek). Furthermore, the concentrations of proinflammatory cytokines (TNF-α, IL-6, and IL-1β) in liver homogenates were also detected. The results showed that the levels of TNF-α in group Van were greater than that in group GSW, and were lower in both of group Van\u0026thinsp;+\u0026thinsp;TSS and GSW\u0026thinsp;+\u0026thinsp;TSS than those in their comparable group Van and GSW (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003el). Moreover, the levels of IL-6 in group TSS, Van\u0026thinsp;+\u0026thinsp;TSS, and GSW\u0026thinsp;+\u0026thinsp;TSS were significantly greater compared with their control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003em). Simultaneously, the levels of IL-1β exhibited a significant increase in the GSW\u0026thinsp;+\u0026thinsp;TSS group, whereas the other TSS-related groups (TSS and Van\u0026thinsp;+\u0026thinsp;TSS) did not exhibit any changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003en).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIntegrality of the colonic mucosa\u003c/h2\u003e \u003cp\u003eThe colon mucosa is composed of the intestinal epithelium, lamina propria, muscle and so on. As shown in H\u0026amp;E images of colon, the rats subjected to TSS treatment and vancomycin administration owned a colon which presented with sparse and narrow mucosa, irregular crypt bases, rough epithelium surfaces, and damaged lamina propria in some niches whereas their control rats did not (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In contrast to rats who were dealt with vancomycin administration, rats suffering from TSS treatment had better colon mucosa, who only damaged in partial segment, whereas vancomycin administration damaged the whole mucosa. Moreover, we detected 7 serum/plasma biomarkers associated with the permeability of intestinal barrier function, namely, lipopolysaccharide (LPS), lipopolysaccharide binding protein (LBP), D-lactate (D-Lac), diamine oxidase (DAO), secretory immunoglobulin A (SIGA), calprotectin (CALP) and chromogranin A (CGA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Interestingly, the serum microbial endotoxin LPS was significantly increased in group TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00014), and the serum D-lactate was increased in group TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and GSW\u0026thinsp;+\u0026thinsp;TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012). In response to the endotoxin, the concentration of serum LBP was obviously lower in group TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04),\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVan (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.86\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), Van\u0026thinsp;+\u0026thinsp;TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) and GSW\u0026thinsp;+\u0026thinsp;TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.98\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e). Moreover, the concentrations of serum intracellular enzyme DAO whom oxidizes diamines (histamine, putrescine and cadaverine) were also markedly lower in group TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0036) and Van\u0026thinsp;+\u0026thinsp;TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.92\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) than their comparable groups. Unexpectedly, the SIGA did not significantly change during TSS or vancomycin treatment, except for a mild decrease in group TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.16). CALP is a calcium-containing protein derived from neutrophils and macrophages which plays a role in anti-inflammatory and immunologic enhancement. Interestingly, the plasma CALP was expressed in TSS-dependent manner. Namely, the levels of plasma CALP were consistently lower in rats among group TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.38\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), Van\u0026thinsp;+\u0026thinsp;TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.37\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) and GSW\u0026thinsp;+\u0026thinsp;TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.04\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). In addition to CALP, another immune related protein-CGA, had increased concentrations in plasma from rats among group TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) and GSW\u0026thinsp;+\u0026thinsp;TSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDysfunction of the gut microbiota caused by microgravity exposure\u003c/h2\u003e \u003cp\u003eThe relative abundance of fecal microbiota in group TSS was characterized by decreasing proportions of genus \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e and increasing genus \u003cem\u003eShigella\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eDesulfovibrio\u003c/em\u003e, and \u003cem\u003eCoprococcus\u003c/em\u003e when compared with group Con. Reference rats treated with vancomycin can destroy microbiota. Consistent with these findings, when rats in the Van group were treated with vancomycin, the intestinal microbiota exhibited a remarkable increase in the abundance of the detrimental genera \u003cem\u003eShigella\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eMorganella\u003c/em\u003e, \u003cem\u003eDesulfovibrio\u003c/em\u003e, and \u003cem\u003ePhascolarctobacterium\u003c/em\u003e and few comparable beneficial genera \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eOscillospira\u003c/em\u003e and \u003cem\u003eCoprococcus\u003c/em\u003e compared with those in the GSW group. Furthermore, when the plants were overlaid with microgravity, the Van\u0026thinsp;+\u0026thinsp;TSS group presented a lower proportion of \u003cem\u003eLactobacillus\u003c/em\u003e and greater abundances of \u003cem\u003eShigella\u003c/em\u003e and \u003cem\u003eEnterococcus\u003c/em\u003e than did the Van group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The taxonomic classification tree evolved with tail-suspension and vancomycin treatment, during which the phylum Gammaproteobacteria evolved into the group Van and Van\u0026thinsp;+\u0026thinsp;TSS, whereas TSS treatment influenced mainly gut microorganisms at the genus level; these changes included the evolution of \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eStreptococcus, Coprococcus, Oscillospira, and Shigella\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe microbial α diversity measures the richness, diversity, and evenness of taxonomy, of which the Chao1 estimator reflects the microbial richness by calculating \u0026ldquo;Singleton\u0026rdquo; together with \u0026ldquo;Doubleton\u0026rdquo; in the operational taxonomic unit (OTU). According to the sparse curves, the Chao1 indices increased with sequencing depth and equilibrated at nearly 20,000 depths, which indicated that the obtained OTUs were sufficient for feature extraction. In particular, the Van and Van\u0026thinsp;+\u0026thinsp;TSS groups had markedly lower α diversity indices than did the other groups, and the Con group had the highest α diversity index (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). In contrast to that of α diversity, the microbial β diversity differed according to treatment. Except for the Van and Van\u0026thinsp;+\u0026thinsp;TSS groups, which were strongly different from the control group, the other groups exhibited similar β diversity in terms of microbial community structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eThe core and unique OTUs were counted in a Venn diagram. Herein, the TSS group had markedly fewer unique OTUs than did the Con group (from 4076 to 1419). Similarly, the number of unique OTUs in the Van combined with Van\u0026thinsp;+\u0026thinsp;TSS group also decreased compared with that in the GSW or GSW\u0026thinsp;+\u0026thinsp;TSS groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). Finally, biomarkers of the microbiota from each group were screened via linear discriminant analysis (LDA) effect size (LEfSe) analysis, which integrates the nonparametric Kruskal‒Wallis test and Wilcoxon rank sum test with linear discriminant analysis (LDA). According to the LDA scores, the intestinal microbial communities of rats in the TSS group were characterized by the genera Streptococcus, Candidatus_Solibacter, Tepidimicrobium, Butyrivibrio, Anaerotruncus and some other unknown genera. Furthermore, with increasing vancomycin concentration, the microbial ecology of rats in the Van\u0026thinsp;+\u0026thinsp;TSS group was enriched for the genera Bilophila, Sutterella, Akkermansia, and Enterococcus and some other unknown genera (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic profiling on liver and colon\u003c/h2\u003e \u003cp\u003eThe supernatants extracted from rats\u0026rsquo; liver and colon were subjected to metabolic profiling via ultrahigh-performance liquid chromatography coupled with Orbitrap Exploris mass spectrometry (UPLS-MS\u003csup\u003e2\u003c/sup\u003e) to reveal changes in the gut-liver axes. Notably, the TSS treated groups, including group TSS, GSW\u0026thinsp;+\u0026thinsp;TSS and Van\u0026thinsp;+\u0026thinsp;TSS, exhibited similar projections in hepatic metabolic profiles and processed distinguishable distances far away from their reference groups (Con, GSW, and Van) which distributed along with the x-axis in the score projection space which was modeled by orthogonal signal correction-orthogonal partial least squares-discriminant analysis (O\u003csub\u003e2\u003c/sub\u003ePLS-DA). Furthermore, vancomycin treatment induced distinct clustering which distributed along with the y-axis for liver metabolism when combined with TSS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In contrast to liver metabotyples, colon metabolic profiles had shown to be strongly related to the impactions of TSS and sensitive to vancomycin treatment as characterized by distinct clustering which distributed along with x-axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). Liver and colon metabolic shifts were further verified by heatmaps of metabolite intensity in which liver metabolic profiles appeared in according to TSS associated clustering whereas the colon presented vancomycin related clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, f). Furthermore, the characteristic metabolites were screened by the VIPs (predictive variable importance in the projection from O\u003csub\u003e2\u003c/sub\u003ePLS-DA), FC (fold change), and P value (one-way ANOVA with standard Bonferroni correction) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As shown by the set of liver- and colon-specific metabolites, a total of 1265 metabolites (907 from the positive ion mass spectrum and 358 from the negative ion mass spectrum) were identified in the liver and colon, among which 164 metabolites from liver were significantly upregulated and 111 downregulated along with TSS treatment. Yet, 270 upregulated and 74 downregulated liver metabolites were associated with treatment which combined TSS with vancomycin administration. In contrast, few metabolites (a total of 17) from colon shown to be sensitive to TSS treatment, but many (62 upregulated and 99 downregulated in the Van\u0026thinsp;+\u0026thinsp;TSS group versus the Van group) changed with vancomycin treatment which could destroy the microbiota.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of significant metabolites between differential treatment groups according to VIP, FC and P value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompared Treatments\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNum. of Total Ident.\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNum. of Total Sig.\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNum. of Sig.Up\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNum. of Sig.down\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCon.vs.GSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVan.vs.GSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVan\u0026thinsp;+\u0026thinsp;TSS.vs.Van\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTSS.vs.Con\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e907\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e358\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e199\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e117\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSW\u0026thinsp;+\u0026thinsp;TSS.vs.GSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSW\u0026thinsp;+\u0026thinsp;TSS.vs.Van\u0026thinsp;+\u0026thinsp;TSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCon.vs.GSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eColon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVan.vs.GSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVan\u0026thinsp;+\u0026thinsp;TSS.vs.Van\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTSS.vs.Con\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e907\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e358\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSW\u0026thinsp;+\u0026thinsp;TSS.vs.GSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSW\u0026thinsp;+\u0026thinsp;TSS.vs.Van\u0026thinsp;+\u0026thinsp;TSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of significant metabolites between Compared Treatments screening by VIP, FC and P-value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompared Treatments\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNum. of Total Ident.\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNum. of Total Sig.\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNum. of Sig.Up\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNum. of Sig.down\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCon.vs.GSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVan.vs.GSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVan\u0026thinsp;+\u0026thinsp;TSS.vs.Van\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTSS.vs.Con\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e907\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e358\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e199\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e117\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSW\u0026thinsp;+\u0026thinsp;TSS.vs.GSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSW\u0026thinsp;+\u0026thinsp;TSS.vs.Van\u0026thinsp;+\u0026thinsp;TSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCon.vs.GSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eColon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVan.vs.GSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVan\u0026thinsp;+\u0026thinsp;TSS.vs.Van\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTSS.vs.Con\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e907\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e358\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSW\u0026thinsp;+\u0026thinsp;TSS.vs.GSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSW\u0026thinsp;+\u0026thinsp;TSS.vs.Van\u0026thinsp;+\u0026thinsp;TSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eThe significant metabolites were screened by VIP (Variable Importance in the Projection), FC(Fold Change), and P-value, VIPs were from O\u003csub\u003e2\u003c/sub\u003ePLS-DA (orthogonal signal correction-orthogonal partial least squares-discriminant analysis);P-value was calculated by one-way ANOVA with standard Bonferroni correction. The threshold values were set as VIP \u0026gt; 1.0, FC \u0026gt; 1.2 or FC \u0026lt; 0.833 and P-value \u0026lt; 0.05. 1. Compared Samples: compared treatments which compare the former treatment with the latter; 2. Num of Total Ident: total numbers of identified metabolites from MS༛3. Num of Total Sig: total numbers of significant metabolites; 4. Num of Sig Up: total numbers of significant up-regulated metabolites; 5. Num of Sig down: total numbers of significant down-regulated metabolites。\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe significant metabolites were screened by variable importance in projection (VIP), fold change (FC), and P value. VIPs were identified via orthogonal signal correction-orthogonal partial least squares-discriminant analysis (O2PLS-DA). The P value was calculated via one-way ANOVA with standard Bonferroni correction. The threshold values were set as VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.0, FC\u0026thinsp;\u0026gt;\u0026thinsp;1.2 or FC\u0026thinsp;\u0026lt;\u0026thinsp;0.833 and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. 1. Comparison of Samples: Comparisons of the former treatment with the latter; 2. Num of total identity: total number of identified metabolites from MS; 3. Num of total Sig: total number of significant metabolites; 4. Num of Sig Up: total number of significantly upregulated metabolites; 5. Num of Sig: total number of significantly downregulated metabolites.\u003c/p\u003e \u003cp\u003eMetabolite pathway enrichment analysis (MPEA) was performed for physiological interpretation base on metabolites screening from various treatments. The pathways mostly enriched for TSS related metabolites derived from liver were defined as aminoacyl-tRNA biosynthesis; mineral absorption; serotonergic synapse; valine, leucine and isoleucine biosynthesis; tryptophan metabolism; and vitamin B6 metabolism. And, the colon-derived metabolites were associated with the pathway of gastric acid secretion (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, the rats whom received vancomycin administration in combination with TSS treatment enriched pathways which hit many proinflammatory metabolites. Namely, the liver exhibited changes in arachidonic acid metabolism, the oxytocin signaling pathway, and renin secretion, and the colon exhibited changes in glycerophospholipid metabolism (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable S1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKEGG enrichment of characteristic metabolites between the Van\u0026thinsp;+\u0026thinsp;TSS group and the Van group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMapID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMapTitle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMetaIDs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap00590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArachidonic acid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProstaglandin E2; Prostaglandin H2; Lipoxin B4; Thromboxane B2; Prostaglandin D2; 16(R)-HETE; Arachidonic acid; Prostaglandin J2; 5-OxoETE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap04924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRenin secretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProstaglandin E2; Adenosine; Adenosine 5'-monophosphate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap00564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlycerophospholipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO-Phosphorylethanolamine; Phosphoethanolamine; Phosphocholine; Cytidine 5'-diphosphocholine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eMapID: map ID of the enriched KEGG pathways. MapTitle: title of enriched KEGG pathways. \u003cb\u003eP value\u003c/b\u003e: Overrepresentation analysis was implemented using a \u003cem\u003ehypergeometric test\u003c/em\u003e to evaluate whether a particular metabolite set was represented more than expected by chance within the given compound list. One-tailed \u003cem\u003ep\u003c/em\u003e values are provided after adjusting for multiple testing. MetaIDs: List of the input metabolites that participated in the KEGG pathway.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKEGG enrichment of liver metabolites derived from the liver and colon between the TSS and Con treatment groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMapID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMapTitle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMetaIDs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap00970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAminoacyl-tRNA biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL-Asparagine; L-Tyrosine; O-Phospho-L-serine; L-Threonine; L-Phenylalanine; L-Glutamic acid; Methionine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap04978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMineral absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL-Asparagine; L-Threonine; L-Phenylalanine; Methionine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap04726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSerotonergic synapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProstaglandin E2; Thromboxane B2; Prostaglandin D2; Prostaglandin J2; Prostaglandin A2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap00290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValine, leucine and isoleucine biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3-Methyl-2-oxobutanoic acid; L-Threonine; 2-Isopropylmalic acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap00380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTryptophan metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTryptamine; Kynurenic acid; Xanthurenic acid; L-Kynurenine; Quinolinic acid; Indole-3-acetic acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap00750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVitamin B6 metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePyridoxamine; Pyridoxine; D-Erythrose 4-phosphate; 4-Pyridoxic acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap04971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGastric acid secretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHistamine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKEGG Enrichment of characteristic metabolites derived from liver and colon between compared treatments TSS vs Con\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMapID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMapTitle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMetaIDs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap00970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAminoacyl-tRNA biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL-Asparagine; L-Tyrosine; O-Phospho-L-serine; L-Threonine; L-Phenylalanine; L-Glutamic acid; Methionine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap04978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMineral absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL-Asparagine; L-Threonine; L-Phenylalanine; Methionine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap04726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSerotonergic synapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProstaglandin E2; Thromboxane B2; Prostaglandin D2; Prostaglandin J2; Prostaglandin A2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap00290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValine, leucine and isoleucine biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3-Methyl-2-oxobutanoic acid; L-Threonine; 2-Isopropylmalic acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap00380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTryptophan metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTryptamine; Kynurenic acid; Xanthurenic acid; L-Kynurenine; Quinolinic acid; Indole-3-acetic acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap00750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVitamin B6 metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePyridoxamine; Pyridoxine; D-Erythrose 4-phosphate; 4-Pyridoxic acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emap04971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGastric acid secretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHistamine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e(1) MapID: map ID of enriched KEGG Pathway. (2) MapTitle༚title of enriched KEGG Pathway. (3) Over Representation Analysis was implemented using the \u003cem\u003ehypergeometric test\u003c/em\u003e to evaluate whether a particular metabolite set is represented more than expected by chance within the given compound list. One-tailed \u003cem\u003ep\u003c/em\u003e values are provided after adjusting for multiple testing. (4) MetaIDs༚list of those input metabolites that participated in this KEGG Pathway.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(1) MapID: Map ID of the enriched KEGG pathways. (2) MapTitle: title of enriched KEGG pathways. (3) Overrepresentation analysis was implemented using a \u003cem\u003ehypergeometric test\u003c/em\u003e to evaluate whether a particular metabolite set was represented more than expected by chance within the given compound list. One-tailed \u003cem\u003ep\u003c/em\u003e values are provided after adjusting for multiple testing. (4) MetaIDs: List of those input metabolites that participated in this KEGG pathway.\u003c/p\u003e \u003cp\u003eMoreover, microbial products that passed into the liver via the portal vein were significantly up- or downregulated associating with TSS treatment. Specifically, the relative abundances of salicylic acid, N-phenylacetylglutamine, N-acetyl-D-galactosamine, 2-isopropylmalic acid, pimelic acid and L-ergothioneine were significantly increased, while the relative abundances of Glu-Glu, glycoursodeoxycholic acid, glycolithocholic acid, theophylline, o-toluic acid, 5-aminopentanoate, pseudouridine and D-cysteine were significantly decreased (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Apart from microbial products, 38 metabolites changed with TSS treatment and were cometabolized by the microbiota and its host.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMetabolites derived from Liver were significantly correlated with those from colon\u003c/h2\u003e \u003cp\u003eThe effects of TSS on substance metabolism among liver and colon were characterized by a number of metabolites whom significantly changed in response to the TSS or antibiotics treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), especially for the comparable groups TSS vs Con, Van\u0026thinsp;+\u0026thinsp;TSS vs Van, and Van vs GSW treatments. Microbial fermentation of dietary items could produce a myriad of metabolites which might later be absorbed by enterocytes and reach the liver via the portal vein (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). As a result, liver-derived metabolites are strongly correlated with colon-derived metabolites. Consistent with this view, we observed that colon-derived metabolites APPC, PMP, 4-MCD, TTPD, and 10-hydroxydecanoic acid were strongly associated with liver-derived metabolites D-proline, SDMA, spermidine, hexanoylcarnitine, proline, Ala-Leu, ADTF, OIELTD, Pro-Leu, and 1-methylxanthine (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Moreover, the correlation network was dramatically altered by TSS when combined with vancomycin treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). In particular, the liver-derived metabolite APPC, which was originally present in the colon metabolic fluxes, was positively correlated with creatine, β-nicotinamide mononucleotide, and phenylacetylglycine whereas was negatively correlated with N-oleoyl dopamine and sakuranetin. Additionally, the signaling pathways enriched by liver- and colon-derived biomarkers partially reflect the important impactions of TSS treatment on the microbiota and its host. TSS treatment had the most differential effects on metabolic pathways, paralleling with some effects on neuroactive ligand\u0026ndash;receptor interactions and protein digestion and absorption (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Expectedly, these TSS-related pathways were further extended by group Van\u0026thinsp;+\u0026thinsp;TSS which combined TSS with vancomycin treatment, and these impactions were defined by phenylalanine metabolism, purine metabolism, pyrimidine metabolism, taurine and hypotaurine metabolism, vitamin B6 metabolism, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eThe impact of TSS treatment on microbial cross-talking with their host\u003c/h2\u003e \u003cp\u003eAmong liver biomarkers that were obviously associated with TSS treatment, 5 metabolites were from the host, 15 were microbiota derived, 48 were cometabolized by both agents, and the others were specifically from the drug, food, environment or unknown sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). When we searched for the most enriched metabolic pathways according to metabolites derived from liver, bacteria, or both through metabolite pathway enrichment analysis (MPEA), we found that some cometabolism pathways shared by the gut microbiota and its host were characterized by aminoacyl-tRNA biosynthesis and vitamin B6 metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). Moreover, a cluster of bacteria, mainly including Coriobacteriaceae, \u003cem\u003eButyricicoccus pullicaecorum\u003c/em\u003e, \u003cem\u003eCoprococcus\u003c/em\u003e, \u003cem\u003eMucispirillum schaedleri\u003c/em\u003e, and \u003cem\u003eAnaerotruncus\u003c/em\u003e; \u003cem\u003eunidentified F16\u003c/em\u003e; \u003cem\u003eLactobacillus vaginalis\u003c/em\u003e; \u003cem\u003eChristensenella\u003c/em\u003e; \u003cem\u003eDesulfovibrio\u003c/em\u003e; \u003cem\u003eunidentified Flexispira\u003c/em\u003e; and \u003cem\u003eRuminococcus\u003c/em\u003e had positive correlation with hepatic metabolites. and another cluster had negative correlation with these hepatic metabolites, which included bacteria \u003cem\u003eCorynebacterium stationis\u003c/em\u003e, \u003cem\u003eLactobacillales\u003c/em\u003e, \u003cem\u003eClostridium celatum\u003c/em\u003e, Veillonellaceae, \u003cem\u003eunidentified rc4-4\u003c/em\u003e, Pasteurellaceae, \u003cem\u003eTuricibacter\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, Peptostreptococcaceae, Enterobacteriaceae, \u003cem\u003eVeillonella parvula\u003c/em\u003e, \u003cem\u003eBarnesiella intestinihominis\u003c/em\u003e, Streptococcaceae, \u003cem\u003ePrevotella\u003c/em\u003e, and \u003cem\u003eSutterella\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). The liver-derived metabolites D-proline, SDMA, spermidine, hexanoylcarnitine, proline, Ala-Leu, ADTF, OIELTD, prolylleucine, and 1-methylxanthine were the top 10 metabolites that were significantly correlated with the top 5 colon-derived metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). As mentioned above, the most enriched cometabolism pathway was aminoacyl-tRNA biosynthesis. Afterwards, all the microbiota that participated in liver metabolites production were drawn by the BIO-Sankey network. As shown, the phylum Euryarchaeota and Candidatus Bathyarchaeota were associated with the production of \u003cem\u003eO\u003c/em\u003e-phospho-\u003cem\u003eL\u003c/em\u003e-serine, demonstrating that they were significantly downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). Furthermore, an STA-Sankey network was drawn to reveal the microbial taxonomy accounting for metabolic changes. As shown, the enteric microorganism had a critical role in aminoacyl-tRNA biosynthesis, which included upregulated genus \u003cem\u003eCoprococcus\u003c/em\u003e downregulated genus \u003cem\u003eVeillonellaceae\u003c/em\u003e, \u003cem\u003eunidentified Peptostreptococcaceae\u003c/em\u003e, \u003cem\u003eunidentified Enterobacteriaceae\u003c/em\u003e, and \u003cem\u003eCorynebacterium\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003eThere were fewer metabolites derived from the colon had significantly altered with TSS than from the liver (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea), and ethylbenzene degradation was the only metabolic pathway enriched in the microbiota (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb). Moreover, the colon-derived metabolites N-methylisoleucine, 5-hydroxytryptophan, PMP, acetophenone and 15-HETE were positively correlated with microbe \u003cem\u003eDelftia\u003c/em\u003e, \u003cem\u003ecoprococcus\u003c/em\u003e, \u003cem\u003eMorganella\u003c/em\u003e, \u003cem\u003eCorynebacterium stationis\u003c/em\u003e, \u003cem\u003eChristensenella\u003c/em\u003e, \u003cem\u003eDesulfovibrio\u003c/em\u003e, \u003cem\u003eFlexispira\u003c/em\u003e, \u003cem\u003eElusimicrobium\u003c/em\u003e, \u003cem\u003eCoriobacteriaceae\u003c/em\u003e, \u003cem\u003eRikenellaceae\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eLactobacillus vaginalis\u003c/em\u003e, and \u003cem\u003eunidentified F16\u003c/em\u003e, and were negatively correlated with \u003cem\u003eStreptococcaceae\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eSutterella\u003c/em\u003e, \u003cem\u003eLactobacillales\u003c/em\u003e, \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, and \u003cem\u003ePeptostreptococcaceae\u003c/em\u003e. However, another group of metabolites, namely dihydrothymine, PNK, BEP, 10-hydroxydecanoic acid, 4-MCD and TTPD, had opposite correlations with the microbiota (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ec). Furthermore, according to the Sankey network (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ed, e), the abundance of significant downregulated microbes Streptococcaceae, \u003cem\u003eConchiformibius\u003c/em\u003e, \u003cem\u003eSutterella\u003c/em\u003e, and Enterobacteriaceae were negatively correlated with the abundance of acetophenone, which is involved in ethylbenzene degradation whereas the microbes \u003cem\u003eJeotgallcoccus\u003c/em\u003e and \u003cem\u003eRuminococcus\u003c/em\u003e, which were significantly upregulated, were positively correlated with acetophenone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen intestinal microbes were disrupted by vancomycin, the cometabolic pathways and correlation maps were predominantly altered (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ea). Notably, the number of liver metabolites as well as enriched metabolic pathways (Fig. b) were obviously increased which were originally from both the microbiota and host-microbe cometabolism (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ea). And, the most effective pathway was the cometabolism of purines. Moreover, compared with TSS treatment, vancomycin administration changed greater numbers of liver metabolites that related to enteric microorganisms (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ec). Furthermore, we observed that the abundances of the genera \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eunidentified Comamonadaceae\u003c/em\u003e, and \u003cem\u003eunidentified Xanthomonadaceae\u003c/em\u003e had significantly decreased and were positively correlated with the downregulated adenosine 5-monophosphate which was involved in purine cometabolism pathway (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ed, e). Moreover, when considering vancomycin treatment, the number of colon metabolites originating from the microbiota, host, and cometabolism was largely increased (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ea), as well as the number of significantly enriched metabolic pathways (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eb). Moreover, the total number of metabolites associated with microflora abatement increased markedly (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ec). Finally, analysis of the cometabolic pentose phosphate pathway demonstrated that the significantly increasing metabolite D-ribulose 5-phosphate was positively related to a significant increase in the abundance of the bacterial genus \u003cem\u003eunidentified Burkholderiales\u003c/em\u003e and a decrease in the abundance of \u003cem\u003eunidentified Sinobacteraceae\u003c/em\u003e (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMicrogravity exposure induced lipid deposition, oxidative stress and inflammation in the liver\u003c/h2\u003e \u003cp\u003eMicrogravity exposure syndrome rats established by tail suspension to the extent of hindlimb unloading can replicate the physiological effects that occur after long-term microgravity exposure; therefore, these rats have become one of the main animal models mimicking the physiological effects resulting from microgravity exposure during spaceflight[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In the present study, simulated microgravity exposure expanded the interstitial space of liver tissue and significantly increased the level of lipid accumulation, which was intuitively reflected by H\u0026amp;E and oil red O staining of pathological tissue sections (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and b). Furthermore, lipid deposition was verified by increased concentrations of total cholesterol (TC) and triglycerides (TGs) in liver tissue homogenates. Although the serum AST and ALT levels, which are indicators of liver injury, were not significantly increased by TSS treatment, the levels of oxidative stress-related markers (MDA) and inflammatory factors (IL-6) in liver tissue homogenates were markedly increased, which might reflect the increase in oxidative stress and inflammation in the liver resulting from long-term microgravity exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei-n). To explore the pivotal role of gut microbes in reversing the effects of microgravity exposure, an antibiotic usually used for gut microbial elimination, vancomycin, was selected as a positive control for comparison. The results indicated that a large imbalance in gut microbes could give rise to more severe lipid deposition and higher levels of oxidative stress as well as inflammation, indicating that enteric microorganisms markedly participate in the formation of liver lesions during long-term microgravity exposure.\u003c/p\u003e \u003cp\u003eFurthermore, metabolic profiles from liver tissue homogenates also reflected the proinflammatory effects of long-term microgravity exposure. Notably, bioactive lipids, such as 13,14-dihydro-15-keto-tetranor Prostaglandin D2, Prostaglandin B1, Prostaglandin E2, 2,3-Dinor-11β-prostaglandin F2α, bicyclo prostaglandin E2, 15-deoxy-Δ12,14-prostaglandin A1,6-kketoprostaglandin F1α, prostaglandin J2, prostaglandin D2 and prostaglandin A2, were simultaneously raised in liver homogenates from TSS-treated rats compared with those from control rats. As documented by previous reports, these bioactive lipids have pronounced effects on ameliorating or aggravating oxidation, apoptosis and inflammation. These results indicated that liver lipid metabolic disturbances resulting from microgravity exposure via TSS treatment might enhance the formation of proinflammatory products such as PGE2 and trigger inflammation and its consequences.\u003c/p\u003e \u003cp\u003eTaken together, these findings indicated that TSS treatment could promote the accumulation of lipids and trigger oxidative stress and inflammation in the liver, which are similar to previous spaceflight mice who has also been observed elevated fat accumulation in the liver[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and induced liver injury and inflammation associated with apoptosis and oxidative stress[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Herein, complementary to the abovementioned mechanisms, we propose an array of metabolic disorders that involve mainly PGE2-based lipid metabolism and tryptophan metabolism and may account for liver inflammation and damage during microgravity exposure. Although the duration of 30-day tail suspension is too short for liver injury to develop, the increases in markers of oxidative stress and inflammation have raised the concern that longer microgravity exposure to the space environment may result in progressive liver damage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSimulated microgravity damaged the gut microflora and the intestinal epithelium\u003c/h2\u003e \u003cp\u003eUsing a TSS rat model, we observed that microgravity exposure resulted in mild alterations in the gut microflora, manifested by an increase in opportunistic pathogens, such as \u003cem\u003eShigella\u003c/em\u003e and \u003cem\u003eDesulfovibrio\u003c/em\u003e, and a decrease in \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The intestinal mucosa is organized into the intestinal epithelium, lamina propria, muscle and so on[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Colon H\u0026amp;E staining revealed broken intestinal mucosa in certain niches from TSS-treated SD rats. Additionally, from these images, we observed irregular surface evenness, a sparse filamentous plexus, and narrow muscularis mucosa (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Coincidentally, damage to the defense of the intestinal barrier was further confirmed by increased absorption of LPS and D-Lac as well as inadequate response activities, for example, insufficient secretion of LBP, CALP and DAO (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). In addition to microbiota changes and structural depletion, changes in functionality were inferred by alterations in liver mammalian-microbial cometabolite levels. For example, TSS treatment caused a significant decrease in the liver-derived metabolites glycolithocholic acid and glycoursodeoxycholic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), which are conjugated secondary bile acids with glycine, and agonists to the nuclear receptor farnesoid X receptor (FXR)-a bile acid receptor. These reductions have been proven to contribute to the low activity of FXR and impaired intestinal barrier function. Finally, liver metabolites related to tryptophan metabolism, for example, tryptamine, kynurenic acid, xanthurenic acid, \u003cem\u003eL\u003c/em\u003e-kynurenine, and indole-3-acetic acid, were markedly increased (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), among which \u003cem\u003eL\u003c/em\u003e-kynurenine and indole-3-acetic acid are important for the intestinal immunological barrier via their roles in the regulation of immunity and inflammation. In addition, the concentration of 5-hydroxytryptophan in the colon coordinately increased (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), which may self-adjust barrier homeostasis. Overall, microgravity exposure gave rise to gut microbiota shifts and colon barrier damage in a hindlimb unloading model.\u003c/p\u003e \u003cp\u003eThe microbial communities residing in the mammalian gut lumen play pivotal roles in regulating intestinal barrier function, and alterations in microbiome composition and function have been linked to impaired defense of the intestinal barrier at different levels[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The NASA twins\u0026rsquo; study revealed that the gastrointestinal (GI) microbiota from inflight faces had no significant differences in terms of richness or the Shannon index relative to that of preflight and postflight samples, and the metabolite 3-indole propionic acid, which has beneficial effects on barrier integrity, was observed at lower levels in abording astronauts throughout the duration of the study[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In contrast, other intestinal microflora in inflight faces from astronauts aboard Salyut/Soyuz and Mir were shown to be infected by increasing opportunistic pathogens[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Compensatory for these results, we demonstrated that the microbiota and biomarkers related to intestinal barrier damage were regulated by abnormalities in TSS-treated rats (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), especially for intestinal permeability, as an expression of gut barrier disruption was established by increased absorption of LPS and \u003cem\u003eD\u003c/em\u003e-Lac. In summary, these results give rise to prevent the disruption of intestinal barrier function during future space travel.\u003c/p\u003e \u003cp\u003eMicrobiota abnormalities resulting from microgravity exposure likely account for four physiological pathways. First, microgravity exposure changes the cellular activity of genes in molecular pathways, which may impact the production of secondary metabolites and influence microbial constitution[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Second, microgravity exposure alters liver activity, which in particular induces changes in bile acid metabolism and synthesis[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], shaping the intestinal microbial phenotype. Third, microgravity exposure can change the differentiation and proliferation of intestinal stem cells[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], thus impairing the integrity of the intestinal mucosa. Finally, microgravity exposure causes changes in the immune response, thereby challenging the growth of pathogens residing in the intestinal mucosa[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Interestingly, we observed changes in bile acid metabolism and intestinal barrier injury in rats after 30 days of microgravity exposure, which might explain the mechanism of intestinal flora disturbance during long-term spaceflight.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLiver insufficiency is associated with gut-liver axis disruption upon microgravity\u003c/h2\u003e \u003cp\u003eCurrent data demonstrate that microbiota abnormalities can impair the intestinal barrier, facilitating the portal influx of microbe-derived metabolites, such as trimethylamine, LPS and secondary bile acids, ultimately worsening inflammation and metabolic abnormalities. Moreover, the host shapes the gut microbiome through the hepatoenteric circulation of bile acids and the regulation of antibody secretion, maintaining gut barrier homeostasis via FXR signaling in the intestinal epithelium[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Thus, the microbiota and intestinal mucosa are interrelated, and both are connected to the host through bile and portal blood.\u003c/p\u003e \u003cp\u003eAs displayed by correlation networks between metabolites derived from the liver and colon (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb and e) as well as associating maps among liver metabolites and fecal microbiota (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ec), liver metabolites that changed with TSS treatment were strongly correlated with colon metabolites as well as the residing microbiome. The correlation maps were further interpreted by cometabolic pathway analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ed and e), which revealed that gut microbes play important roles in the synthesis and transformation of amino acids in the liver and in vitamin metabolism. Furthermore, those effects of the gut microbiota on metabolic flux from gut-liver axis were further verified by depleting the gut microbes via vancomycin administration (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Moreover, liver metabolites from the portal influx of microbial products significantly changed their relative abundance (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), especially for increased N-phenylacetylglutamine and decreased secondary bile acids (glycoursodeoxycholic acid and glycolithocholic acid).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong these microbially derived metabolites, N-phenylacetylglutamine, which impairs the firing rate and induces axonal damage in cultured neurons[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], is highly expressed in the sepsis group[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and is elevated in concentrations associated with phenylketonuria[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In addition, conjugated secondary bile acids (BAs), glycoursodeoxycholic acid (glyco-UDCA) and glycolithocholic acid (glyo-LCA) are reduced along with the microbial genera \u003cem\u003eLactobacillus\u003c/em\u003e (Firmicutes) and \u003cem\u003ePrevotella\u003c/em\u003e (Bacteroidetes), in which deconjugated UDCA is oxidized and epimerized of bile acids by Actinobacteria, Proteobacteria, Firmicutes and Bacteroidetes[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and LCA is involved in dihydroxylation of bile acids by Firmicutes (Clostridium and Extibacter spp.)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Both UDCA and LCA can bind to both FXR and G protein-coupled receptors (TGR5)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] which exhibit many beneficial effects, including accelerating bile acid enterohepatic circulation[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], treating cholestatic liver diseases[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], exerting cytoprotective and anti-inflammatory actions, and preventing colorectal adenoma recurrence\u003csup\u003e28\u003c/sup\u003e. Specifically, treatment with UDCA can dampen mucosal inflammatory responses, prevent epithelial apoptosis, promote restitution, and restore a more \u0026ldquo;normal\u0026rdquo; microbial composition[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Thus, the alterations in the metabolites mentioned above suggested that microgravity exposure may affect the production of microbial secondary metabolites which further cause insufficient liver activity. In actual spaceflight and animal models, the liver was regarded as an early sensor and frequent target of microgravity exposure and gradually developed into disturbances of hepatic homeostasis which resulted in liver injury and inflammation. Otherwise, this liver inflammation was accompanied by cell apoptosis and oxidative stress, compromised carbohydrate metabolism, accumulated lipid droplets in the liver which ultimately alter hepatic biotransformation capacity[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, proteomics analysis of liver tissue from mice aboard a biosatellite for 30 days revealed regeneration of bile acid secretion as well as reconstitution of transporter proteins and CYP enzymes[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, we proposed another mechanism accounting for liver insufficiency, which is well defined by gut-liver axis disruption, further compensating for the mechanism of liver damage during space travel.\u003c/p\u003e \u003cp\u003eOverall, microgravity exposure simulated in a hindlimb animal model can alter the portal influx of microbial secondary metabolites in two ways. On the one hand, microgravity elevated the influx of harmful metabolites. On the other hand, it reduced the population of secondary BAs, which might account for microbiota abnormities, increasing intestinal permeability, and subsequently give rise to liver insufficiency in metabolism.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCometabolism pathways of the microbiota with its host contribute to hepatic synthesis and metabolism of amino acids upon microgravity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe liver is a highly active metabolic organ that plays vital roles in the biosynthesis and metabolism of carbohydrates, lipids, proteins, vitamins and so on. Inherent to its unique location and function in the body, the liver is generally an early sensor and frequent target of stress from microgravity exposure[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To explore the roles of the microbiota in host metabolism, we constructed Sankey networks among liver metabolites and fecal microbes. The pathway enrichment results (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e) revealed that the metabolic pathways associated with TSS treatment were: aminoacyl-tRNA biosynthesis; vitamin B6 metabolism; alanine, aspartate, and glutamate metabolism; glutathione metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; D-glutamine and D-glutamate metabolism; glycine, serine, and threonine metabolism; biosynthesis of siderophore group nonribosomal peptides; tryptophan metabolism; taurine and hypotaurine metabolism; glyoxylate and dicarboxylate metabolism; citrate cycle (TCA cycle); cysteine and methionine metabolism; arginine biosynthesis; valine, leucine, isoleucine biosynthesis; arginine and proline metabolism; histidine metabolism; and lysine biosynthesis. Moreover, the fecal microbes exhibited obvious correlations with liver metabolites. In view, the enriched pathways indicated the impact of microgravity exposure on hepatic synthesis and metabolism of amino acids. Interestingly, several specific metabolic pathways, including tryptophan metabolism, valine, leucine, isoleucine metabolism, glycine, serine, threonine metabolism, glutathione metabolism, glycerophospholipid metabolism, and tricarboxylic acid (citric acid) cycle metabolism, are dysregulated in liver fibrosis[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In the largest cohort of astronauts, spatial data flown revealed that hepatic fluxes related to these fibrosis-related pathways were significantly increased[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore, it is reasonable to speculate that dysregulation in microbial cometabolism of amino acids with its host aggravated the liver lipid deposition as well as upregulated fibrosis-related pathway. Additionally, we also discovered abnormities in amino acid biosynthesis and vitamin B6 metabolism. Vitamin B6 is a coenzyme involved in the metabolism of amino acids and amino acid biosynthesis, and vitamin B6 metabolism might contribute to protein loss in astronauts[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and immune system weakening[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Hence, microbial homeostasis is critical for maintaining hepatic synthesis and metabolism of amino acids upon microgravity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe liver and gut, as major metabolic hubs of the human body, play vital roles in health maintenance and performance premotion for astronauts, but known little because of enormous limitations in researches due to the unavailability of organic samples and ethical reasons. In this work, we highlighted gut-liver axis disturbances upon microgravity exposure via a well-established hindlimb unloading rat model. Overall, microgravity exposure can induce lipid deposition, oxidative stress and inflammation in the liver and increase the proportion of opportunistic pathogens, following by intestinal barrier function damage. Consequently, these abnormalities result in marked dysregulation of enterohepatic metabolic communication associated with liver insufficiencies. These results provide insight into the health effects of gut-liver axis hemostasis on long-term human-crewed space missions and raise new targets to maintain and improve health as well as performance for astronaut inflight.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eH\u0026amp;E\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehematoxylin-eosin staining\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eELISA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnzyme-linked immunosorbent assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriglyceride\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealanine transaminase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003easpartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emalondialdehyde\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSH-Px\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglutathione peroxidase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esuperoxide dismutase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elipopolysaccharide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elipopolysaccharide binding protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eD-Lac\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eD-lactate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediamine oxidase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSIGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esecretory immunoglobulin A\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCALP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecalprotectin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echromograninA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOTUs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoperational taxonomic units\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eO\u003csub\u003e2\u003c/sub\u003ePLS-DA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eorthogonal signal correction-orthogonal partial least squares-discriminant analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epredictive variable importance in the projection from O\u003csub\u003e2\u003c/sub\u003ePLS-DA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efold change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMPEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetabolite pathway enrichment analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPGA1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e15-deoxy-Δ 12,14-prostaglandin A1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTBDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etrans-2-butene-1,4-dicarboxylic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethiazolidine-4-carboxylic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e7-hydroxy-3,4-dihydrocarbostyril\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPCBA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN1-(1,3-diphenyl-1H-pyrazol-5-yl)-2-chlorobenzamide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e(+/-)-CP 47,497-C7-hydroxy metabolite\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHNPP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e3-hydroxy-2-(3-nitro-4-piperidinobenzyl)propanenitrile\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMBA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e2-(acetylamino)-4-(methylthio)butanoic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTYMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e2-[(1H-1,2,4-triazol-3-ylimino)methyl]phenol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBAMC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etert-butyl N-[1-(aminocarbonyl)-3-methylbutyl]carbamate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHMMMNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN-[(4-hydroxy-3-methoxyphenyl)methyl]-8-methylnonanamide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFIPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e2-furyl[4-(1H-indol-4-yl)piperazino]methanone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e5-HT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eb5-Hydroxytryptophan\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e1-Phenyl-3-methyl-5-pyrazolone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e5-amino-1-phenyl-1H-pyrazole-4-carbonitrile\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e3-(2-methylpropyl)-octahydropyrrolo[1,2-a]pyrazine-1,4-dione\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTTPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e1,3,7-trimethyl-2,3,6,7-tetrahydro-1H-purine-2,6-dione\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBEP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e2-(1,3-benzodioxol-5-yl)-7-ethylimidazo[1,2-a]pyridine. APPC:5-amino-1-phenyl-1H-pyrazole-4-carbonitrile\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePMP, 1-phenyl-3-methyl-5-pyrazolone\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e4-MCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e4-methoxycinnamaldehyde\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTTPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e1,3,7-trimethyl-2,3,6,7-tetrahydro-1H-purine-2,6-dione\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOIELTD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e4-[2-(2-oxo-1-imidazolidinyl)ethyl]-1lambda\u0026thinsp;~\u0026thinsp;6,4-thiazinane-1,1-dione\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADTF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e3-acetyl-2,5-dimethylfuran\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN3,N4-dimethyl-L-arginine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPGA1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e15-deoxy-Δ 12,14-prostaglandin A1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e4-(4-cyclohexylphenyl)-4-oxobut-2-enoic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e(2R)-2,3-dihydroxypropanoic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN3,N4-Dimethyl-L-arginine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFIPHA, 2-furyl[4-(1H-indol-4-yl)piperazino]methanone\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOIELTD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e4-[2-(2-oxo-1-imidazolidinyl)ethyl]-1lambda\u0026thinsp;~\u0026thinsp;6~,4-thiazinane-1,1-dione\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePro-Hyp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProline-hydroxyproline\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTBAMBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etert-Butyl N-[1-(aminocarbonyl)-3-methylbutyl]carbamate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGluAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e(5-L-Glutamyl)-L-Amino Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPPCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e3-amino-2-phenyl-2H-pyrazolo[4,3-c]pyridine-4,6-diol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e4-MCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e4-Methoxycinnamaldehyde\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN-Phenylacetylglutamine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPGA1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e15-Deoxy-Δ 12,14-prostaglandin A1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADTF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e3-Acetyl-2,5-dimethylfuran\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTestosterone undecanoate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAAPAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e2-(acetylamino)-3-[4-(acetylamino)phenyl]acrylic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDKTPGD2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e13,14-dihydro-15-keto-tetranor Prostaglandin D2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e3-amino-1H-pyrazolo[4,3-c]pyridine-4,6-diol.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the procedures involved in our animal studies were performed in accordance with the Guidelines in the Care and Use of Animals and approved by the China Astronaut Research and Training Center Animal Welfare Committee (Permit Number: ACC-LACUC-2021-014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequence files for all samples used in this study have been deposited in the Genome Sequence Archive (GSA) (https://ngdc.cncb.ac.cn/gsub/submit/gsa/list) with serial number subCRA025472. And raw MZ data for all samples used in this study have been deposited in the MetaboLights (https://www.ebi.ac.uk/metabolights/editor/console) with unique identifier MTBLS9924. The metadata, microbial sequencing data, and ELISA data have been included in Additional files 3, 4 and 5, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential\u0026nbsp;conflicts\u0026nbsp;of interest\u0026nbsp;were\u0026nbsp;reported by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePu Chen implemented the experiment, created the Graphical Abstract,\u0026nbsp;analyzed\u0026nbsp;and interpreted the data and wrote and edited the manuscript. Junli Chen participated in the experiment and collected the samples. Nan Xu conducted the H\u0026amp;E staining, generated the figures, and edited the manuscript. Weiran Wang created the correlation network, generated the figures, and edited the manuscript. Lingwei Hou\u0026nbsp;analyzed the\u0026nbsp;cometabolomic pathways, generated\u0026nbsp;the figures, and edited the manuscript. Bowen Sun\u0026nbsp;analyzed\u0026nbsp;the metabolomic flux, generated the figures, and edited the manuscript. Haiyun Lan\u0026nbsp;analyzed\u0026nbsp;and interpreted the microbiome data and edited the manuscript. Wei Liu conducted the experiments and edited the manuscript. Qibing Shen\u0026nbsp;analyzed\u0026nbsp;the metabolic data, interpreted the data, and generated the figures. Yanbo Yu participated in the exchange of ideas, created the figures, and edited the manuscript. Peng Zang supervised the project and paper,\u0026nbsp;analyzed\u0026nbsp;and interpreted the data and summary results, and wrote and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the State Key Lab of Space Medicine Fundamentals and Application, Astronaut Center of China (SMFA19B04) and Science and Technology Planning Project of Shenzen Municipality (JCYJ20180507182854651).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGarrett-Bakelman FE, Darshi M, Green SJ, Gur RC, Lin L, Macias BR, McKenna MJ, Meydan C, Mishra T, Nasrini J et al. The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight. \u003cem\u003eScience\u003c/em\u003e 2019, 364(6436).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIlyin VK. Microbiological status of cosmonauts during orbital spaceflights on Salyut and Mir orbital stations. Acta Astronaut. 2005;56(9\u0026ndash;12):839\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJonscher KR, Alfonso-Garcia A, Suhalim JL, Orlicky DJ, Potma EO, Ferguson VL, Bouxsein ML, Bateman TA, Stodieck LS, Levi M, et al. Spaceflight Activates Lipotoxic Pathways in Mouse Liver. PLoS ONE. 2016;11(4):e0152877.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesei D, Jewczynko A, Lynch AM, Urbaniak C. Understanding the Complexities and Changes of the Astronaut Microbiome for Successful Long-Duration Space Missions. Life (Basel) 2022, 12(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBedree JK, Kerns K, Chen T, Lima BP, Liu G, Ha P, Shi J, Pan HC, Kim JK, Tran L et al. Specific host metabolite and gut microbiome alterations are associated with bone loss during spaceflight. Cell Rep 2023:112299.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang B, Li D-G, Huang Y, Liu C-T. Effects of spaceflight and simulated microgravity on microbial growth and secondary metabolism. Military Med Res. 2018;5(1):18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbillos A, de Gottardi A, Rescigno M. The gut-liver axis in liver disease: Pathophysiological basis for therapy. J Hepatol. 2020;72(3):558\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilosevic I, Vujovic A, Barac A, Djelic M, Korac M, Radovanovic Spurnic A, Gmizic I, Stevanovic O, Djordjevic V, Lekic N et al. Gut-Liver Axis, Gut Microbiota, and Its Modulation in the Management of Liver Diseases: A Review of the Literature. Int J Mol Sci 2019, 20(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins SL, Stine JG, Bisanz JE, Okafor CD, Patterson AD. Bile acids and the gut microbiota: metabolic interactions and impacts on disease. Nat Rev Microbiol. 2023;21(4):236\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorey-Holton E, Globus RK, Kaplansky A, Durnova G. The hindlimb unloading rat model: literature overview, technique update and comparison with space flight data. Adv Space Biol Med. 2005;10:7\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeldman AT, Wolfe D. Tissue processing and hematoxylin and eosin staining. Methods Mol Biol. 2014;1180:31\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehlem A, Hagberg CE, Muhl L, Eriksson U, Falkevall A. Imaging of neutral lipids by oil red O for analyzing the metabolic status in health and disease. Nat Protoc. 2013;8(6):1149\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang B, Jiang X, Cao M, Ge J, Bao Q, Tang L, Chen Y, Li L. Altered Fecal Microbiota Correlates with Liver Biochemistry in Nonobese Patients with Non-alcoholic Fatty Liver Disease. Sci Rep. 2016;6:32002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pe\u0026ntilde;a AG, Goodrich JK, Gordon JI, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWant EJ, Masson P, Michopoulos F, Wilson ID, Theodoridis G, Plumb RS, Shockcor J, Loftus N, Holmes E, Nicholson JK. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc. 2012;8(1):17\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan M, Breitkopf SB, Yang X, Asara JM. A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat Protoc. 2012;7(5):872\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen B, Mei Z, Zeng C, Liu S. metaX: a flexible and comprehensive software for processing metabolomics data. BMC Bioinformatics. 2017;18(1):183.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVinken M. Hepatology in space: Effects of spaceflight and simulated microgravity on the liver. Liver Int. 2022;42(12):2599\u0026ndash;606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eda Silveira WA, Fazelinia H, Rosenthal SB, Laiakis EC, Kim MS, Meydan C, Kidane Y, Rathi KS, Smith SM, Stear B, et al. Comprehensive Multi-omics Analysis Reveals Mitochondrial Stress as a Central Biological Hub for Spaceflight Impact. Cell. 2020;183(5):1185\u0026ndash;e12011120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGehart H, Clevers H. Tales from the crypt: new insights into intestinal stem cells. Nat Reviews Gastroenterol Hepatol. 2019;16(1):19\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeibovitzh H, Lee S-H, Xue M, Garay JAR, Hernandez-Rocha C, Madsen KL, Meddings JB, Guttman DS, Espin-Garcia O, Smith MI. Altered gut microbiome composition and function are associated with gut barrier dysfunction in healthy relatives of patients with Crohn\u0026rsquo;s disease. Gastroenterology. 2022;163(5):1364\u0026ndash;76. e1310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma G, Curtis PD. The impacts of microgravity on bacterial metabolism. Life. 2022;12(6):774.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnselm V, Novikova S, Zgoda V. Re-adaption on earth after spaceflights affects the mouse liver proteome. Int J Mol Sci. 2017;18(8):1763.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTopal U, Zamur C. Microgravity, stem cells, and cancer: A new Hope for cancer treatment. \u003cem\u003eStem Cells International\u003c/em\u003e 2021, 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrimm D, Wehland M, Corydon TJ, Richter P, Prasad B, Bauer J, Egli M, Kopp S, Lebert M, Kr\u0026uuml;ger M. The effects of microgravity on differentiation and cell growth in stem cells and cancer stem cells. Stem cells translational Med. 2020;9(8):882\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiddiqui R, Akbar N, Khan NA. Gut microbiome and human health under the space environment. J Appl Microbiol. 2021;130(1):14\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePabst O, Hornef MW, Schaap FG, Cerovic V, Clavel T, Bruns T. Gut\u0026ndash;liver axis: barriers and functional circuits. Nat Reviews Gastroenterol Hepatol 2023:1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNtranos A, Park H-J, Wentling M, Tolstikov V, Amatruda M, Inbar B, Kim-Schulze S, Frazier C, Button J, Kiebish MA. Bacterial neurotoxic metabolites in multiple sclerosis cerebrospinal fluid and plasma. Brain. 2022;145(2):569\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Wang C, Chen M. Metabolomics-based study of potential biomarkers of sepsis. Sci Rep. 2023;13(1):585.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWild J, Shanmuganathan M, Hayashi M, Potter M, Britz-McKibbin P. Metabolomics for improved treatment monitoring of phenylketonuria: urinary biomarkers for non-invasive assessment of dietary adherence and nutritional deficiencies. Analyst. 2019;144(22):6595\u0026ndash;608.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFunabashi M, Grove TL, Wang M, Varma Y, McFadden ME, Brown LC, Guo C, Higginbottom S, Almo SC, Fischbach MA. A metabolic pathway for bile acid dehydroxylation by the gut microbiome. Nature. 2020;582(7813):566\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuzior DV, Quinn RA. Microbial transformations of human bile acids. Microbiome. 2021;9(1):1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiriyama Y, Nochi H. Physiological role of bile acids modified by the gut microbiome. Microorganisms. 2022;10(1):68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Jiang R, Zheng X, Lei S, Huang F, Xie G, Kwee S, Yu H, Farrar C, Sun B. Ursodeoxycholic acid accelerates bile acid enterohepatic circulation. Br J Pharmacol. 2019;176(16):2848\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolinaro A, Marschall H-U. Bile acid metabolism and FXR-mediated effects in human cholestatic liver disorders. Biochem Soc Trans. 2022;50(1):361\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasegawa S, Yoneda M, Kurita Y, Nogami A, Honda Y, Hosono K, Nakajima A. Cholestatic liver disease: current treatment strategies and new therapeutic agents. Drugs. 2021;81(10):1181\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWard JB, Lajczak NK, Kelly OB, O\u0026rsquo;Dwyer AM, Giddam AK, N\u0026iacute; Gabhann J, Franco P, Tambuwala MM, Jefferies CA, Keely S. Ursodeoxycholic acid and lithocholic acid exert anti-inflammatory actions in the colon. Am J Physiology-Gastrointestinal Liver Physiol. 2017;312(6):G550\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLajczak-McGinley NK, Porru E, Fallon CM, Smyth J, Curley C, McCarron PA, Tambuwala MM, Roda A, Keely SJ. The secondary bile acids, ursodeoxycholic acid and lithocholic acid, protect against intestinal inflammation by inhibition of epithelial apoptosis. Physiological Rep. 2020;8(12):e14456.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang M-L, Yang S-S. Metabolic signature of hepatic fibrosis: from individual pathways to systems biology. Cells. 2019;8(11):1423.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfshinnekoo E, Scott RT, MacKay MJ, Pariset E, Cekanaviciute E, Barker R, Gilroy S, Hassane D, Smith SM, Zwart SR. Fundamental biological features of spaceflight: advancing the field to enable deep-space exploration. Cell. 2020;183(5):1162\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeer M, Titze J, Smith SM, Baecker N. Nutrition physiology and metabolism in spaceflight and analog studies. In.: Springer; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi L, Tian H, Wang P, Li L, Zhang Z, Zhang J, Zhao Y. Spaceflight and simulated microgravity suppresses macrophage development via altered RAS/ERK/NFκB and metabolic pathways. Cell Mol Immunol. 2021;18(6):1489\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSupplementary. tables.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Microgravity exposure, gut-liver axis, intestinal barrier, hindlimb suspension rat model","lastPublishedDoi":"10.21203/rs.3.rs-4190281/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4190281/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background: Recent advances in understanding gut‒liver axis homeostasis have been made because of the promising beneficial effects of these systems on health maintenance and performance promotion. However, little is known about the effects of long-term microgravity exposure on the gut-liver axis or about effective countermeasures to prevent disruptions in gut-liver axis homeostasis. Hence, we conducted a well-controlled study to determine the effects of long-term microgravity exposure on liver activity, the gut microbiota and gut-liver axis homeostasis via a hindlimb suspension rat model.\nResults: Interestingly, long-term microgravity exposure increased lipid deposition, oxidative stress and inflammation in the liver; increased proportions of opportunistic enteric pathogens; and disrupted intestinal barrier integrity, paralleling with dysregulation of gut-liver axis homeostasis, which especially underlined portal influx of secondary bile acid (mainly ursodeoxycholic acid and lithocholic acid). Notably, metabolites (mostly prostaglandins, kynurenine and derivatives) derived from the liver reflected the aggravating oxidative stress and inflammation and were strongly associated with those from the colon. In addition, the gut microbiota played a vital role in cometabolism pathways of aminoacyl-tRNA biosynthesis, vitamin B6 metabolism, alanine, and aspartate and glutamate metabolism, which may emphasize the critical role of microbial homeostasis in maintaining liver activities as well as intestinal barrier integrity upon microgravity.\nConclusions: Taken together, our findings suggest that enteric microorganism is an effective target for maintaining gut-liver axis homeostasis as well as protecting astronauts from inflammation when deal with microgravity exposure in further long-term manned space mission.","manuscriptTitle":"Effects of Long-term Microgravity Exposure on Liver Activity and the Gut Microbiota as well as Gut-liver Axis Homeostasis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-22 08:33:37","doi":"10.21203/rs.3.rs-4190281/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-15T10:50:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-15T09:29:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T18:38:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211827055249907755261262684548461208910","date":"2025-06-30T06:37:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251154688136092981484774302358441814414","date":"2025-06-24T15:52:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-24T08:12:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116392985055344949270239719767364784551","date":"2025-06-05T07:07:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62412204224755771234243904372636935230","date":"2024-09-27T02:01:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-25T01:44:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-22T07:52:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-22T07:50:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-13T07:47:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2024-03-30T02:41:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"31293fd7-9e4b-4385-a498-8c4b7734e869","owner":[],"postedDate":"May 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:05:41+00:00","versionOfRecord":{"articleIdentity":"rs-4190281","link":"https://doi.org/10.1186/s12866-025-04616-x","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2026-02-11 15:59:16","publishedOnDateReadable":"February 11th, 2026"},"versionCreatedAt":"2024-05-22 08:33:37","video":"","vorDoi":"10.1186/s12866-025-04616-x","vorDoiUrl":"https://doi.org/10.1186/s12866-025-04616-x","workflowStages":[]},"version":"v1","identity":"rs-4190281","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4190281","identity":"rs-4190281","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}