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

doi:10.21203/rs.3.rs-4190281/v1

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

https://doi.org/10.21203/rs.3.rs-4190281/v1

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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

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.

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).

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. 2a 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. 2b 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. 2e), and total triglyceride (TG) was significantly greater in all TSS-related treatment groups (including the TSS, GSW + TSS, and Van + TSS groups) (Fig. 2f). 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. 2g, 2h). Oxidative-related factors in the liver were subsequently evaluated, including malondialdehyde (MDA), glutathione peroxidase (GSH-Px), and superoxide dismutase (SOD). As shown in Fig. 2i, 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. 2j). In contrast, the levels of SOD in liver homogenates from group GSW + TSS were markedly lower than it from group GSW (Fig. 2k). 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. 2l). Moreover, the levels of IL-6 in group TSS, Van + TSS, and GSW + TSS were significantly greater compared with their control groups (Fig. 2m). 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. 2n).

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. 3a). 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. 3b). 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. 4a). 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. 4b).

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. 4c). 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. 4d).

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. 4e). 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. 4f).

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-MS²) 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 (O₂PLS-DA). Furthermore, vancomycin treatment induced distinct clustering which distributed along with the y-axis for liver metabolism when combined with TSS (Fig. 5a). 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. 5e). 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. 5b, f). Furthermore, the characteristic metabolites were screened by the VIPs (predictive variable importance in the projection from O₂PLS-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 Treatments¹	Tissue	Num. of Total Ident.²		Num. of Total Sig.³		Num. of Sig.Up⁴		Num. of Sig.down⁵
Compared Treatments¹	Tissue	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 Treatments¹	Tissue	Num. of Total Ident.²		Num. of Total Sig.³		Num. of Sig.Up⁴		Num. of Sig.down⁵
Compared Treatments¹	Tissue	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 O₂PLS-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
Liver	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. 6a), 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. 6d). 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. 6b). Moreover, the correlation network was dramatically altered by TSS when combined with vancomycin treatment (Fig. 6e). 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. 6c). 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. 6f).

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. 7a). 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. 7b). 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. 7c). 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. 6b). 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. 7d). 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. 7e).

There were fewer metabolites derived from the colon had significantly altered with TSS than from the liver (Fig. S2a), and ethylbenzene degradation was the only metabolic pathway enriched in the microbiota (Fig. S2b). 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. S2c). Furthermore, according to the Sankey network (Fig. S2d, 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. S3a). 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. S3a). 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. S3c). 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. S3d, e). Moreover, when considering vancomycin treatment, the number of colon metabolites originating from the microbiota, host, and cometabolism was largely increased (Fig. S4a), as well as the number of significantly enriched metabolic pathways (Fig. S4b). Moreover, the total number of metabolites associated with microflora abatement increased markedly (Fig. S4c). 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. S4d).

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. 2a 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. 2i-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. 4a). 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. 3a). 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. 3b). 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. 3d), 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. 6b and e) as well as associating maps among liver metabolites and fecal microbiota (Fig. 7c), 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. 7d 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 recurrence²⁸. 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.

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.

H&E

hematoxylin-eosin staining

ELISA

Enzyme-linked immunosorbent assay

Total cholesterol

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

O₂PLS-DA

orthogonal signal correction-orthogonal partial least squares-discriminant analysis

VIPs

predictive variable importance in the projection from O₂PLS-DA

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

(+/-)-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

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.

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).

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Supplementary. tables.

No competing interests reported.

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.

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

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Version 1

Abstract

Figures

Background

Methods

Animals and reagents

Animal modeling

H&E Staining

Oil red O staining

ELISA

16S rRNA gene Sequencing

Metabolic analysis

Results

Body weight

Microgravity exposure induced lipid deposition and inflammation within the liver

Integrality of the colonic mucosa

Dysfunction of the gut microbiota caused by microgravity exposure

Metabolic profiling on liver and colon

Metabolites derived from Liver were significantly correlated with those from colon

The impact of TSS treatment on microbial cross-talking with their host

Discussion

Microgravity exposure induced lipid deposition, oxidative stress and inflammation in the liver

Simulated microgravity damaged the gut microflora and the intestinal epithelium

Liver insufficiency is associated with gut-liver axis disruption upon microgravity

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1