Gut microbial metabolite- PE(0:0/14:0) could inhibit sepsis-induced intestinal injury

doi:10.21203/rs.3.rs-2830724/v1

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Gut microbial metabolite- PE(0:0/14:0) could inhibit sepsis-induced intestinal injury

https://doi.org/10.21203/rs.3.rs-2830724/v1

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Sepsis causes injury to the intestinal mucosa, bacterial translocation, and worsens intestinal and distant organ injury. Herein, we harvested fecal samples from the sepsis group and the healthy group. Intestinal microbiota 16sRNA sequencing of the fecal samples revealed that sepsis destroyed the imbalance in intestinal microbiota. More recently, there is a growing interest in the link between lipid metabolism and disease. Fecal metabolome analysis has identified four differentially lipid metabolized compounds: PE(O-16:0/0:0), PE(17:0/0:0), PE(0:0/14:0), and PE(12:0/20:5(5Z,8Z,11Z,14Z,17Z)). Subsequently, we found that the relative abundanceof PE(0:0/14:0) was lower in the sepsis group compared with the healthy group. In vitro and in vivoexperimentations were finally used to demonstrated that PE(0:0/14:0) treatment protected against sepsis-induced damage to the intestinal barrier. Collectively, these findings provided new insight into enhanced therapy and/or preventative measures against sepsis-induced damage to the intestinal barrier.

Intestinal microbiota

Sepsis

PE(0:0/14:0)

Intestinal barrier function

Sepsis is a severe clinical condition with high morbidity and mortality rates [1]. Sepsis disrupts the integrity of the gastrointestinal barrier, allowing bacteria and endotoxins to enter the systemic circulation [2, 3]. Entry of numerous toxins and bacteria into the bloodstream through the damaged intestinal walls causes blood infection, leading to multiple organ failure and death [4, 5]. The current conventional treatment strategies for patients with sepsis include anti-infection drugs, agents that improve organ perfusion, regulate immune response, and those that regulate intestinal microbiota disorder and early initiation of enteral nutrition; however, their therapeutic effects are not satisfactory [6, 7].

Recently, there has been growing attention to the significant contribution of the intestinal microbiota and micromicrobiota to the onset and progression of metabolic illnesses [8]. The microbiota in the gut acts as a mediator for physiological functions that contribute to the synthesis and transformation of small bioactive molecules and keep the human body and external conditions in a dynamic balance [9]. Damage to the intestinal barrier allows products and toxins produced by the intestinal microbiota or microbial organisms into the bloodstream, resulting in sepsis and multiorgan failure [10]. As a result, intestinal microecology serves an integral function in the occurrence and advancement of sepsis. The preservation of intestinal microbial homeostasis is critical for preventing sepsis-induced intestinal injury. Metabolites produced by intestinal microbiota regulate disease progression. Ursodeoxycholic acid (UDCA) is a tertiary bile acid commonly employed to treat gallstones, primary biliary cirrhosis, and primary sclerosing cholangitis [11].

Herein, the intestinal microbiota and metabolites were performed to analysis the fecal samples of sepsis patients and healthy people. Finally, we evaluated the function of metabolites on intestinal barrier through in vitro and in vivo study

2.1 Patient profiles

We collected fecal samples from healthy individuals (n=10)from the health check-up center of the hospital and sepsis patients (n=10) who sought treatment at the hospital, between July 2019 and December 2020 at the Fifth People’s Hospital associated with Fudan University. The hospital ethics committee granted the research protocol approval (ethics batch number: 2019118), and all patients provided informed consent signed by their legal representatives or themselves. The inclusion criteria for patients with sepsis were: (i) diagnosed with sepsis based on the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)[12]; (ii) between the ages of 18 and 80; and (iii) patients who were hospitalized during the first 12 hours after the onset of sepsis symptoms.Pregnancy, nursing, and active cancer were the exclusion criteria for the sepsis. The following healthy individuals were included in our analyses: (a) age and gender matched with septic patients; (b) with no abnormality in biochemical indexes, which was confirmed in the health examination.. Among the healthy individuals who were eliminated from this study were: those with (a) prior sepsis or other severe infections; (b) prior hematological malignancies or other solid tumors; and (c) complicated with inflammatory disease.

Stool samples were harvested from patients who were diagnosed with sepsis in 12 hours and were frozen and kept at -80 °C in polyethylene tubes for subsequent analysis.

2.2 Gut microbiota analysis

Fecal DNA was isolated from stool samples with a Standard DNA Extraction Kit (QIAGEN), and its quality was assessed via agarose gel electrophoresis. Following amplification and the quality assessment of the V3-V4 regions of the 16S rRNA genes, the amplicon underwent purification, then reamplification. The Illumina MiSeq platform was utilized for the V3-V4 gene amplicons sequencing [13]. Filtration was performed on the raw data, and clean tags were eliminated to acquire valid tags to prepare operational taxonomic units (OTUs), which were divided utilizing the Vsearch software (version 2.4.2) carrying a 97% sequence similarity cut-off. Using OTU sequence comparisons, a phylogenetic tree was built using the pynast (v0.1) software. A rarefied OTU table was employed to analyze the intestinal microbiota to assess its diversity and composition. A normalized OTU table and consistent depth were utilized when computing the alpha diversity indices for stool specimens. Beta diversity indices were produced by the Bray-Curtis method combined with the unweighted UniFrac distance to determine if substantial differences existed within the gut microbiota between the two cohorts. Principal component analysis (PCA) was also executed to compare gut microbiota between the groups [14].

2.3 Metabolomics analysis

60mg of each stool sample from the patients was placed in a 1.5 mL Eppendorf tube containing internal standard, with subsequent additions of ice-cold methanol and water (4:1 v/v), prior to a 5 min storage at 20°C, followed by a 2 min grinding at 60 HZ, a 10 min ultrasonication in ice water bath, and a 30 min storage at -20°C. Subsequently, the solution mixture centrifugation for 15 min at 13000 rpm, 4°C, and 300 μL of supernatant underwent drying via a freeze concentration centrifugal dryer in a brown glass vial. 400 μL of methanol and water (1:4, v/v) were introduced, prior to a 30 s vortex, and maintenance at -20°C for 2 h. The samples underwent centrifugation again for 10 min at 13000 rpm, 4°C, before 150μL of supernatants were filtered through 0.22μm microfilters, prior to transfer to LC vials, which were then maintained at -80℃ until Liquid Chromatography-Mass Spectrometry (LC-MS) analysis. QC samples were generated via combinations of aliquots from all 18 samples. A Dionex Ultimate 3000 RS UHPLC system accompanied with Q Exactive quadrupole Orbitrap mass spectrometer and a heated electrospray ionization (ESI) source (Thermo Fisher Scientific, Waltham, MA, USA) was then employed for metabolic profiling analysis under both ESI positive and negative ion modes, using an ACQUITY UPLC HSS T3 column (100 mm × 2.1mm, 1.8 μm)[15].

2.4 In vivo study

6–8-week-old C57BL/6 mice, with body weight 20-23 g, were purchased from East China Normal University's Animal Center (Shanghai, China). After a week of conditional feeding, the animals were separated into 3 categories: normal control, sepsis, and sepsis + PE(0:0/14:0). Mice in the sepsis group (n = 20) were intraperitoneally injected with 0.9% aqueous sodium chloride solution at a dosage of 0.2 mL daily for 5 days before surgery. Cecum ligation and perforation were performed on day 6, after which the previous dosing method was continued for 7 days post-surgery. Control mice (n = 20) were intraperitoneally administered with 0.2 mL 0.9% aqueous sodium chloride solution every day from 5 days before surgery and continued to the end of the experiment. mouse in the sepsis +PE(0:0/14:0) (n = 20) group were intraperitoneally injected with PE(0:0/14:0) at a dosage of 5mg/kg, 10 mg/kg and 20mg/kg respectively once daily from 5 days before surgery till end of experiment. Mice were sacrificed by cervical dislocation 24 h after surgery, and fresh stool samples were collected immediately.

2.5 Histomorphological analysis

Six colon tissues underwent fixation in 4% neutral formalin before dehydration in varying ethanol concentrations, paraffin-embedding, and 5 mm sectioning. Following mounting on slides, they underwent cleaning, hydration, and staining with hematoxylin and eosin (H&E). Two experienced pathologists performed histological examinations on all specimens. A modified scoring system based on the severity of tissue injury was used to assess histologic changes in a blinded manner. A semi-quantitative histopathology score was derived: I, epithelial dysregulation; II, goblet cell reduction: III, inflammatory cell invasion; IV, submucosal stiffness.

2.6 Serum cytokine quantification

After collecting blood and Caco-2 monolayer specimens from mice, samples underwent a 10 min centrifugation at 3,000 rpm and 4 ºC to extract serum and cell supernatant samples, respectively. These samples were stored at -80 ºC. Murine ELISA kits (88-7064, Thermo Fisher, Austria; EK280/3-01, MuLTI SCIENCE, Shanghai) were used to measure IL-6, D-lactic acid, and IL-1β levels. All the tests were conducted following kit directions.

2.7 Evaluation of FITC-dextran leakage

The intestines’ permeability was evaluated by measuring the amount of FITC-dextran that leaked out. After an 8-hour of fasting, the FITC-dextran (4 kDa, Sigma-Aldrich, USA) was diluted in PBS and administered to the mouse at 25 mg/mL. FITC-dextran was administered orally at 0.6 mg/g body weight. Blood samples (400 µL) were obtained from a retro-orbital puncture 4 hours later. A similar volume of PBS was added to the supernatants after centrifugation and mixed. The fluorescence intensity of each diluted serum sample (100 mL) was then measured with a multimode reader (excitation: 485 nm, emission: 528 nm, bandwidth: 20 nm). The FITC amount was computed using a standard curve.

2.8 Cell culture and treatment

Human colorectal adenocarcinoma (Caco-2) cells were maintained in Eagle's Minimum Essential Medium with 10% heat-inactivated fetal bovine serum (FBS, Gemini Bioproducts) and 1% non-essential amino acids from the American Type Culture Collection (Invitrogen, Manassas, VA, USA). Caco-2 cells (1 ´ 10⁶ cells/well) were grown in 6-well plates, prior to exposure to Salmonella enterica serotype Typhimurium lipopolysaccharide (LPS) (Sigma). Prior investigations were conducted to ascertain the optimum LPS dosage (1 mg/mL) and duration of exposure to LPS (48 h). Cell stimulation employed LPS with or without 5μM, 10μM and 15μM PE(0:0/14:0)(Selleck, USA) respectively.

2.9 Quantitative PCR (qPCR)

Samples from the Caco-2 cell were harvested, frozen in liquid nitrogen, prior to storage at -80℃ until further analyses. Total RNA isolation was conducted from Caco-2 cell using TRIzol (15596026, Invitrogen, Carlsbad, CA, USA) and quantified utilizing the Universal SYBR FAST qPCR Kit Master Mix (2x) (KAPA Biosystems, USA). The following conditions were set as the qPCR parameters: 10 min, 95 °C; 45 cycles, 10 s at 95 °C; and 60 s, 59 °C; 15 s, 95 °C; 15 s, 72 °C; and 15 s, 95 °C. Relative gene levels of Zonula occludens-1 (ZO-1), Occludin, and Gapdh were assessed via real-time qPCR (RT-qPCR). The following primer sequences were employed in the RT-qPCR reactions: ZO-1 forward (F) 5′-GAGCAAGCCTCC-5′-GAGCAAGCCTCC-5′-GAGCAAGCCTCC-5′-GAGCAATGCACATA-3′, reverse (R) 5′-TCAGTTTCGGGTTTCCTT-3′; Occludin F 5′-CAACGGCAAAGTGAATGGCA-3′, R 5′-CTTTCCTTCGTGGGAGTC-3′; Gapdh F 5′-TGTGAACGGATTTGGCCGTA-3′, R 5′-GATGGTGATG GGTTTCCCGT-3′.

2.11 Western blot assay

Cellular and mice intestinal lysis used specific lysis buffers, and protein quantification was done via a BCA kit (Beyotime, China), prior to separation on an SDS/PAGE in a Bio-Rad Mini-PROTEAN apparatus, then transfer to PVDF membranes (Bio-Rad, Marnes-la-Coquette, France), followed by a 1 h blocking at room temperature (RT) in 5% nonfat milk (w/v), and overnight (ON) treatment with primary antibodies at 4℃. The employed antibodies are listed as follows: anti-Occludin antibody (13409-1-AP); anti-ZO-1 antibody (21773-1-AP); anti-GAPDH antibody (60004-1-Ig). All aforementioned antibodies were prepared in a 1:1000 dilution, and were acquired from Proteintech, USA. The employed secondary antibodies were: HRP-goat anti-mouse IgG (115-035-003) and HRP-goat anti-rabbit IgG (111-035-144) from Jackson ImmunoResearch. Protein band visualization was done with ECL chemiluminescence imaging system and IntDen (target protein)/IntDen (GAPDH) ratio computation via ImageJ (Version 1.50i; National Institutes of Health, Bethesda, MD, USA).

2.12 Immunofluorescence and confocal microscopy

Caco-2 monolayers underwent a 15 min fixation in 4% paraformaldehyde, prior to a 10 min permeabilization in 0.2% Triton X-100, followed by a 1 h blocking in PBS with 3% BSA at RT, ON incubation at 4°C overnight with primary antibodies, namely, anti-Occludin (1:200, 13409-1-AP; Proteintech, USA) and anti-ZO-1 antibodies, with subsequent 1 h incubation with secondary antibodies (diluted 1:400) without light. Following rinse and mounting with ProLong Gold Antifade Reagent with DAPI (Invitrogen), the cells were next visualized under a laser scanning fluorescence microscope (Leica TCS SP5; Leica).

2.13 Statistical analyses

Data provided as mean ± standard error. The one-way analysis of variance (ANOVA) was employed for comparison of differences across multiple subgroups, while the t-test was employed for inter-group comparisons. * p＜ 0.05 was established as the significance threshold.

3.1 The imbalance of intestinal microbiota was destroyed by the sepsis

The 20 analyzed fecal samples were assessed via the 16S rRNA gene amplicon sequencing and metabolomic analyses. Bacterial beta diversity using Bray-Curtis distance-based Principal Coordinate analysis (PCoA) between the two groups at the OTU, phylum, and genus levels (Figure 1A). Alpha diversity analysis of each sample’s microbial diversity showed that richness, ACE, Chao1, and Shannon index considerably decreased in the sepsis group relative to the healthy group, suggesting that sepsis destoriedthe microbiota diversity Figure 1B). Species abundance cluster analysis at the phylum and genus level revealed a significant variation in the gut microbiota composition in each group (Figure 1C-D). Analysis of similarities (ANOSIM), a statistical method to determine the difference between two or more samples, showed dissimilarity between the two groups (p = 0.045) (Figure 1E). To explore differences in the relative taxa content between the two cohorts, the LEfSe (LDA Effect Size) analysis was employed (Figure 1 F).

3.2 The gut microbiota metabolites of the patients destroyed by the sepsis

Metabolic changes are believed to be a main characteristic of intestinal inflammation and are intricately linked to alterations in gut microbiota. LC-MS analysis identified 960 metabolites in the 20 stool samples analyzed. PCA scatter plots showed Clustered QC samples, suggesting that the metabolomics analysis was of high quality (Figure 2A). Volcano plot filtering revealed significantly differentially expressed metabolites between the two cohorts (Figure 2B). KEGG pathway differentially expressed metabolites enrichment analysis between the sepsis group and the healthy group via the Fisher exact test demonstrated marked alterations in several essential networks, namely, tryptophan, purine, and lipid metabolisms (Figure 2C). The top 50 metabolites heatmap showed that the differential metabolites were highly efficiently clustered in each group (Figure 2 D), and the leading 20 differentially regulated metabolites between the two cohorts as depicted in Table 1. Spearman correlation analysis was performed to determine the potential associations between marked changes in gut microbiota composition and its association with stool-related co-metabolites(Figure 2E). These results indicated that changes in gut microbiota dysbiosis influence the abundance of fecal metabolites.

3.3 The Characteristics of PE (0:0/14:0) in different groups were analyzed

KEGG pathway enrichment analysis revealed a marked alteration in lipid metabolism between the two cohorts, and a previous study showed that lipid metabolism participated in the development of disease[16]. We identified the top four differentially lipid metabolism compounds: PE(O-16:0/0:0), PE(17:0/0:0), PE(0:0/14:0), and PE(12:0/20:5(5Z,8Z,11Z,14Z,17Z) according to the VIP values (VIP > 1) and p-values (P < 0.05). The relative abundance of PE(0:0/14:0) in the sepsis patients was lower, compared to the healthy compared with other compounds(Figure 3A). Additional physical and chemical profile analyses with the Human Metabolome Database (HMDB) revealed that PE(0:0/14:0) (HMDB11470) is a phosphatidic acid molecule (C₁₉H₄₀NO₇P) with a molecular weight of 425.49721 Da(Figure 3B).

3.4 Gut microbial metabolite-PE(0:0/14:0) alleviates intestinal injury caused by sepsis in vivo

Control mice had a survival rate (SR) of 100% over the 0-24 h observation duration. In contrast, the sepsis mice SR was 6.66 % at 24 h. Among the sepsis + PE(0:0/14:0) (5 mg/kg) mice, the SR was 14.57 % at 24 h. Among the sepsis + PE(0:0/14:0) (10 mg/kg) mice, the SRs was 48.98 % at 24 h, whereas among the sepsis + PE(0:0/14:0) (20 mg/kg) mice, the SRs was 42.48 % at 24 h. Therefore, PE(0:0/14:0) (10 mg/kg) drastically decreased the sepsis mice mortality (Fig. 4A). In mice with sepsis, shortening of the colon length is one marker for evaluating the severity of colonic inflammation, the length of the colon in mouse from the sepsis group was considerably shorter, compared to the control and sepsis + PE(0:0/14:0) mice (p < 0.05) (Fig. 4B). Relative to the sepsis mice, PE(0:0/14:0) can decrease the levels of FITC, D-lactic acid, IL-1β, and IL-6 (p < 0.05) (Figure 4C-F). The Chiu pathological mucosal injury score was employed to determine the degree of intestinal histological damage. In mice from the control group, we found normal epithelial cells without ulcers, a high percentage of goblet cells, a close arrangement of the large intestinal glands, and a normal colonic mucosa morphology. Among sepsis mice, we observed ulcers in the colonic mucosa’s superficial layer, and the mucosal layer’s tissue structure disappeared. Inflammatory cell infiltration was also observed. In the sepsis + PE(0:0/14:0 group, no inflammatory cell infiltration, ulcers, or epithelial cell damage was seen, whereas the quantity of goblet cells was diminished. The Chiu pathological scoring system demonstrated that the sepsis + PE(0:0/14:0) group exhibited more intestinal mucosa injury, relative to the sepsis mice (Figure 3G). Occludin and ZO-1 are integral membrane proteins that aid the formation of the mucosal barrier of the intestines by maintaining the structural integrity of tight junctions. Based on western blot analysis, the sepsis+PE(0:0/14:0) mice exhibited enhanced occludin and ZO-1 expression, relative to the sepsis mice, indicating that PE(0:0/14:0) could protect the colonic mucosal barrier from sepsis-induced damage (Figure 3H, p < 0.01).

3.5 Gut microbial metabolite-PE(0:0/14:0) alleviates intestinal epithelial cell injury injury caused by LPS in vitro

We utilized the Caco-2 cell monolayer culture model to simulate the intestinal epithelium in vitro. The lactate dehydrogenase (LDH) release assay was used to determine the activity of LDH. Surprisingly, LDH contents were considerably enhanced in the LPS group, relative to the LPS + PE(0:0/14:0)(10μM) subgroup (Figure 5A). Occludin and ZO-1 expression levels were elevated in the LPS + PE(0:0/14:0) group as opposed to the LPS group, indicating that PE(0:0/14:0) could protect the colonic mucosal barrier from damage. The findings were confirmed by qRT-PCR and immunofluorescence analyses (Figure 5 B-D ).

Sepsis causes intestinal mucosal injury, bacteria translocation, and worsens the intestinal and distant organ injury [17]. Studies have demonstrated that intestinal injury is a major contributor to critical illness, sepsis, and multiorgan failure [18]. Zhang et al. reported that the disruption of intestinal microbiota promoted the development of abdominal aortic aneurysm by increasing neutrophil infiltration and formation of the neutrophil extracellular trap (NET) [19]. Elsewhere, analysis of intestinal microbiota in premature infants revealed that high abundance of Staphylococcus epidermidis and Enterobacter induced injury to the intestinal barrier and aggravated the inflammatory reactions[20]. Modulating alterations in intestinal microbiota can potentially aid in the maintenance of the microecology and environmental alteration balance [21].

Subsequently, we explored changes in intestinal microbiota through 16sRNA sequencing and metabonomic analysis of patients with sepsis and the healthy group. The results showed that sepsis resulted in intestinal microbiota disorders, which further aggravated metabolic disorders, consistent with a previous study [22]. Gut microbial-related mechanisms have been shown to regulate gut microbial metabolites and improve intestinal barrier function. For instance, Lactobacilli metabolizes tryptophan to form an AhR ligand, indole-3-aldehyde, which, in turn, accelerates IL-22 synthesis to protect against C. albicans infections while suppressing inflammation [23]. Spearman correlation analysis revealed a correlation between lipid metabolites, bile acid metabolites, and tryptophan metabolites and the quantity of beneficial bacteria including Lactobacillus, Bacteroides, and Streptococcus.

Lipids and lipid metabolites have long been recognized to have other biological functions besides acting as energy stores and contributing to the membrane structure [24]. Dysregulation of lipid metabolism was previously found to trigger disease onset and progression in age-related macular degeneration and diabetes [25]. However, some lipids and their metabolites can be beneficial [24]. For example, lipids and fatty acids can promote the phagocytosis of macrophages[26]. In our study, we found a marked difference in the abundance of PE(0:0/14:0) in the sepsis patients and healthy individuals. Currently, no study has investigated the relationship between PE(0:0/14:0) and other intestinal bacteria. Our results suggested that the Parabacteroides distasonis amount was elevated among healthy individuals, in relation to sepsis patients. Moreover, spearman correlation analysis of intestinal microbiota and metabolites showed that PE(0:0/14:0) was closely related to Parabacteroides distasonis. Based on these results, we speculated that the metabolites were mainly produced by intestinal Parabacteroides distasonis. This bacterium can modulate the host’s metabolism and alleviate obesity and metabolic dysfunctions[27]. In vivo and in vitro experiments revealed that PE(0:0/14:0) treatment suppressed inflammation, enhanced the intestinal tight junction protein expressionss, and protected against sepsis-induced injury to the intestinal barrier. Collectively, these results indicate that optimal intestinal metabolism can protect the intestinal barrier.

In summary, our analyses indicate that sepsis could disrupt the intestinal microbiota and intestinal metabolites composition. The intestinal metabolite, PE(0:0/14:0) also protected against sepsis-induced intestinal injury. However, the mechanism of PE (0:0/14:0) in the protection of the gut need to be further explored. Our research highlighted the sepsis-mediated regulation of the intestinal microbiota, metabolites, and intestinal barrier, which may aid in the development of novel and effectively diagnostics and therapy for sepsis.

Ethical Approval

The hospital ethics committee granted the research protocol approval (ethics batch number: 2019118), and all patients provided informed consent signed by their legal representatives or themselves. All animal experiments were approved by the Experimental Animal Ethical ReviewCommittee, East China Normal University's Animal Center (Shanghai, China).

Competing interests

We hereby state that there are no potential conflicts of interest.

Authors' contributions

Wang Zetian, Qi Yue and Wang Fei have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data；Peng Ziyao has been involved in drafting the manuscript or revising it critically for important intellectual content; Ruiqin Han, Xingyun Wang have given final approval of the version to be published; Tang Jianguo agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Funding

This research was supported by the Key Specialty Construction of Shanghai Fifth People's Hospital affiliated to Fudan University (2020WYZDZK06). and Construction Project of Characteristic College in Minhang District,Shanghai(2020MWTZA02).

Availability of data and materials

The 16sRNA sequence data can be found in the NCBI database under accession number PRJNA905077.

Acknowledgments

The authors would like to thank Prof. Xirong Guo and Xingyun Wang Ph.D (Hongqiao International Institute of Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200240, China), for critically reviewing the manuscript.

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Table 1 is available in the Supplementary Files section.

No competing interests reported.

Table1.xlsx
Table 1. The top 20 differential metabolites between the two group. Note: VIP: Variance importance.

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Gut microbial metabolite- PE(0:0/14:0) could inhibit sepsis-induced intestinal injury

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