Gut microbes of the cecum versus the colon drive more severe lethality and multi-organ damage

An intestinal perforation or puncture leads to a high risk of sepsis-associated morbidity. A perforation initiates the transfer of the intestinal contents (ICs) to the peritoneal cavity, leading to abdominal infections and varying with different prognoses. However, the mechanisms associated with different perforations in the cecum and colon remain unknown. We sought to examine how different gut flora contribute to prognoses in different intestinal perforation sites. We compared the microbiome of the ICs in the cecum and colon in a fecal-induced peritonitis mouse model. The results showed that cecum ICs developed more severe sepsis than colon ICs, including a shorter median survival time, increased biochemical indicators, more pathological changes in multiple organs and overwhelmed systematic inflammation. Moreover, our results demonstrated that cecum ICs hold more bacterial burden in unit weight than colon ICs, and the microbial communities differed between the ICs from the cecum and colon. A more detailed comparison of the two microbiome groups showed that the abundance of potentially pathogenic bacteria increased in the cecum ICs. Our data suggest that the sepsis severity developed by perforation was associated with bacterial burden and increased abundance of potentially pathogenic bacteria in the cecum. Our findings first compared the differences in the lethality associated with the ICs of the cecum and colon, which pointed out that the site of perforation could help providers predict the severity of sepsis.

Abdominal infection; gut microbiome; sepsis; cecum perforation; colon perforation; 16S rRNA gene sequencing (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint 1 Introduction Abdominal infections commonly occur after perforation of the lower digestive tract. Lower digestive tract perforation, mainly including cecal perforation and colonic perforation, is a prevalent but acute disease that threatens life. The leading cause is intestinal contents (ICs) leakage to the sterile peritoneal cavity, thus contributing to harmful or lethal infection, severe multi-organ damage, and even death [1, 2]. Such perforations can increase sepsis-related mortality and morbidity [3-5]. Once a perforation occurs, emergency surgery and antibiotic treatment are required, but its prognosis varies with the patient's overall health status, diagnosis, and surgery duration [3, 6]. Some retrospective studies showed that cecal and colon perforations have different prognoses [7-10], which implies a different mechanism of lethality. However, clinical retrospective pilot studies have drawn inconsistent

for which different outcomes vary in different sites of the lower digestive tract [11, 12]. Therefore, these clinical issues are still being debated and need further clarification. Gut microbes vary and can be drivers of many diseases in previous studies [13-17], which can predict patient susceptibility to disease and provide microbiota-targeted tools for disease treatment [18]. For example, the fecal microbiome signature exhibits high specificity for colon cancer [19], non-alcoholic fatty liver disease development (NAFLD) [20], and Parkinson's disease [21]. Interestingly, these microbiome signatures produced strategies for treating Parkinson's disease [21], ulcerative colitis [22], and intestinal inflammation [23]. In colonic perforation-associated sepsis treatment, empiric antibiotics are routinely prescribed, but their efficacy varies because no specific antibiotics or spectrum of antibiotics to treat sepsis are available [1, 17] due to uncontrolled bacterial infection. One possible reason is that the patient misses an optimal treatment window when the physicians are forced to modify the therapy plan to tackle the unsatisfactory results of empiric antibiotic therapy [24], which implies it is wise to target the driving bacteria. It reports that gut microbe compositions vary in their different fragments of the low digestive tract in humans and animals [25, 26], and several lethal or infectious bacterial strains were previously reported in gut microbiota [2, 27]. Therefore, it can be reasonably considered that sepsis caused by colonic perforation needs further analysis, and whether it is driven by different gut microbiota remains to be explored. Intra-abdominal sepsis hospital admissions took up 20% of patients with sepsis [28], which could be mimicked with polymicrobial rodent models for basic research [29]. The polymicrobial models of (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint sepsis are typically applied as they involve either cecal ligation and puncture (CLP) or fecal-induced peritonitis (FIP) with intra-peritoneal injection. Nevertheless, in the CLP model with an open abdominal setup, the cecum is ligated and punctured with a needle to allow ICs to leak into the sterile peritoneal cavity [30]. Thus, the CLP model introduces the risk of infection caused by surgery that would add additional complicated factors to the outcome. Instead, FIP provides an alternative model for mimicking the polymicrobial sepsis model without surgery [29]. Therefore, the FIP model might be reasonably used to evaluate the severity of the on-site ICs in sepsis development. In the study, we sought to examine whether gut microbiome from different perforation sites in the intestine would cause variations in sepsis development. We used a mature FIP sepsis model for the first time to evaluate the effects of ICs from different sites (cecum or colon) on the outcomes by monitoring their survival, blood biochemical indicators, systemic cytokines, lung inflammation, and histological (liver, lung and kidney) alterations of the model mice. In addition, we compared the microbiomes of the cecum and colon and linked their bacterial number and microbial community structure to the severity of perforation sites. 2. Results 2.1 Different survival time between the mice receiving ICs from the cecum and colon As shown in Figure 1A , we monitored the survival time following the injections with IC solutions from different intestinal sites. The Kaplan–Meier curve showed different survival times between mice receiving IC aliquots from the cecum and colon. The ICs from the cecum group showed a much shorter median survival time than the colon group (22.57 vs. 25.20 h; p < 0.0001, Figure 1B ), while no deaths were observed in saline-injected mice. These results demonstrate that cecal ICs exerted more lethal effects than those from the colon. 2.2 ICs from different intestinal sites induce different blood biochemical indicators The blood biochemical indicators exhibit the degree of organ injuries in rodents and humans. Thus, biochemical blood indicators were detected and demonstrated various degrees of injuries among the cecum-FIP and colon-FIP groups. We noted that biochemical indicators of impaired liver function, alanine transaminase (ALT), tended to increase in the cecum group compared to the control or colon group but without a noticeable difference (p=0.24 and 0.34, respectively, Figure 2A). The aspartate (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint transaminase (AST) of the cecum-FIP group increased when compared to the control group (p<0.05, Figure 2B ) and tended to be higher than the colon group (p=0.10, Figure 2B ). In addition, an indicator of tissue damage lactate dehydrogenase (LDH, p<0.05; Figure 2C), and indicators of kidney dysfunction creatinine (p<0.05; Figure 2D), creatinine kinase (p<0.05; Figure 2E), and blood urea nitrogen (BUN, p=0.072, Figure 2F ), were significantly higher in the Cecum-FIP group than the colon-FIP group. Of note, blood LDH (p<0.05), BUN (p=0.056), Creatinine (p=0.11), and CK (p=0.12) in the cecum-FIP group but not the colon-FIP group tend to increase than the control groups. The blood biochemical indicators demonstrate a more severe organ injury in cecum-FIP mice than in the colon-FIP group. 2.3 ICs from the cecum induce high levels of blood cytokine productions To monitor the systemic immune response to the cecum-FIP or colon-FIP challenge at 16h, the multiplex cytokines panel was employed to show that pro-inflammatory cytokines such as TNF- α , IL-1α, IL-1β, and IL-6 in the cecum-FIP group have a higher level than the colon-FIP group ( Figure 3). Furthermore, other plasma cytokines also elicited similar higher levels in the Cecum-FIP group than in the colon group. These data indicate an extreme immune response to the FIP developed by the cecum compared to the colon group. 2.4 ICs from the cecum induce severe liver, lung and kidney pathological alterations To evaluate the organ injuries of mice receiving ICs injections, pathological examinations of the multiple organs (liver, lung, and kidney) were found to demonstrate various degrees of pathological changes at 16 h after mice received injections with either saline or ICs from the cecum or colon (Figure 4A). In liver tissues, we observed that parenchymal cells demonstrated turbidly (or edema) and were swollen, and the sinus cord structure was unclear (the liver sinuses became narrowed, and a thickened disorganized liver plate was detected). Nevertheless, the cecum group had degenerated and showed balloon-like changes—accounting for ~60% of the area of the liver sections—was scattered with individual lymphocytes and the focal point necrosis of hepatocytes could be observed ( Figure 4A, upper panel). The Knodell scoring on the degenerative and necrotic cells and Ishak scoring on the fibrosis showed that the cecum-FIP mice tended to receive slightly higher marks than the colon-FIP mice (p=0.38 and 0.31, Figure 4B). In mice with ICs, the lungs showed broken focal alveolar septa, (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint fused alveolar cavity, slightly widened focal alveolar septum, and alveolar epithelium hyperplasia compared to the control group. However, the cecum-FIP group showed more lymphocyte and neutrophil infiltration in the small focal pulmonary interstitium than in the colon-FIP group ( Figure 4A, middle panel) and tended to receive a higher clinical score (p=0.09, Figure 4C ). In the kidney tissues, the epithelial cells of renal tubules in small foci were loose and swollen, and tiny vacuoles with foams could be observed in the cytoplasm of both groups (Figure 4A , bottom panel); the severity was more intense in the cecum-FIP group when compared to the control group or the colon group (p<0.01 and p=0.34, respectively, Figure 4D). In addition, the NGAL mRNA, an early marker gene for acute kidney injury, is upregulated in both the cecum- and colon-FIP mice ( Figure 4E ), while its expression in the cecum-FIP group tends to be higher than in the colon-FIP group (p=0.32). These descriptions for the hematoxylin and eosin staining and further clinical scoring ( Figures 4B-D) demonstrated severe pathological alterations of several tissues in the cecum-FIP mice compared to the colon-FIP group. 2.5 ICs from the cecum induce severe lung inflammation Several indicators were stained for lung specimens to examine the lung injuries of mice receiving ICs injections. The Gr-1 (also called Ly6G) staining showed more neutrophil infiltration in the lung tissue of the cecum-FIP group than in the colon-FIP group ( Figures 5A and B). Moreover, the TNF- α is more pronounced in the lung tissue of the cecum-FIP mice than in the colon-FIP mice ( Figures 5A and C ). However, the ZO-1 expression showed unobserved alterations in the cecum-FIP group compared to the colon-FIP group ( Figures 5A and D). These data suggest that the cecum-FIP mice developed more severe lung inflammation than the colon-FIP mice, further corroborating the pathological examinations on the lung tissues (Figures 4A, middle panel, and 4D). 2.6 The gut microbiome varied in different ICs To associate the severity of sepsis with the gut microbiota, we compared the bacterial burden in the ICs by the 16S rDNA qPCR, and the quantification results demonstrated that the cecum ICs contained 3 times bacterial copy per unit weight than in the colon ICs group ( Figure 6A). Moreover, we used 16S rRNA gene sequencing to evaluate the gut microbiota from either cecal or colonic sections. Overall, 1,286,949 effective 16S sequencing tags were obtained from the ICs of 10 young mice with (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint an average of 64347.45 tags per sample (ranging from 56755 to 67929). After taxonomic identification, the validated tags were assigned to 98,443 operational taxonomic units (OTUs) for further analysis ( Supplementary Table S1 ). According to the rarefaction ( Supplementary Figure S1A) and species accumulation curves ( Supplementary Figure S1B ), the current sequencing and samples were sufficient for taxa identification. Furthermore, the flower plot showed that all ICs contained a core of 843 OTUs, and most could discriminate the ICs of the cecum from the colon (Figure 6B ). Nevertheless, the rank abundance distribution curve showed no noticeable changes in the richness and evenness of the bacteria in both groups (Supplementary Figure S1C). Next, we compared the microbial difference between ICs from the cecum and colon. The alpha diversity indexes, including Shannon, Simpson, Chao1, and PD tree indices in the cecal ICs, were not significantly altered compared to the colon group ( Supplementary Figure S1C–F ), suggesting similar richness and alpha diversity. However, a difference in beta diversity between the two groups was observed based on the PCoA analysis of the Bray–Curtis distance ( Figure 6C ), which was significant according to the Adonis analysis (p = 0.001). Additionally, the binary_jaccard, bray_curtis, and unweighted_unifrac distance ( Figure 6D, Supplementary Table S2 ) significantly increased in the cecum group. In addition, our data showed a considerable difference in the relative abundance of each bacterium at the phylum and genus levels across samples in the cecum and colon (Figures 6E, F, and Supplementary Figure S2). 2.7 Potentially pathogenic gut microbes differ between the cecum and colon To investigate the alterations associated with lethality or survival time, we conducted a linear discriminant analysis effect size (LEfSe) analysis. The main differences were found to be the increase of Firmicutes (class Clostridia, order Oscillospirales and Lachnospirales, family Oscillospiraceae ), Desulfobacterota (Class Desulfovibrionia, order Desulfovibrionales , family Desulfovibrionaceae ), and Campilobacterota (class Campylobacteria, order Campylobacterales, family Helicobacteraceae), and a reduction in the abundance of Bacteroidota (class Bacteroidia, order Bacteroidales, family Muribaculaceae and Prevotellaceae in the ICs of the cecum ( Figure 7A ). In addition, some differences were observed at a lower taxonomical level. For example, Oscillospiraceae (genus Lachnospiraceae_NK4A136_group) and Helicobacteraceae (genus Helicobacter) increased in the cecum. In contrast, the colon exhibited a gain of Muribaculaceae and Alloprevotella at the genus level (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint and Muribaculaceae and Prevotellaceae at the family level (Figure 7B, Supplementary Figure S2) . Furthermore, our data showed that several pathogenic bacteria were highly enriched in the cecum group, while the beneficial bacteria decreased in the cecum compared to the colon group ( Figures 7C-I and Supplementary Figure S2 ). Those data indicate that the gut microbiome community structure in the cecum differs from the colon. 3. Discussion Intestinal perforation or puncture presents a high risk for patient morbidity, mortality, and sepsis severity. The cecum and colon were major perforation sites, especially the cecal perforation, which was more common in patients [31, 32]. Nevertheless, the ICs of different lower digestive tract fragments were variably abundant in the microbiome, food debris, metabolites, and digestive secretions. In this study, we compared the microbiome of the cecal and colon ICs and their lethality in an FIP mouse model. The results showed that the ICs of the cecum developed more severe sepsis than the colon and differed in bacterial biomass and microbiome structure. In the present study, we compared the lethality of ICs in the mouse cecum and colon. The observations showed that cecal ICs produced a shorter median survival time, indicating that the cecum perforation might exhibit higher sepsis-associated lethality than the colon perforation. Moreover, we noticed that the blood biochemical indicators for liver impairment (AL T and AST), tissue damage (LDH), kidney dysfunction (BUN and Creatinine), and heart dysfunction (CK) were significantly higher or tended to increase in the FIP model developed with ICs from cecum which further corroborated by the histological examinations on liver, lung and kidney tissues. Compared to the colon group, we noticed more severe pathological alterations (including liver, kidney, and lung) in the cecum specimens than in the colon specimens, further corroborating the greater sepsis-associated lethality of cecum FIP mice. These findings are lined with clinical observations that the abdominal infection caused by intestinal perforation on the right side of the colon is more likely to have a poor prognosis and poor antibiotic treatment effect than on the left side [33] and a higher mortality rate (30-72%) occurred in cecum perforation [34]. In our study, the mice did not receive any treatment during sepsis progression, which differs from the clinical patients who were well treated with antibiotic administration. In short, the present study adopted a very objective strategy that did not introduce therapeutic interference to observe sepsis progression caused by different ICs of intestine (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint segments. We assumed the cecum perforation led to more serious infection and severe inflammation than the colon perforation. Further study on the blood plasma samples showed that the characteristic cytokine markers of sepsis[35], including TNF α , IL-1 α , IL-1 β , IL-6, and IL-10, increased significantly during the experimental timeline in the cecum-FIP mice compared to the colon group or the control mice. Similarly, we also demonstrate that levels of Eotaxin, G-CSF, GM-CSF, IFN- γ , IL-2, IL-3, IL-4, IL-5, IL-6, IL-9, IL-12(p40), IL-12(p70), IL-13, IL-17A, KC, MCP-1, MIP-1 α , MIP-1 β , RANTES rise significantly during cecum-induced sepsis but not significantly or less extent increase in colon-induced inflammation. These changes are often associated with inflammation in various organs, which indirectly indicates that cecum-induced sepsis has a higher systemic inflammatory response. IL-5 expression level is often associated with lung inflammation [36], and in the present study, we found plasma IL-5 raised in the Cecum-FIP mice that manifested more severe pneumonia and pathological changes. IL-6 is a sensitive marker of inflammation and can be of prognostic value in critically ill patients with multi-organ failure [37]. Interestingly, compared to colon-induced sepsis, more severe inflammation happened in cecum-induced sepsis. Patients with sepsis had a significant increase in circulating IFN- γ released by CD4 + T cells via IL-12 [38]. These increased plasma levels of injury and inflammatory markers confirmed the complexity of the dysregulated immune response during cecum-induced sepsis with exacerbated systematic inflammation and acute lung injury and worsened survival. The composition of ICs varies in nutrients, pH, ions, gut microbes, and food debris in different segments, and their leakage into the peritoneal cavity can cause unexpected outcomes [1, 2, 27, 39]. The gut microbiome is the main component of the intestine and may vary in different sites [25, 26]. Moreover, the gut microbiome taxonomic differences are associated with additional mediators of disease pathogenesis, such as metabolites, short-chain fatty acids (SCFA), and metagenomic changes [1, 2, 39, 40]. Our study demonstrates that the cecum ICs harbor a ~ 3-fold increased bacterial burden in unit weight than the colonic ICs, which could explain the increased illness severity and systemic inflammation due to increased infectious load and pathogen-associated molecular pattern (PAMPs) [2, 40]. In addition, we observed that the texture of the cecal contents is more fluid than that of the colon, which may indicate that more intestinal flora could quickly enter the abdominal cavity when the cecum is perforated. This high bacterial density and more fluidic characteristics in the cecum segment (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint collectively lead to a more severe infection during cecal perforation. Although the alpha diversities of the cecum and colon microbiomes were similar in this study and suggested similar richness and diversity, these beta diversities demonstrated that the cecum and colon hold a different microbial community structure. Interestingly, several bacterial populations were identified in the microbial community as potential immunological or pathogenic [2]. For example, at the phylum level, we observed that the potentially pathogenic bacteria p_Firmicutes [41] primarily caused an increase in the cecum group. We also noticed that several potentially pathogenic gut flora [42-44], including genera Helicobacter, Mucispirillum, and Lachnospiraceae_FCS020_group , and the family Desulfovibrionaceae, were more abundant in the cecum than in the colon group at the lower taxonomic level. Furthermore, inflammation-associated Lachnoclostridium and Desulfovibrio [45] also increased in the cecum compared to the colon group. In the present study, we knew Helicobacter could stimulate CD4+ T cells to Th1 differentiation through IL-12 production and enable these cells to secrete cytokines such as IL-1, IL-6, TNF- α and IFN- γ [46]; Mucispirillum and Lachnospiraceae_FCS020_group can promote intestinal inflammatory response [47, 48]; Desulfovibrionaceae were known for their sulfate-reducing capacities, as a result of this producing toxic hydrogen sulfide [49]. Therefore, these potentially pathogenic bacteria would explain their high IC-associated pathogenic potential for mouse survival and more severe multi-organ injury. In contrast, the potentially beneficial gut microbiome, such as the genera Alloprevotella, Alistipes, Bacteroides, Parabacteroides, Rikenellaceae_RC9_gut_group, Prevotellaceae_UCG-001, Prevotellaceae_NK3B31_group, and family Muribaculaceae [43, 44] decreased in the cecum group. In addition, we found that the Firmicutes to Bacteroidota (F/B) ratio [50-52] was higher in the cecum than in the colon group, indicating a dysbiosis in the community structure. Therefore, our results demonstrate that the greater accumulation of potentially pathogenic and less beneficial bacteria in the cecum has pro-inflammatory properties, causing more serious inflammatory storms and contributing to their lethality during the perforation or puncture. In recent years, many updated international consensus definitions for sepsis and septic shock provide microbiota-targeted tools to improve sepsis-associated outcomes [53]. The target of changing antibiotics is to provide moderate homeostasis of the healthy intestinal microbiota. Therefore, we conducted a series of experiments starting with the different intestinal flora and demonstrated that (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint causing abdominal infections was related to the ratio of different intestinal flora rather than just considering the presence of pathogenic bacteria. In the study, we compared the different IC-associated lethality for the first time while mimicking different perforation sites. The study showed that the cecal ICs are more lethal to mouse survival and cause multi-organ damage, which provides an understanding for evaluating and modulating the microbiota community to decrease the levels of harmful bacteria and increase the beneficial bacteria, thus minimizing antibiotic overuse. However, we did not compare the ICs of the cecum and colon in humans, and such clinical associations require further investigation. Of note, the perforations in the patients who received antibiotic administration make it challenging to compare to the animal data (without antibiotic treatment) in our study. Our work suggested that the severity of cecum ICs-developed sepsis was associated with the bacterial burden and enriched pathogenic bacteria in microbial communities. However, we did not detect additional mediators, such as SCFA, fungal or other pathogens, and metagenomic changes, which need further investigation. Further work is needed to identify the pathogenic bacteria that drive sepsis and mechanistic study. In summary, we standardized and developed a protocol to examine the different sites (cecum and colon) of perforation driving the sepsis. Our study demonstrated that cecum ICs developed more lethality, systemic inflammation, and multi-organ damage than colon ICs and found that the sepsis severity developed by perforation was associated with bacterial burden and increased abundance of potentially pathogenic bacteria in the cecum. This study provides the first experimental evidence to support that different perforations in the low digestive tract vary in terms of their prognoses. Therefore, it will be beneficial to design microbiota-based strategies to intervene in the microbial community to treat or slow the progression of sepsis or abdominal infections. 4. Materials and methods 4.1 Study design To explore the possible cause of the severity of sepsis from different punctures or perforations in the lower digestive tract in mice, we used the FIP model [29] to evaluate gut microbes from the different intestinal sites and test whether they produced different effects on systematic inflammatory responses (SIRS) or survival of the mice. As shown in Figure 1A, the ICs from the cecum and colon (including the ascending, transverse, and descendant colon sections) were collected individually from the site of (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint the euthanized mice (n = 10). Briefly, their abdominal were opened to expose different intestine fragments, and the ICs were scrutinized using sterile forceps to the clean tube. A small proportion of each IC sample was stored at −80 /i4 for subsequent 16S rRNA gene sequencing analysis. The remaining IC samples were mixed, weighed, and dissolved to form the solution for intraperitoneal injections into C57BL/6J mice for 16S rDNA quantification, survival, biochemical, cytokines and histological analysis. 4.2 Mice and fecal-induced peritonitis (FIP) model The male C57/BL6J (7–9-week-old) mice were purchased from the Shanghai Model Organism Center (SMOC) and maintained for ~2 weeks under a 12-h day/night specific-pathogen-free hood with free access to food and water for animal experiments. All procedures were carried out under the regulations for animal experimentation and were approved by the ethics committee of Tongren Hospital, Shanghai Jiao Tong University School of Medicine (No. A2022-012-01). To assess the lethality and prognosis of different ICs, we adopted the FIP sepsis model [29] to construct the sepsis model in this study. Briefly, ICs from the different fragments of the cecum or colon were collected, followed by sacrifice and dissection of the donor mice (n = 10). ICs from 10 mice were mixed, weighed, and dissolved in saline at a 90 mg/ml concentration. Finally, the IC solutions were filtered with a 70-µm cell strainer and intraperitoneally injected (i.p.) with a dose of 2 g/kg body weight as previously described [29]. Then, we monitored the mouse survival and recorded their death time for a survival curve analysis among the saline (n=8), cecum (n=9), and colon (n=10) groups. They were euthanized at 28 h-post injection when the humane endpoints reached according to the following criteria: unable to crawl, unresponsive to touch stimulation, muscle trembling, arching hair on the back, dyspnea, and large amount of secretions in the mouth and nose. For histology analyses and biochemical analysis, mice (n=4 per group) were peritoneally injected with saline, cecum-ICs and colon-ICs to euthanization at 16h post-injections for dissecting tissues for further analysis as described below. 4.3 Tissue preparations, hematoxylin-eosin staining and pathological scoring After intraperitoneally injected with the indicated ICs or saline for 16 h, the mice received anesthesia with 0.3% pentobarbital sodium followed by whole blood collection. Then, they were killed for tissue (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint preparations. Freshly dissected livers, lungs, and kidneys were fixed in 4% paraformaldehyde to prepare paraffin-embedded sections. The 3 µm sections were used to perform the hematoxylin-eosin (H&E) staining as described previously [54], scanned by the Digital pathology slide scanners (KFBIO, Ningbo, China), and viewed by its K-ViEWER software. The clinical scores of the liver [55, 56], kidney [57], and lung [58] were employed by the clinic pathologist to evaluate the specific manifestations of pathological tissues. 4.4 Multiplex-Immunofluorescence staining and quantification TSAPLus Fluorescence Triple Staining Kit (Wuhan Servicebio Technology Co., Ltd., Wuhan, China) was used for multiple immunofluorescence staining of lung specimens paraffin sections, i.e., herein sequentially labeling antigen with TNF- α (# GB11188, 1/i11000), Ly6G (#GB11229, 1:2000) and ZO-1 (#GB111402, 1:100) antibodies purchased from Wuhan Servicebio Technology Co., Ltd. to evaluate the degree of lung injury when receiving the FIP. The slides were counterstained with 4'-6-diamidino-2-phenylindole (DAPI) and mounted with anti-fading solutions. The slides were scanned with the 3DHISTECH scanner and Viewer (Pannoramic MIDI, Hungary). The Ly6G- and TNFα− positive numbers and ZO-1 staining area were quantified using HighPlex FL v3.1. module or Area Quantification FL v2.1.2 module of the Indica Labs HALO software (v3.0.311.314) and plotted as the ratio to the Saline group. 4.5 Blood Biochemical Indicators Detection The -80 °C stored plasma was detected for alanine aminotransferase (AL T), aspartate aminotransferase (AST), Creatinine, creatinine kinase (CK), Blood Urea Nitrogen (BUN), and lactate dehydrogenase (LDH) in the saline- and gut-microbes receiver group (n=3-4 per group). All biochemical indicators were measured using the Nanjing Jiancheng Bioengineering Institute (Nanjing, China) kit. 4.6 Cytokines measurements The mouse plasma cytokines from the saline- and gut-microbes receiver mice (n=4 per group) were measured by Luminex liquid-phase suspension chip (Bio-Plex Pro Mouse Cytokine Grp I Panel 23-plex, #M60009RDPD, Wayen Biotechnologies (Shanghai), Inc). Briefly, the plasma and standard (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint samples were diluted with Assay buffer to load on the chip for 30 min incubation. The chip incubates with the detection antibody for another 30 min, followed by Streptavidin-PE for 10 min. Finally, the multi-plate was detected by a Bio-Plex 200 system (Luminex Corporation, Austin, TX, USA) and the cytokine concentration was determined according to the standard curve fit. 4.7 RNA extraction, reverse transcription and quantitative PCR (qPCR) The frozen kidney was homogenized for RNA extraction with Trizol Reagent (Invitrogen), quantification, reverse transcription, and SYBR green quantitative PCR for detecting the gene expression as previously [59-61]. The primers for qRT-PCR are listed below: neutrophil gelatinase-associated lipocalin ( NGAL) (Forward: 5´-CTCAGAACTTGATCCCTGCC-3', Reverse: 5´-TCCTTGAGGCCCAGAGACTT-3'); Actb (Forward: 5´-GGCTGTA TTCCCCTCCA TCG-3', Reverse: 5´-CCAGTTGGTAACAATGCCA TGT-3'). The mRNA expression was normalized to the Actb mRNA and interpreted as the foldchange of the Control group (Saline). 4.8 16S rDNA qPCR 16S rDNA from mouse cecum ICs and colonic ICs was analyzed by SYBR green (#Q711-03, V azyme, Nanjing, China) qPCR for bacterial 16S rDNA genes using universal primers 341F (5’-cctacgggnggcwgcag-3’) and 785R (5’-gactachvgggtatctaatcc-3’) according to the following cycling conditions [62]: 50°C for 2 min, 95°C for 2 min, 45 cycles of 95°C for 15 s, 58°C for 15 s and 72°C for 1 min. Quantitation of 16S rDNA copies normalized to pUC57 vector harboring 16S V3 and V4 region and interpreted as the copies per fecal (in mg). 4.9 16S rRNA gene sequencing analysis We performed 16S rRNA gene sequencing analysis as described previously [63]. Briefly, IC samples from the cecum or colon fragments of the donors (9–11-week-old male mice) were collected and stored at −80 °C after collection. According to the manufacturer's instructions, bacterial DNA was isolated from the IC samples using a MagPure Soil DNA LQ Kit (Magen, Guangdong, China), measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and evaluated by electrophoresis. First, the bacterial 16S rRNA gene was amplified using universal primer pairs that targeted the V3-V4 hypervariable regions (343F: 5 ′ -TACGGRAGGCAGCAG-3′ ; (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint 798R: 5 ′ -AGGGTATCTAATCCT-3′ ). The amplification products were then purified with Agencourt AMPure XP beads (Beckman Coulter Co., USA) and quantified using a Qubit dsDNA assay kit. Finally, the DNA was sequenced using the Illumina NovaSeq6000 platform (Illumina Inc., San Diego, CA) by OE Biotech Company (Shanghai, China). Paired-end raw reads were deposited in the NCBI Sequence Read Archive (SRA) with BioProject ID accession number PRJNA872016, preprocessed using Trimmomatic software [64], and assembled using FLASH software [65]. Reads with 75% of bases above Q20 were retained using QIIME software (version 1.8.0) [66]. The clean reads were then subject to primer sequence removal and clustering to generate operational taxonomic units (OTUs) using VSEARCH software with a 97% similarity cutoff [67]. The representative read of each OTU was selected using the QIIME package. All representative reads were annotated and blasted against the Silva database (Version 132) using the RDP classifier (confidence threshold was 70%) [68]. The alpha diversity, including the Chao1 index [69], Shannon index [70], phylogenetic diversity (PD) tree, and beta diversity principle coordinate analysis (PCoA) with a Bray–Curtis distance matrix, were determined using the QIIME software. Linear discriminant analysis effect size (LEfSe) analysis was performed to identify unique microbial composition species associated with infection severity. Microbiota with a linear discriminant analysis (LDA) score greater than 3.5 were defined as different genera. 4.10 Statistical Analysis Statistical analysis was performed on raw data for each group using a One-way ANOV A with post unpaired t-test, Kruskal-Wallis test, or Mantel–Cox test as indicated using GraphPad Prism 8 software (GraphPad Software, San Diego, CA). Data were expressed as mean ± standard error of the mean (SEM). P-values refer to the probability of the null hypothesis that the means do not differ. A p < 0.05 was considered significant, and p ≥ 0.05 was not significant. Ethics approval The animal study (No. A2022-012-01) was reviewed and approved by the ethics committee of Tongren Hospital, Shanghai Jiao Tong University School of Medicine. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint Consent for publication All authors agree to publication. Availability of data and materials The raw reads of 16S rRNA-seq have been deposited in the SRA database and assigned a BioProject accession number PRJNA872016 for 16S rRNA-seq. In addition, other raw data and materials can be reasonably requested from the authors (Y . Lu or J. Zhang ). Competing interests The authors declare no competing interests. Funding This work was supported by the National Natural Science Foundation of China (No. 82072205 to Y . Lu, No. 82302441 to K. Xu, and No. 82002667 to L. Zhou), the Shanghai Science and Technology Committee (No. 16DZ1911105 to J. Zhang), the Research Fund of Medicine and Engineering of Shanghai Jiao Tong University (No. YG2021QN144 to K. Xu and No. YG2019QNB27 to D. Lin) and Research fund of Tongren hospital (No. TR82072205 to Y . Lu). Authors' contributions Y . Lu, J. Zhang, and K. Xu designed the study. K. Xu, J. Tan, D. Lin and Y . Lu performed experiments. K. Xu, J. Tang, D. Lin, Y. Chu, L. Zhou, J. Zhang and Y. Lu analyzed the data. Y . Lu and J. Zhang supervised the study. Y. Lu, K. Xu, J. Tan, Y . Chu, L. Zhou, and J. Zhang wrote the paper. All authors reviewed the results and approved the final version of the manuscript.

We thank OE Biotech Co., Ltd. (Shanghai, China) for assistance in the 16S RNA gene sequencing analysis and their cloud tools for data analysis. Supplementary information The Supplementary information for this article can be found online. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint

1. Assimakopoulos SF, Triantos C, Thomopoulos K, Fligou F, Maroulis I, Marangos M, Gogos CA: Gut-origin sepsis in the critically ill patient: pathophysiology and treatment. Infection 2018, 46(6):751-760. 2. Adelman MW, Woodworth MH, Langelier C, Busch LM, Kempker JA, Kraft CS, Martin GS: The gut microbiome's role in the development, maintenance, and outcomes of sepsis . Crit Care 2020, 24(1):278. 3. Hecker A, Uhle F, Schwandner T, Padberg W, Weigand MA: Diagnostics, therapy and outcome prediction in abdominal sepsis: current standards and future perspectives. Langenbecks Arch Surg 2014, 399(1):11-22. 4. Perner A, Gordon AC, De Backer D, Dimopoulos G, Russell JA, Lipman J, Jensen JU, Myburgh J, Singer M, Bellomo R et al : Sepsis: frontiers in diagnosis, resuscitation and antibiotic therapy . Intensive Care Med 2016, 42(12):1958-1969. 5. Asner SA, Desgranges F, Schrijver IT, Calandra T: Impact of the timeliness of antibiotic therapy on the outcome of patients with sepsis and septic shock. J Infect 2021, 82(5):125-134. 6. Goldstone RN, Steinhagen RM: Abdominal Emergencies in Inflammatory Bowel Disease. Surg Clin North Am 2019, 99(6):1141-1150. 7. Biondo S, Ramos E, Deiros M, Rague JM, De Oca J, Moreno P, Farran L, Jaurrieta E: Prognostic factors for mortality in left colonic peritonitis: a new scoring system . J Am Coll Surg 2000, 191(6):635-642. 8. Law WL, Lo CY , Chu KW: Emergency surgery for colonic diverticulitis: differences between right-sided and left-sided lesions. Int J Colorectal Dis 2001, 16(5):280-284. 9. Tan KK, Hong CC, Zhang J, Liu JZ, Sim R: Predictors of outcome following surgery in colonic perforation: an institution's experience over 6 years. J Gastrointest Surg 2011, 15(2):277-284. 10. Lee KY, Lee J, Park YY, Oh ST: Right colon perforation is associated with the higher mortality compared to left colon perforation. Asian J Surg 2021, 44(7):1009-1010. 11. Sartelli M, Catena F, Ansaloni L, Leppaniemi A, Taviloglu K, van Goor H, Viale P, Lazzareschi DV , Coccolini F, Corbella D et al: Complicated intra-abdominal infections in Europe: a comprehensive review of the CIAO study. W orld J Emerg Surg 2012, 7(1):36. 12. Cawcutt KA, Beachy M, Peitz GJ: Antibiotic Delivery in Sepsis: How Do We Improve? Crit Care Med 2020, 48(6):e540-e541. 13. Vaiserman AM, Koliada AK, Marotta F: Gut microbiota: A player in aging and a target for a nti-aging intervention. Ageing Res Rev 2017, 35:36-45. 14. Chu C, Murdock MH, Jing D, Won TH, Chung H, Kressel AM, Tsaava T, Addorisio ME, Putzel GG, Zhou L et al: The microbiota regulate neuronal function and fear extinction learning. Nature 2019, 574(7779):543-548. 15. Yu Q, Newsome RC, Beveridge M, Hernandez MC, Gharaibeh RZ, Jobin C, Thomas RM: Intestinal microbiota modulates pancreatic carcinogenesis through intratumoral natural killer cells . Gut Microbes 2022, 14(1):2112881. 16. Mossad O, Batut B, Yilmaz B, Dokalis N, Mezo C, Nent E, Nabavi LS, Mayer M, Maron FJM, Buescher JM et al : Gut microbiota drives age-related oxidative stress and mitochondrial damage in microglia via the metabolite N(6)-carboxymethyllysine. Nat Neurosci 2022, 25(3):295-305. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint 17. Carvalho MJ, Sands K, Thomson K, Portal E, Mathias J, Milton R, Gillespie D, Dyer C, Akpulu C, Boostrom I et al: Antibiotic resistance genes in the gut microbiota of mothers and linked neonates with or without sepsis from low- and middle-income countries . Nat Microbiol 2022, 7(9):1337-1347. 18. Dickson RP: The microbiome and critical illness. Lancet Respir Med 2016, 4(1):59-72. 19. Kartal E, Schmidt TSB, Molina-Montes E, Rodriguez-Perales S, Wirbel J, Maistrenko OM, Akanni WA, Alashkar Alhamwe B, Alves RJ, Carrato A et al: A faecal microbiota signature with high specificity for pancreatic cancer. Gut 2022, 71(7):1359-1372. 20. Leung H, Long X, Ni Y , Qian L, Nychas E, Siliceo SL, Pohl D, Hanhineva K, Liu Y , Xu A et al: Risk assessment with gut microbiome and metabolite markers in NAFLD development. Sci Transl Med 2022, 14(648):eabk0855. 21. Tan AH, Lim SY , Lang AE: The microbiome-gut-brain axis in Parkinson disease - from basic research to the clinic. Nature Reviews Neurology 2022, 18(8):476-495. 22. Kedia S, Virmani S, S KV , Kumar P, Kante B, Sahu P, Kaushal K, Farooqui M, Singh M, V erma M et al: Faecal microbiota transplantation with anti-inflammatory diet (FMT-AID) followed by anti-inflammatory diet alone is effective in inducing and maintaining remission over 1 year in mild to moderate ulcerative colitis: a randomised controlled trial. Gut 2022, 71(12):2401-2413. 23. Federici S, Kredo-Russo S, V aldes-Mas R, Kviatcovsky D, Weinstock E, Matiuhin Y , Silberberg Y , Atarashi K, Furuichi M, Oka A et al : Targeted suppression of human IBD-associated gut microbiota commensals by phage consortia for treatment of intestinal inflammation . Cell 2022, 185(16):2879-2898 e2824. 24. Sterling SA, Miller WR, Pryor J, Puskarich MA, Jones AE: The Impact of Timing of Antibiotics on Outcomes in Severe Sepsis and Septic Shock: A Systematic Review and Meta-Analysis . Crit Care Med 2015, 43(9):1907-1915. 25. Jiao L, Kourkoumpetis T, Hutchinson D, Ajami NJ, Hoffman K, White DL, Graham DY , Hair C, Shah R, Kanwal F et al : Spatial Characteristics of Colonic Mucosa-Associated Gut Microbiota in Humans. Microb Ecol 2022, 83(3):811-821. 26. Li N, Zuo B, Huang S, Zeng B, Han D, Li T, Liu T, Wu Z, Wei H, Zhao J et al: Spatial heterogeneity of bacterial colonization across different gut segments following inter-species microbiota transplantation. Microbiome 2020, 8(1):161. 27. Amornphimoltham P, Y uen PST, Star RA, Leelahavanichkul A: G ut Leakage of Fungal-Derived Inflammatory Mediators: Part of a Gut-Liver-Kidney Axis in Bacterial Sepsis . Dig Dis Sci 2019, 64(9):2416-2428. 28. Vincent JL, Rello J, Marshall J, Silva E, Anzueto A, Martin CD, Moreno R, Lipman J, Gomersall C, Sakr Y et al : International study of the prevalence and outcomes of infection in intensive care units. JAMA 2009, 302(21):2323-2329. 29. Shrum B, Anantha RV , Xu SX, Donnelly M, Haeryfar SM, McCormick JK, Mele T: A robust scoring system to evaluate sepsis severity in an animal model. BMC Res Notes 2014, 7:233. 30. Dejager L, Pinheiro I, Dejonckheere E, Libert C: Cecal ligation and puncture: the gold standard model for polymicrobial sepsis? Trends Microbiol 2011, 19(4):198-208. 31. Kriwanek S, Armbruster C, Beckerhinn P, Dittrich K: Prognostic factors for survival in colonic perforation. Int J Colorectal Dis 1994, 9(3):158-162. 32. Komatsu S, Shimomatsuya T, Nakajima M, Amaya H, Kobuchi T, Shiraishi S, Konishi S, Ono S, Maruhashi K: Prognostic factors and scoring system for survival in colonic perforation . (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint Hepatogastroenterology 2005, 52(63):761-764. 33. Kwaan MR, Al-Refaie WB, Parsons HM, Chow CJ, Rothenberger DA, Habermann EB: Are right-sided colectomy outcomes different from left-sided colectomy outcomes?: study of patients with colon cancer in the ACS NSQIP database. JAMA Surg 2013, 148(6):504-510. 34. Al-Balas H, Al-Balas M, Al-Wiswasy M: Idiopathic spontaneous cecal perforation: A rare pathology with high mortality. Ann Med Surg (Lond) 2020, 60:518-521. 35. Bozza FA, Salluh JI, Japiassu AM, Soares… M: Cytokine profiles as markers of disease severity in sepsis: a multiplex analysis. Critical Care,11,2(2007-04-21) 2007, 11(2):R49. 36. Wang H, Tumes DJ, Hercus TR, Yip KH, Aloe C, Vlahos R, Lopez AF, Wilson N, Owczarek CM, Bozinovski S: Blocking the human common beta subunit of the GM-CSF, IL-5 and IL-3 receptors markedly reduces hyperinflammation in ARDS models. Cell Death Dis 2022, 13(2):137. 37. Harbarth S, Holeckova K, Froidevaux C, Pittet D, Ricou B, Grau GE, V adas L, Pugin J: Diagnostic value of procalcitonin, interleukin-6, and interleukin-8 in critically ill patients admitted with suspected sepsis. Am J Respir Crit Care Med 2001, 164(3):396-402. 38. Jin H, Aziz M, Murao A, Kobritz M, Shih AJ, Adelson RP, Brenner M, Wang P: Antigen-presenting aged neutrophils induce CD4+ T cells to exacerbate inflammation in sepsis . J Clin Invest 2023, 133(14). 39. Chancharoenthana W, Sutnu N, Visitchanakun P, Sawaswong V , Chitcharoen S, Payungporn S, Schuetz A, Schultz MJ, Leelahavanichkul A: Critical roles of sepsis-reshaped fecal virota in attenuating sepsis severity. Front Immunol 2022, 13:940935. 40. Chancharoenthana W, Kamolratanakul S, Schultz MJ, Leelahavanichkul A: The leaky gut and the gut microbiome in sepsis - targets in research and treatment. Clin Sci (Lond) 2023, 137(8):645-662. 4 1 . W e i D , M a P , F a n Q , Y u H , P e n g Y , L i X : Y anning Syrup ameliorates the lipopolysaccharide-induced inflammation: Adjusting the gut microbiota, short-chain fatty acids, and the CD4(+) T cell balance. J Ethnopharmacol 2022, 283:114729. 42. Ma Y , Zhu L, Ke H, Jiang S, Zeng M: Oyster (Crassostrea gigas) polysaccharide ameliorates obesity in association with modulation of lipid metabolism and gut microbiota in high-fat diet fed mice. Int J Biol Macromol 2022, 216:916-926. 43. Huang H, Chen J, Chen Y , Xie J, Xue P, Ao T, Chang X, Hu X, Yu Q: Metabonomics combined with 16S rRNA sequencing to elucidate the hypoglycemic effect of dietary fiber from tea residues . Food Res Int 2022, 155:111122. 44. Wang T, Y e Y , Ji J, Zhang S, Yang X, Xu J, Wang JS, Chen Z, Xia B, Shen H et al : Astilbin from Sm ilax glabra Roxb. alleviates high-fat diet-induced metabolic dysfunction . Food Funct 2022, 13(9):5023-5036. 45. Wang H, Liu Y , Li W, Li W, Xu H, Niu G, Wang Z: Microbiota in Gingival Crevicular Fluid Before and After Mechanical Debridement With Antimicrobial Photodynamic Therapy in Peri-Implantitis. Front Cell Infect Microbiol 2021, 11:777627. 46. Figueiredo CA, Marques CR, Costa Rdos S, da Silva HB, Alcantara-Neves NM: Cytokines, cytokine gene polymorphisms and Helicobacter pylori infection: friend or foe? World J Gastroenterol 2014, 20(18):5235-5243. 47. Chen H, Ye C, Cai B, Zhang F, Wang X, Zhang J, Zhang Z, Guo Y, Yao Q: Berberine inhibits intestinal carcinogenesis by suppressing intestinal pro-inflammatory genes and oncogenic factors through modulating gut microbiota. BMC Cancer 2022, 22(1):566. 48. Sun Y , Wu D, Zeng W, Chen Y , Guo M, Lu B, Li H, Sun C, Y ang L, Jiang X et al : The Role of (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint Intestinal Dysbacteriosis Induced Arachidonic Acid Metabolism Disorder in Inflammaging in Atherosclerosis. Front Cell Infect Microbiol 2021, 11:618265. 49. V an Hecke T , De V rieze J, Boon N, De V os WH, V ossen E, De Smet S: Combined Consumption of Beef-Based Cooked Mince and Sucrose Stimulates Oxidative Stress, Cardiac Hypertrophy, and Colonic Outgrowth of Desulfovibrionaceae in Rats. Mol Nutr Food Res 2019, 63(2):e1800962. 50. Armougom F, Raoult D: Use of pyrosequencing and DNA barcodes to monitor variations in Firmicutes and Bacteroidetes communities in the gut microbiota of obese humans . BMC Genomics 2008, 9:576. 51. Spychala MS, V enna VR, Jandzinski M, Doran SJ, Durgan DJ, Ganesh BP, Ajami NJ, Putluri N, Graf J, Bryan RM et al: Age-related changes in the gut microbiota influence systemic inflammation and stroke outcome. Ann Neurol 2018, 84(1):23-36. 52. Mulder M, Radjabzadeh D, Kiefte-de Jong JC, Uitterlinden AG, Kraaij R, Stricker BH, V erbon A: Long-term effects of antimicrobial drugs on the composition of the human gut microbiota . Gut Microbes 2020, 12(1):1795492. 53. Haak BW, Wiersinga WJ: The role of the gut microbiota in sepsis . Lancet Gastroenterol Hepatol 2017, 2(2):135-143. 54. Xu K, Li H, Zhang B, Le M, Huang Q, Fu R, Croppi G, Qian G, Zhang J, Zhang G et al: Integrated transcriptomics and metabolomics analysis of the hippocampus reveals altered neuroinflammation, downregulated metabolism and synapse in sepsis-associated encephalopathy. Front Pharmacol 2022, 1. 55. Brunt EM: Grading and staging the histopathological lesions of chronic hepatitis: the Knodell histology activity index and beyond. Hepatology 2000, 31(1):241-246. 56. Desmet VJ: Knodell RG, Ishak KG, Black WC, Chen TS, Craig R, Kaplowitz N, Kiernan TW, Wollman J. Formulation and application of a numerical scoring system for assessing histological activity in asymptomatic chronic active hepatitis [Hepatology 1981;1:431-435] . J Hepatol 2003, 38(4):382-386. 57. Gaut JP, Liapis H: Acute kidney injury pathology and pathophysiology: a retrospective review . Clin Kidney J 2021, 14(2):526-536. 58. Matute-Bello G, Downey G, Moore BB, Groshong SD, Matthay MA, Slutsky AS, Kuebler WM: An official American Thoracic Society workshop report: features and measurements of experimental acute lung injury in animals. Am J Respir Cell Mol Biol 2011, 44(5):725-738. 59. Lu Y , Wan J, Y ang Z, Lei X, Niu Q, Jiang L, Passtoors WM, Zang A, Fraering PC, Wu F: Regulated intramembrane proteolysis of the AXL receptor kinase generates an intracellular domain that localizes in the nucleus of cancer cells. F ASEB J 2017, 31(4) :1382-1397. 60. Lu Y, Zhang W, Zhang B, Heinemann SH, Hoshi T, Hou S, Zhang G: Bilirubin Oxidation End Products (BOXes) Induce Neuronal Oxidative Stress Involving the Nrf2 Pathway . Oxid Med Cell Longev 2021, 2021:8869908. 61. Xu K, Li H, Zhang B, Le M, Huang Q, Fu R, Croppi G, Qian G, Zhang J, Zhang G et al: Integrated transcriptomics and metabolomics analysis of the hippocampus reveals altered neuroinflammation, downregulated metabolism and synapse in sepsis-associated encephalopathy. Front Pharmacol 2022, 13:1004745. 62. Erttmann SF, Swacha P, Aung KM, Brindefalk B, Jiang H, Härtlova A, Uhlin BE, Wai SN, Gekara NO: The gut microbiota prime systemic antiviral immunity via the cGAS-STING-IFN-I axis. Immunity 2022, 55(5):847-861.e810. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint 63. Li Y , Luo ZY , Hu YY , Bi YW, Yang JM, Zou WJ, Song YL, Li S, Shen T, Li SJ et al : The gut microbiota regulates autism-like behavior by mediating vitamin B(6) homeostasis in EphB6-deficient mice. Microbiome 2020, 8(1):120. 64. Bolger AM, Lohse M, Usadel B: Trimmomatic: a flexible trimmer for Illumina sequence data . Bioinformatics 2014, 30(15):2114-2120. 65. Reyon D, Tsai SQ, Khayter C, Foden JA, Sander JD, Joung JK: FLASH assembly of TALENs for high-throughput genome editing. Nat Biotechnol 2012, 30(5):460-465. 66. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI et al : QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010, 7(5):335-336. 6 7 . R o g n e s T , F l o u r i T , N i c h o l s B , Q u i n c e C , M a h e F : VSEARCH: a versatile open source tool for metagenomics. PeerJ 2016, 4:e2584. 68. Wang Q, Garrity GM, Tiedje JM, Cole JR: Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 2007, 73(16):5261-5267. 69. Chao A, Bunge J: Estimating the number of species in a stochastic abundance model . Biometrics 2002, 58(3):531-539. 70. Hill TC, Walsh KA, Harris JA, Moffett BF: Using ecological diversity measures with bacterial communities. FEMS Microbiol Ecol 2003, 43(1):1-11. Figures and figure legends Figure 1. Study design and survival rates monitering. (A) Study design including treatment and sampling times . (B) Survival rates during the fecal-induced peritonitis (FIP)-induced sepsis. Saline, cecum, and colon show the mice injected intraperitoneally with their respective saline or IC aliquots. ****P < 0.0001, indicating a significant difference between the cecum (n=9) and colon (n=10) based on the Mantel–Cox test. Figure 2. Blood biochemical indicators altered during the FIP-induced sepsis. (A -F) Mice were intraperitoneally injected with saline or ICs from fecal slurry (2 mg/kg) or colon slurry (2 mg/kg), and 16 hours later, plasma was analyzed for amount of (A) alanine aminotransferase (AL T), ( B) aspartate aminotransferase (AST), ( C) Lactate dehydrogenase (LDH), ( D) Blood urea nitrogen (BUN), ( E) creatinine, and (F) creatine kinase (CK) (each dot represents one mouse, n = 3 to 4 per group); Data represent as means ± SEM with p-value or not, * P < 0.05, ns, no significant; one-way ANOVA with post-t-test. Figure 3. Blood plasma cytokines and chemokines increased during FIP-induced sepsis. Mice (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint were intraperitoneally injected with saline or ICs from fecal slurry (2 mg/kg) or colon slurry (2 mg/kg), and 16 hours later, plasma was analyzed for the amount of cytokine or chemokine levels (each dot represents one mouse, n = 3 to 4 per group). Data are shown as means ± SEM. p value are shown or not, *p < 0.05, **p < 0.01, ***p < 0.001, ns, no significant; one-way ANOV A with post-t-test. Figure 4. Histology altered in the cecum FIP mice. (A) Representative hematoxylin and eosin (H&E) staining and clinical score data for the liver, lung and kidney from the mice peritoneally injected with saline or intestinal contents (ICs) of the cecum or colon (2g/kg body weight) at 16 h post-injection. Scale bar, 100 μ m; (B-D) The graph shows clinical scores of the liver (B), lung (C) and kidney ( D), respectively. ( E) Ngal mRNA expression measured by qRT-PCR. Data are shown as means ± SEM (n=4, each dot represents one mouse). *p<0.05, **p<0.01, and indicated p-value are shown as determined by the Kruskal-Wallis test on the clinical score and one-way ANOVA with post-t-test on the mRNA expression. Figure 5. ICs of the cecum induce lung inflammation. (A-D) Representative image and average data for Ly6G, TNF α and ZO-1 in the lung tissues of mice peritoneally injected with saline or ICs of the cecum or colon at a dose of 2g/kg bodyweight at 16 h post-injection. Blue, DAPI; Red, ZO-1; Pink, TNF α ; Green, Ly6G. Scale bar, 200 μ m. The graph shows quantification data of ( A) as the percentage of Ly6G- positive ( B), TNF α -positive number cells ( C) and ZO-1 staining area ( D), respectively. Data are shown as means ± SEM (n=4, each dot represents one mouse). *p<0.05, **p<0.01, ns, no significant, one-way ANOV A with post-t-test. Figure 6. Microbiome community difference in the cecum and colon. (A ) qPCR quantification of bacterial 16S rDNA in ICs of donor mice. Data presented as means ±SD (n = 5). ( B) The flower plot of the operational taxonomic units (OTUs) identified in the ICs of the cecum and colon. ( C) The principle coordinate analysis (PCoA) ordination of the Bray–Curtis distances between the cecum and colon based on Adonis bray_curtis (P < 0.001). ( D, E) Relative abundance of bacterial phylum and genus in ICs from cecum and colon. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint Figure 7. Linear discriminant analysis (LDA) Effect size analysis and the relative abundance of potentially pathogenic bacteria enriched in the ICs of the cecum. (A) Cladogram of the linear discriminant analysis of the effect size (LEfSe) of the microbiome from the cecum and colon ICs. Red and green circles represent the differences between the most abundant microbiome class. The diameter of each circle was proportional to the relative abundance of the taxon. ( B) Histogram of the LDA scores for different abundant genera in the cecum and colon ICs. Red, enriched in Cecum ICs; Green, enriched in Colon ICs. (C-I) The relative abundance of bacteria, including Firmicutes (C), Helicobacter (D), Mucispirillum (E), and Lachnospiraceae_FCS020_group (F), the family Desulfovibrionaceae (G), Desulfovibrio (H), and Lachnoclostridium ( I). ( J) The Firmicutes to Bacteroidota (F/B) ratio in the cecum group was higher than in the colon group. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 1, 2024. ; https://doi.org/10.1101/2024.02.26.582076doi: bioRxiv preprint