Effects of Saccharomyces boulardii on the characteristics and metabolomics of the gut microbiota in patients with liver cirrhosis

Effects of Saccharomyces boulardii on the characteristics and metabolomics of the gut microbiota in patients with liver cirrhosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of Saccharomyces boulardii on the characteristics and metabolomics of the gut microbiota in patients with liver cirrhosis Wei Wei, Peng Chen, Qing Ye, Yu Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7501319/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background : Metabolism serves an important role in the gut microbiota and regulating the progression of liver cirrhosis. The present study investigated the characteristics of the gut microbiota and associated metabolites in patients with liver cirrhosis. Methods : The characteristics of the gut microbiota and fecal metabolites in patients with liver cirrhosis from January 2019 to December 2022 were analyzed using 16S rRNA sequencing and metabolomics with bioinformatic analysis. The effect of Saccharomyces boulardii on the gut microbiota and phenotype of patients with liver cirrhosis was examined. Results : It was demonstrated that there was a low similarity and diversity of the gut microbiota in the patients with liver cirrhosis compared with the normal group. Microorganisms such as Veillonella , Streptococcus , Blautia and Faecalibacterium were significantly correlated with the liver function index, which may serve an important role in abnormal amino acid biosynthesis and metabolism associated with liver cirrhosis progression, as demonstrated through functional prediction and metabolomics. The abnormal intestinal microorganism and serum ammonia levels was decreased in patients with liver cirrhosis after S. boulardii treatment. Conclusion : Therefore, the abnormal amino acid metabolism and serum ammonia levels were induced by gut microbiota disorder in patients with liver cirrhosis and probiotic treatment alleviated this. gut microbiota liver cirrhosis Saccharomyces boulardii Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Liver cirrhosis is a chronic progressive liver disease associated with liver damage. A variety of factors could lead to cirrhosis, such as viral hepatitis, drug-induced hepatitis, autoimmune hepatitis and fatty hepatitis (Yuan et al. 2019 ; Yip et al. 2020). The gut microbiota serves an important role in human health by maintaining internal environment stability (Fan et al. 2021 ). A disturbance of the gut microbiota associated with an imbalance of pathogenic and beneficial bacteria has been previously reported in patients with liver cirrhosis. Additionally, fecal transfer from patients with liver cirrhosis could lead to abnormal liver function in mice, suggesting that abnormal gut microbiota may be a factor associated with liver cirrhosis. 7 The gut microbiota is formed of a number of complex and abundant species in the human body and produces a variety of metabolites which affect human health (Albhaisiet al. 2020 ; Albillos et al. 2020 ; Shen et al. 2023). Hepatic encephalopathy (HE) is a symptom of end-stage liver disease with nervous function disorder associated with high morbidity and mortality, as 60–80% of patients with decompensated liver cirrhosis developed HE with a 1 year survival rate of 20–42% and a 3 year survival rate of 15–23%.13 A number of previous studies have shown that the HE is related to the presence of aromatic amino acids in the serum, such as phenylalanine, cucine and tryptophan, and the role of the gut-liver-brain axis in the pathogenesis of nervous dysfunction associated with liver disease (Giuli et al. 2023 ). However, although the liver cirrhosis was correlated with abnormal gut microbiota and metabolism, some questions remain unclear. For example, the type of microbes that are associated with the progression or diagnosis of patients with liver cirrhosis, the metabolic pathways that are altered in these patients, the connections between intestinal microorganisms and serum ammonia levels and whether probiotic treatment can regulate the imbalanced gut microbiota and aberrant serum ammonia levels. The present study investigated the aforementioned issues by analyzing the characteristic gut microbiota and associated metabolic pathways of patients with liver cirrhosis. Additionally, the relationship between intestinal microbes and liver function phenotypes were explored. Saccharomyces boulardii has been used for the treatment of various liver disorders with reducing the severity of many manifestations of cirrhosis and metabolic dysfunction (Maslennikov et al. 2023). These results provided a potential basis for the use of Saccharomyces boulardii in the treatment of cirrhosis. Materials and methods Patients. A total of 22 patients with compensatory liver cirrhosis (COM group) and 26 patients with decompensatory liver cirrhosis (DECOM group) diagnosed at the Tianjin Second People’s Hospital from January 2019 to December 2020 were selected for inclusion in the present study and the Child-Pugh scores of these patients were analyzed. A total of 21 healthy volunteers were selected as the NORMAL group. A total of 16 patients with decompensatory liver cirrhosis diagnosed from January 2021 to December 2022 were selected for S. boulardii treatment, including 6 patients in the conventional treatment group (patients 1–6) given conventional treatment and 10 patients selected for both the S. boulardii and conventional treatment (patients 7–16). The latter group were given S. boulardii (cat. no. S20150051; batch no. 3393; Biocodex) at 0.5 g twice daily, in addition to the conventional treatment. Patients in both groups were given continuous medication for 28 days. The Committee of Tianjin Second People’s Hospital (#2017-26) and the study was performed according to the principles of the Declaration of Helsinki. All patients provided written informed consent prior to participation in the study. Diagnostic criteria. Patients were diagnosed with cirrhosis using imaging techniques such as ultrasonography, CT or MRI. Positive B-scan ultrasonography suggested the presence of ascites whereas positive CT results showed ascites of cirrhosis. Endoscopic or esophageal barium swallowing X-ray examination showed esophageal and gastric fundus varices. Inclusion criteria . The inclusion criteria used were as follows: i) Patients who had not received other clinical treatment within 1 month; ii) both patients and healthy volunteers lived in Tianjin, China; and iii) healthy volunteers were included based on normal body function. Exclusion criteria . Both patients and healthy volunteers was excluded as follows: i) Diagnosis of recent concurrent hepatic encephalopathy, gastrointestinal massive hemorrhage, infection and liver cancer; ii) fatty liver disease, hypertension, diabetes and other systemic diseases; iii) antibiotic use up to 4 weeks before enrollment; iv) patients with peritonitis; v) patients with alcoholic hepatitis, non-alcoholic steatohepatitis, autoimmune liver disease, genetic metabolic liver disease, schistosomiasis, drug-induced hepatitis and other causes of cirrhosis. Genomic DNA amplification. A total of 500 mg of feces were taken from patients and healthy volunteers and stored in sterile tubes at -20˚C. Total RNA was extracted using the fecal genome extraction kit (Beijing Tiangen Biochemistry Technology Co., Ltd.) and the variable region of the bacterial 16S rRNA gene V4 was amplified by PCR. High-fidelity PCR premixture (New England Biolabs, Inc.) of Phusion®GC buffer solution and high-efficient high-fidelity enzymes were used. The V4 variable region of the bacterial 16S rRNA gene was amplified by PCR using barcode primers 514F, 5'-GTGCCAGCMGCCGCGGTAA-3' and 805R, 5'-GGACTACHVGGGTWTCTAAT-3). The thermocycling protocol used was as follows: 94˚C for 2 min, followed by 30 cycles of 94˚C for 30 sec, 52˚C for 30 sec and 72˚C for 45 sec, then 72˚C for 5 min. Each 25 µl PCR reaction system consisted of 0.5 µl template DNA, 2.0 µl dNTP mixture (2.5 mM; TaKaRa), 2.5 µl non-10×Mg 2 + Ex Taq buffer, 1.5 µl Mg 2+ (25 mM), 0.25 µl Lex Taq DNA polymerase (2.5 units), 0.5 µl 10 mol/l primer 514F, 0.5 µl 10 mol/l primer 805R and 17.25 µl water. Sterile double-steamed water was used for dilution. The library was constructed using the TruSeq® DNA PCR-Free Sample Preparation Kit and the variable region of the bacterial rRNA gene V4 was sequenced using the HiSeq2500 PE250 platform. 21 The fastx_clipper tool was used to removed the adaptor sequences and filtered the data such as Phred score threshold or minimal nucleotide, fastx_collapser was used to removed the duplication in fastx_toolkit. Laboratory testing . Fasting venous blood was extracted from the patients and centrifugated at 4,000 r/min at 4˚C for 10 min. Serum was collected for glutamic pyruvic transaminase (ALT, Beckman Coulter, USA, #AUZ3363), total bilirubin (TBIL, Nipro, Japan, #H4U04) and albumin (ALB, Beijing Leadman Biochemistry Co., Ltd, China, #24061204) detection, which were measured by the biochemical method adopted by the Hitachi 7180 series full automatic biochemical analyzer test. Additionally, the prothrombin time (PT, Siemens Healthcare Diagnostics Products Co., Ltd, Germany, #572143) was measured using the Japan Sysmex prothrombin analyzer test and blood ammonia (AMM, Ortho-Clinical Diagnostics, inc., USA, #1726926) was measured by the American VITROS 350 biochemical analyzer test. Venous blood was extracted and platelets (PLTs, Sysmex Co., Ltd, Japan, Fluorocell WDF#CV-377-552, Fluorocell WNR#CP-066-715) were detected using the Sysmex XN-10 hematology analyzer. Metabolomics analysis. 100 mg fresh fecal samples were ground and homogenized with liquid nitrogen, then suspended in 80% methanol (-20˚C). Samples were incubated at -20˚C for 60 min and centrifuged at 14,000 x g at 4˚C for 20 min. The supernatant was transferred to a fresh tube and placed in a vacuum concentrator to be rotated for drying. The dried metabolites were mixed with 60% methanol and analyzed using liquid/gas chromatography-mass spectrometry (LC-MS/MS). LC-MS/MS analysis was performed using the Vanquish UHPLC system (Thermo Fisher) and the Orbitrap Q ExActive HF-X mass spectrometer (Thermo Fisher) in the data-dependent acquisition mode. Samples were injected into a Hyperil Gold column and eluted for 16 min at a linear gradient flow rate of 0.3 ml/min. The eluents in positive polarity mode were A (0.1% FA aqueous solution) and B (methanol). The eluents in negative polarity mode were A (5 mM ammonium acetate; pH 9.0) and B (methanol). The solvent gradients were as follows: A, 98% + B, 2%, 1.5 min; B, 100%, 12.0 min; B, 100% 14.0 min; A + B, 2% 98% 14.1 min; 98% + 2% B, 16 min. Q ExActive HF-X mass spectrometer was operated in the positive/negative polarity mode with a spray voltage of 3.2 kV, capillary temperature of 320˚C, sheath gas flow rate of 35 arB and an auxiliary gas flow rate of 10 arB. The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.0 (Thermo Fisher) to compare, screen and quantify the peaks value of each metabolite. The main parameters were set as follows: Retention time tolerance, 0.2 min; actual mass tolerance, 5 ppm; signal strength tolerance, 30%; signal-to-noise ratio, 3; minimum strength, 100,000. The peak intensities were converted to total spectral intensities for predicting molecular formulations based on addition ions, molecular ion peaks and fragmentation ions. The peaks were matched with mzCloud ( https://www.mzcloud.org/ ) and ChemSpider databases ( http://www.chemspider.com/ ) to achieve accurate qualitative and relative quantitative results. Statistical analysis . Data were expressed as x ± s and analyzed using SPSS 11.0 software. Statistical differences were determined by one way ANOVA analysis with Tukey’s post hoc test for normally distributed data and the Mann-Whitney test for data that did not conform to a normal distribution. R software (version 2.15.3) was used for principal coordinate analysis (PCoA). The Pearson method was used for correlation analysis (r>|0.3|; P < 0.05 for correlation). The Tax4Fun software package and Matlab software were used to construct the Thiessen polygon for the function prediction of the gut microbiota. Variable Importance in the Projection (VIP) of the first principal component of the PLS-DA model was used to search for differentially expressed metabolites. The differentially expressed metabolites were screened out by setting the threshold of VIP > 2.0, the differentially expressed multiple fold-change (FC) BBB > 0 or FC < 0.5 and P < 0.05. The gene ontology (GO) information of the altered expression protein levels was analyzed using David software (Release 6.8). GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to bioinformatics analysis. The significant difference threshold was set as follows: Enrichment ≥ 2.0, multiple enrichment ≥ 2.0 and the Benjamini correction P < 0.05. Cytoscape software (version 3.3.0) was used to build the network. Recevier operating curve (ROC) analysis was performed with GraphPad Prism 6.0. P < 0.05 was considered to indicate a statistically significant difference. Results Structure and characteristics of gut microbiota in patients with liver cirrhosis. PCoA analysis was used to analyze the similarity of microbial community structure. Although the intestinal microbial community structure of the COM and DECOM groups had a low similarity, the normal and COM group were overlapped (Fig. 1A1). There was two part in DECOM group with significant different community structure via PCoA analysis, therefore, the DECOM1 and DECOM2 groups was established based on the PC1 -0.34 in PCoA analysis of the DECOM group in order to explored the relationships between liver cirrhosis process and gut microbiota alteration (Fig. 1A2). It was demonstrated that the AMM level of the DECOM2 group was significantly higher compared with that of the COM group (Fig. 1 B; other clinical phenotypes were shown in Table 1 ). Meanwhile, there was a similar tendency in the weighted unweighted pair-group method with the arithmetic mean and heat map analysis for β diversity (Fig. 2 B-C), suggesting that gut microbiota disorder was related to the disturbance of amino acid metabolism. Table 1 Summary of clinical phenotype Phenotype Mean ± sd in NORMAL Mean ± sd in COM Mean ± sd in DECOM Mean ± sd in DECOM1 Mean ± sd in DECOM2 Sex male = 15, female = 6 male = 17, female = 5 male = 19, female = 7 male = 9, female = 4 male = 10, female = 3 Age 52.1 ± 7.8 48.2 ± 8.5 50.6 ± 11.3 51.3 ± 11.6 49.8 ± 11.4 Child-Pugh 5.1 ± 0.3 5.3 ± 0.6 9.1 ± 2.3 9.0 ± 2.2 9.2 ± 2.5 ALT (U/L) 19.7 ± 9.6 79.9 ± 100.5 62.9 ± 71.5 80.7 ± 85.2 38.6 ± 38.4 TBIL (µM) 12.9 ± 4.6 21.8 ± 10.0 52.3 ± 38.3 45.3 ± 30.8 61.9 ± 46.4 ALB (g/L) 45.3 ± 2.5 42.2 ± 6.3 32.5 ± 5.9 33.1 ± 7.1 31.7 ± 3.9 PT (sec) 11.4 ± 0.7 14.6 ± 1.5 17.9 ± 2.8 17.9 ± 2.9 18.0 ± 2.9 PLT 242.7 ± 60.2 94.6 ± 46.1 78.4 ± 48.0 89.2 ± 58.7 63.6 ± 22.9 AMM (µM) 5.8 ± 1.1 23.1 ± 15.0 32.6 ± 16.1 29.8 ± 13.8 36.5 ± 18.8 P-value (NORMAL vs. COM) P-value (NORMAL vs. DECOM) P-value (COM vs. DECOM) P-value (NORMAL vs. DECOM1) P-value (NORMAL vs. DECOM2) Sex 0.6606 0.9000 0.7380 0.8913 0.7242 Age 0.1252 0.6080 0.4171 0.8111 0.4892 Child-Pugh 0.0296 < 0.0001 < 0.0001 < 0.0001 < 0.0001 ALT (U/L) 0.0093 0.0087 0.4986 0.0025 0.0385 TBIL (µM) 0.0005 < 0.0001 0.0007 < 0.0001 < 0.0001 ALB (g/L) 0.0414 < 0.0001 < 0.0001 < 0.0001 < 0.0001 PT (sec) < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 PLT < 0.0001 < 0.0001 0.2416 < 0.0001 < 0.0001 AMM (µM) < 0.0001 < 0.0001 0.0407 < 0.0001 < 0.0001 P-value (COM vs. DECOM1) P-value (COM vs. DECOM2) P-value (DECOM1 vs. DECOM2) Sex 0.5989 0.9810 0.6584 Age 0.3696 0.6389 0.7424 Child-Pugh < 0.0001 < 0.0001 0.8442 ALT (U/L) 0.9799 0.2013 0.1415 TBIL (µM) 0.002 0.0004 0.2819 ALB (g/L) 0.0002 < 0.0001 0.5771 PT (sec) < 0.0001 < 0.0001 0.9083 PLT 0.7584 0.0446 0.1833 AMM (µM) 0.1762 0.0346 0.3097 There were 162 unique operational taxonomic units (OTUs) in the COM group, 167 in the DECOM group, 89 in the DECOM1 group and 51 in the DECOM2 group, according to the Venn analysis (Fig. 1 C). According to the species annotations and abundance information, it was demonstrated that there was a lower α diversity by chao1 in the DECOM group compared with that in the NORMAL group (Fig. 1 D). LDA effect size (LEFSE) analysis was used to screen marker microorganism for liver cirrhosis (Fig. 1E1 and 1E2), and the correlation of marker microorganism with the Child-Pugh score and ALT, TBIL, ALB, PT, PLT and AMM levels was analyzed (Fig. 1E3 and 1E4; Table 2 ). ROC analysis also demonstrated diagnostic efficiency based on marker microorganism such as Veillonella , Streptococcus , Blautia and Faecalibacterium for patients with liver cirrhosis (Fig. 2 A; Table 3 ). Table 2 The Spearman’s correlation of clinical index and significant different bacterial Child-Pugh ALT TIBL ALB PT PLT AMM phylum Firmicutes -0.1802 0.0637 -0.2601* 0.2937* -0.1730 0.3013* -0.2737* Proteobacteria 0.3602* -0.0930 0.4270* -0.4109* 0.3679* -0.3526* 0.3469* Actinobacteria 0.145 -0.158 0.191 -0.1740 0.003 0.057 -0.195 class Clostridia -0.7054* -0.0762* -0.5884* 0.6624* -0.592* 0.4943* -0.4703* Gamma proteobacteria 0.3733* -0.0860 0.4399* -0.4291* 0.3825* -0.3659* 0.3590* Negativicutes 0.2182 0.0038 0.1415 -0.4008* 0.2138 -0.2294 0.2751* Bacilli 0.6697* 0.1754 0.4495* -0.3888* 0.5219* -0.2287 0.2103 unidentified_Actinobacteria -0.0757 0.1490 -0.0725 0.1608 -0.1672 0.1171 -0.0595 order Clostridiales -0.7054* -0.0762 -0.5884* 0.6624* -0.5920* 0.4943* -0.4703* Enterobacteriales 0.3524 -0.0897 0.3933* -0.4011* 0.3834* -0.3525* 0.3393* Selenomonadales 0.2182 0.0038 0.1415 -0.4008* 0.2138 -0.2294 0.2751* Lactobacillales 0.6706* 0.1758 0.4504* -0.3896* 0.5231* -0.2296 0.2117 Bacteroidales -0.1954 -0.0684 -0.2012 0.0439 -0.1721 -0.0084 -0.1187 Bifidobacteriales 0.150 -0.147 0.195 -0.173 0.003 0.055 -0.181 family Enterobacteriaceae 0.3524 -0.0897 0.3933* -0.4011* 0.3834* -0.3525* 0.3393* Ruminococcaceae -0.4893* -0.0528 -0.4493* 0.4346* -0.4193* 0.3734* -0.4199* Veillonellaceae 0.2246 0.0076 0.1486 -0.4084* 0.2233 -0.2337 0.2770* Streptococcaceae 0.6633* 0.2068 0.4210* -0.4576* 0.4991* -0.2597* 0.2357 Lachnospiraceae -0.5893* -0.0576 -0.4644* 0.5717* -0.4853* 0.3470* -0.2981* Bifidobacteriaceae -0.0737 0.1519 -0.0651 0.1484 -0.1506 0.1060 -0.0448 Lactobacillaceae 0.3190* -0.0248 0.2449* -0.0845 0.2872* -0.0704 0.0621 Bacteroidaceae -0.1954 -0.0684 -0.2012 0.0439 -0.1721 -0.0084 -0.1187 genus Faecalibacterium -0.3325* -0.0784 -0.3069* 0.2532* -0.2575* 0.1251 -0.2594* Megamonas 0.0393 0.0474 0.0404 -0.2769* 0.0980 -0.1809 0.3009* Streptococcus 0.6687* 0.2002 0.4238* -0.4628* 0.5034* -0.2659* 0.2389* unidentified_Enterobacteriaceae 0.3576* -0.0708 0.1737 -0.2449* 0.3419* -0.2793* 0.3002* Lactobacillus 0.3204* -0.0248 0.2448* -0.0845 0.2871* -0.0705 0.0623 Bifidobacterium 0.150 -0.146 0.195 -0.173 0.003 0.055 -0.181 Blautia 0.231 -0.004 -0.103 -0.261 0.083 -0.030 -0.030 Veillonella 0.4489* 0.0353 0.3305* -0.3946* 0.3041* -0.2502* 0.1286 unidentified_Ruminococcaceae -0.4735* -0.1042 -0.3699* 0.4778* -0.393* 0.3245* -0.2845* unidentified_Lachnospiraceae -0.4427* -0.1353 -0.2685* 0.421* -0.3463* 0.2842* -0.1264 Roseburia -0.2188 0.0486 -0.1767 0.1763 -0.1452 -0.1154 -0.0227 Bacteroides -0.1954 -0.0684 -0.2012 0.0439 -0.1721 -0.0084 -0.1187 Subdoligranulum -0.3449* 0.0262 -0.2503* 0.3797* -0.2997* 0.2299 -0.2719* species Escherichia_coli 0.3608* -0.0693 0.1751 -0.2451* 0.3430* -0.2781* 0.2994* Streptococcus_salivarius_subsp -0.104 -0.2080 0.246 0.021 0.268 -0.001 -0.196 Lactobacillus_salivarius -0.3260 0.0000 -0.2810 0.2670 -0.2650 -0.0480 -0.0570 Ruminococcus_sp_5_1_39BFAA -0.0615 -0.0025 -0.0883 0.1420 -0.1747 0.2319 0.0127 Bifidobacterium_adolescentis -0.2140 -0.0787 -0.1585 0.2013 -0.1940 0.1964 -0.1342 Staphylococcus_salivarius_subsp_thermophilus 0.5083* 0.1713 0.3026* -0.3709* 0.4093* -0.2041 0.3219* Bacteroides_uniformis -0.2119 -0.1286 -0.1947 0.2469* -0.2855* 0.2131 -0.2243 Roseburia_inulinivorans -0.0863 -0.0354 -0.0781 0.0032 -0.0241 -0.1978 -0.0263 * , P < 0.05 Table 3 The ROC of gut microbiota in liver cirrhosis COM AUC sensitivity (%) specificity (%) cut-off g_bacteroides 0.5931 50 81 0.1305 g_blautia 0.6082 55 71 0.0394 g_faecalibacterium 0.5065 50 62 0.1709 g_streptococcus 0.5801 45 81 0.0126 g_veillonella 0.5400 45 76 0.0004 DECOM AUC sensitivity (%) specificity (%) cut-off g_bacteroides 0.6355 23 95 0.0056 g_blautia 0.7949 58 95 0.0208 g_faecalibacterium 0.707 23 95 0.0197 g_streptococcus 0.859 62 95 0.0594 g_veillonella 0.8846 81 95 0.0027 DECOM1 AUC sensitivity (%) specificity (%) cut-off g_bacteroides 0.5683 53 62 0.0347 g_blautia 0.7683 47 95 0.0208 g_faecalibacterium 0.5746 53 76 0.0843 g_streptococcus 0.9238 73 95 0.0594 g_veillonella 0.9429 80 95 0.0035 DECOM2 AUC sensitivity (%) specificity (%) cut-off g_bacteroides 0.7273 45 95 0.0054 g_blautia 0.8312 73 95 0.0181 g_faecalibacterium 0.8874 55 95 0.0197 g_streptococcus 0.7706 73 81 0.014 g_veillonella 0.8052 73 95 0.0035 Function prediction of gut microbiota in patients with liver cirrhosis . Tax4Fun is a bioinformatics software package used to predict the gut microbiota function. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUST) was used to predict the bacterial phenotypes based on the genetic information of OTUs in the Greengene database. The distribution and heterology of bacterial function between the normal and cirrhosis groups was shown by Venn and PCA analysis (Fig. 3 A and B). It was predicted that the regulation of carbohydrate metabolism and amino acid biosynthesis and metabolism was an important function for the gut microbiota by heat map and KEGG analyses (Fig. 3 C and D), suggesting a correlation between gut microbiota and blood ammonia levels. Metabolomics analysis of feces of patients with liver cirrhosis. To certify the functions predicted by PICRUST, the metabolites in feces were analyzed by metabolomics (Table 4 ). PCA analysis of fecal metabolites also showed the development of liver cirrhosis, further suggesting the important role of fecal metabolites in the function of gut microbiota (Fig. 4 A). The biosynthesis and metabolism of amino acids, phenylalanine and purine, enzymes such as xanthine dehydrogenase and acetyl-CoA synthetase may serve an important role in the development of liver cirrhosis based on metabolite-protein network analysis (Fig. 4 B). These data suggested that there was an abnormal metabolism of fatty acids, amino acids, bile acids and vitamins in patients with liver cirrhosis. Table 4 The regulated number of metabolite in feces Compared Samples Num. of Total Ident. Num. of Total Sig. Num. of Sig.Up Num. of Sig.down COM.vs.NORMAL_pos 7456 155 47 108 DECOM.vs.NORMAL_pos 7456 287 105 182 DECOM.vs.COM_pos 7456 203 120 83 DECOM.1.vs.NORMAL_pos 7456 228 103 125 DECOM.2.vs.NORMAL_pos 7456 324 94 230 DECOM.1.vs.COM_pos 7456 162 128 34 DECOM.2.vs.COM_pos 7456 223 86 137 DECOM.1.vs.DECOM.2_pos 7456 217 168 49 COM.vs.NORMAL_neg 5312 129 33 96 DECOM.vs.NORMAL_neg 5312 213 79 134 DECOM.vs.COM_neg 5312 121 70 51 DECOM.1.vs.NORMAL_neg 5312 183 78 105 DECOM.2.vs.NORMAL_neg 5312 252 90 162 DECOM.1.vs.COM_neg 5312 100 76 24 DECOM.2.vs.COM_neg 5312 153 72 81 DECOM.1.vs.DECOM.2_neg 5312 121 72 49 (1) Num. of Total Ident.: The number of total metabolite identification (2) Num. of Total Sig.: The number of total significant different metabolite (3) Num. of Sig.Up: The number of total significant up-regulated metabolite (4) Num. of Sig.down: The number of total significant down-regulated metabolite Therapeutic effect of S. boulardii on patients with liver cirrhosis. It was demonstrated that ALT and TBIL levels were significant decreased in both the S. boulardii and conventional treatment groups. The serum AMM levels of the S. boulardii group were significantly decreased, however, there was not a significant decrease in serum AMM levels in the conventional treatment group, suggesting probiotic therapy could reduce serum ammonia levels in patients with liver cirrhosis (Fig. 5 , the clinical phenotypes of patients were shown in Table 5 ). Table 5 Summary of clinical phenotype (Mean ± sd ) Phenotype conventional treatment S. boulardii treatment P-value Sex male = 3, female = 3 male = 7, female = 3 0.4237 Age 54.3 ± 4.0 52.9 ± 4.0 0.5090 Child-Pugh 8.8 ± 2.0 8.5 ± 1.6 0.7453 ALT (U/L) 43.0 ± 15.1 53.5 ± 26.2 0.3888 TBIL (µM) 52.8 ± 25.6 48.8 ± 24.8 0.7621 ALB (g/L) 30.4 ± 6.4 30.3 ± 6.3 0.9760 PT (sec) 15.3 ± 1.3 15.9 ± 1.2 0.3634 PLT 67.2 ± 16.7 79.0 ± 24.8 0.3218 AMM (µM) 48.9 ± 10.7 51.7 ± 15.5 0.7039 Discussion Liver dysfunction can lead to dysregulation of the gut microbiome via the abnormal production of secondary bile acids and primary bile acids and a decrease in the production of intestinal bile acids and bile flow. The gut microbiota was a complex microbial ecosystem which closely connected to the liver via the portal vein and it has emerged as a critical regulator of liver health and disease. Numerous studies have underscored its role in the onset and progression of liver disorders such as alcoholic liver disease, metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction-associated steatohepatitis (MASH), liver fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The gut microbiota plays an important role in metabolism. The diversity, and abundance of microbiota communities in the gut have been shown to change in cirrhosis and affect the development of cirrhosis complications (Xirouchakis et al. 2023; Ren et al. 2025 ). In the present study, it also was demonstrated that there was a dysregulation of the gut microbiota in patients with liver cirrhosis, such as alterations in the microbial community and a predominance of bacteria associated with disease progression and the aggravation of liver function. The present study demonstrated the presence of biomarkers such as Lactobacillus , Subdoligranulum , Ruminococcaceae , Blautia , Roseburia , Veillonella , Streptococcus and Staphylococcus_salivarius_subsp_thermophilus in Firmicutes and Escherichia_coli in Proteobacteria by LEFSE analysis. These biomarkers are associated with Child-Pugh score and ALT, TBIL, ALB, PT, PLT and AMM levels. It has been previously reported that Lactobacillus , Subdoligranulum , Ruminococcaceae , Blautia and Roseburia negatively correlated with the Child-Pugh and AMM levels and could promote the proliferation of butyric acid bacteria, maintaining intestinal integrity (Iwaki et al. 2025). The decrease in Subdoligranulum , Ruminococcaceae and Lactobacillus could lead to a deficiency in lactic acid with an increase of intestinal pH, branched chain fatty acids, amino acid fermentation and harmful metabolites, aggravating HE (Liu et al. 2021 ; Chen et al. 2021 ). Veillonella and Streptococcus degrade amino acids, purines and urea in the intestine and have been shown to be related to liver cirrhosis by aggravating liver damage and increasing blood ammonia (Ponziani et al. 2021 ; Haderer et al. 2022 ). The relative abundance of Staphylococcus_salivarius_subsp_thermophilus was also positively correlated with cirrhosis and damaged liver function caused by staphylolysin and enterotoxin. Proteobacteria was identified as a biomarker of decompensated liver cirrhosis in the present study. Therefore, it was considered that the aforementioned microorganisms may serve an important role on liver function injury with gut microbiota dysregulation. It was demonstrated that Escherichia_coli was positively correlated with liver cirrhosis in the present study. It was also demonstrated that Veillonella may be a potential biomarker for the diagnosis of the decompensatory stage of liver cirrhosis. HE is a reversible metabolic disorder caused by central nervous system dysfunction in patients with acute or chronic liver disease due to increased serum AMM and intestinal microbial dysregulation (Afecto et al. 2021 ; Kjærgaard K et al. 2021 ). To understand the influence of the gut microbiota on amino acid metabolism, the present study investigated the changes of metabolites in the feces of patients with liver cirrhosis using metabolomics. The KEGG analysis showed that there was a significant abnormal metabolism of glycine, phenylalanine and purine, which are important intermediates in the synthesis of the pseudo neurotransmitter such as phenolethanolamine and octopamine (Diniz et al. 2021; Carnagarin et al. 2021 ; Cheng et al. 2021 ). S. boulardii is a probiotic that is conducive to maintaining the intestinal microbiological balance and reducing intestinal mucosal cell damage. Some studies show that S. boulardii could release various liver disorders and promote the liver function of CCl4-treated rats. Meanwhile, it play an important role on reducing the abundance of Escherichia ( Proteobacteria ), increasing the abundance of Bacteroidetes in the gut microbiota, preventing an increase in intestinal barrier permeability, and reduced bacterial translocation and endotoxemia (Ren et al. 2025 ). In the present study, S. boulardii treatment was compared with the conventional treatment of patients with decompensated liver cirrhosis. A significant decrease in ALT and AMM levels was demonstrated in the S. boulardii group compared with the conventional treatment group. Additionally, an improved regulatory effect on intestinal microorganisms was shown in the S. boulardii group, suggesting its potential as a future therapy for patients with liver cirrhosis. It was reported that there was a decreased AMM, inflammation damage, oxidative stress and endotoxemia, meanwhile, there was an increased abundance of the short-chain fatty acids related microorganism such as Butyricum , Lactobacillus and Blautia , a decreased abundance of Proteobacteria by probiotics intervention in liver disorders. We considered that there was a similar mechanism of S. boulardii on liver cirrhosis therapeutics and it would be certified in further study (Wang et al. 2021 ). The present study investigated the characteristics and relationships of the gut microbiota and fecal metabolomics in patients with liver cirrhosis to analyze the role of the gut microbiota and the effect S.boulardii of in liver cirrhosis. It was demonstrated that there was a dysregulation of the gut microbiota and metabolism in patients with liver cirrhosis. The microorganisms such as Veillonella , Streptococcus , Blautia and Faecalibacterium were correlated with liver function index, Child-Pugh and AMM levels and they may be a potential biomarker. The abnormal gut microbiota, liver function index and serum ammonia levels was revised by S. boulardii treatment, suggesting its potential as a future therapy for patients with liver cirrhosis. However, above-mentioned conclusion need to be certified by more patient data and the mechanism such as the detailed relationship between gut microbiota and metabolite, also need to be deeper explored. Declarations Informed Consent: All patients provided written informed consent prior to participation in the study. Conflict of interest disclosure: The remaining authors have no conflicts of interest to report. Funding statement: The present study was supported by Integrated Traditional Chinese and Western Medicine of Tianjin Administration of Traditional Chinese Medicine Project (2019125), the Fund of Tianjin Second People's Hospital (YS0015), Tianjin Innovation Consortium Major Science and Technology Project (24ZXKJSY00020). Author Contribution YZ was responsible for the conception and design of the study. WW and PC were responsible for acquisition and interpretation of data. QY was responsible for drafted and revised the work. All authors read and approved the final manuscript. Acknowledgments: N/A Data availability statement: The data generated in the present study may be found in the NCBI Sequence Read Archive (SRA) database under accession number (PRJNA1242070) or at the following URL: https://www.ncbi.nlm.nih.gov/sra/PRJNA1242070 . 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Gut. 2019;68(11):2044–56. 10.1136/gutjnl-2018-316091 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 24 Nov, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers invited by journal 21 Oct, 2025 Editor assigned by journal 03 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 31 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":217348,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of the gut microbiota of patients with liver cirrhosis. (A1) The Principal Coordinate Analysis (PCoA) analysis of gut microbiota in Normal, COM and DECOM groups and (A2) in Normal, COM, DECOM.1 and DECOM.2 groups. (B) The level of AMM in serum. (C1) The Venn analysis of unique OTUs in Normal, COM and DECOM groups and (C2) in Normal, COM, DECOM.1 and DECOM.2 groups. (D) The α diversity of gut microbiota. (E1) The LEFSE analysis of gut microbiota and the correlation analysis of characteristic microbe and liver function phenotype in Normal, COM and DECOM groups and (E2) in Normal, COM, DECOM.1 and DECOM.2 groups. (E3) The correlation of microorganism with liver cirrhosis marker in Normal, COM and DECOM groups and (E4) in Normal, COM, DECOM.1 and DECOM.2 groups. \u003csup\u003e*\u003c/sup\u003eP\u0026lt;0.05; \u003csup\u003e**\u003c/sup\u003eP\u0026lt;0.01. COM, compensatory liver cirrhosis; DECOM, decompensatory liver cirrhosis; OTU, operational taxonomic unit; LEFSE, LDA effect size.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7501319/v1/03f8f8b2fe364655e89dc8e0.jpeg"},{"id":94986520,"identity":"a1841318-1041-4872-b56f-7a0fd36f64b7","added_by":"auto","created_at":"2025-11-03 07:00:23","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":163071,"visible":true,"origin":"","legend":"\u003cp\u003eROC and diversity in gut microbiota of patients with liver cirrhosis. (A) The diagnostic efficiency of candidate intestinal microorganisms was analyzed by ROC analysis. (B1) The Unweighted Pair Group Method with Arithmetic Mean (UPGMA) in Normal, COM and DECOM groups and (B2) in Normal, COM, DECOM.1 and DECOM.2 groups. (C1) Heat map for the β diversity of the gut microbiota in Normal, COM and DECOM groups and (C2) in Normal, COM, DECOM.1 and DECOM.2 groups. COM, compensatory liver cirrhosis; DECOM, decompensatory liver cirrhosis; ROC, receiver operating characteristic.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7501319/v1/3d388f9deb1fd1a79a269ade.jpeg"},{"id":94986513,"identity":"ad068c1a-466d-482b-88ce-cb92570445af","added_by":"auto","created_at":"2025-11-03 07:00:23","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141150,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of gut microbiota phenotypes in patients with liver cirrhosis.\u003cstrong\u003e \u003c/strong\u003e(A1) The Venn analysis of the gut microbiota phenotypes prediction in Normal, COM and DECOM group, (A2) in Normal, COM, DECOM.1 and DECOM.2 group; (B1) PCA analysis of the gut microbiota phenotypes prediction in Normal, COM and DECOM group, (B2) in Normal, COM, DECOM.1 and DECOM.2 group; (C1) the heat map of the gut microbiota phenotypes prediction in Normal, COM and DECOM group, (C2) in Normal, COM, DECOM.1 and DECOM.2 group; (D) KEGG pathway annotation of the gut microbiota phenotypes prediction. COM, compensatory liver cirrhosis; DECOM, decompensatory liver cirrhosis; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7501319/v1/5dc6514f2736cf799b2c61e8.jpeg"},{"id":94986251,"identity":"4fe5b2e4-0f4a-40b3-b757-efde52a3aeca","added_by":"auto","created_at":"2025-11-03 07:00:07","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":231151,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolite analysis of the feces of patients withliver cirrhosis.\u003cstrong\u003e \u003c/strong\u003e(A1) The PCA analysisof fecal metabolites in Normal, COM and DECOM groups and (A2) in Normal, COM, DECOM.1 and DECOM.2 groups. (B) The predictive Protein-Protein Interaction (PPI) of metabolites, enzymes and their function in Normal, COM, DECOM.1 and DECOM.2 groups. PCA, principal component analysis; COM, compensatory liver cirrhosis; DECOM, decompensatory liver cirrhosis.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7501319/v1/de963244e84497527f088958.jpeg"},{"id":94986545,"identity":"9cb72ddf-b329-4873-8068-8e48bdebae51","added_by":"auto","created_at":"2025-11-03 07:00:25","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":65760,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of \u003cem\u003eS. boulardii\u003c/em\u003e on symptoms of patients with liver cirrhosis.\u003c/p\u003e\n\u003cp\u003eThe effect of conventional and\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eS. boulardii\u003c/em\u003e association treatment on ALT, TBIL, ALB, PT, PLT and AMM levels in patients with liver cirrhosis. \u003csup\u003e*\u003c/sup\u003eP\u0026lt;0.05; \u003csup\u003e**\u003c/sup\u003eP\u0026lt;0.01. ALT, glutamic pyruvic transaminase; TBIL, total bilirubin; ALB, albumin; PT, prothrombin time; AMM, blood ammonia; \u003cem\u003eS. boulardii\u003c/em\u003e, \u003cem\u003eSaccharomyces boulardii\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7501319/v1/d12993bc2b65c6bfe1d4a684.jpeg"},{"id":94990446,"identity":"f6f02318-6bbc-4a68-8632-c2dc968c3665","added_by":"auto","created_at":"2025-11-03 07:17:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1921750,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7501319/v1/182c3457-0d28-4f33-883a-74369c93cd33.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEffects of \u003cem\u003eSaccharomyces boulardii\u003c/em\u003e on the characteristics and metabolomics of the gut microbiota in patients with liver cirrhosis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cirrhosis is a chronic progressive liver disease associated with liver damage. A variety of factors could lead to cirrhosis, such as viral hepatitis, drug-induced hepatitis, autoimmune hepatitis and fatty hepatitis (Yuan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yip et al. 2020). The gut microbiota serves an important role in human health by maintaining internal environment stability (Fan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A disturbance of the gut microbiota associated with an imbalance of pathogenic and beneficial bacteria has been previously reported in patients with liver cirrhosis. Additionally, fecal transfer from patients with liver cirrhosis could lead to abnormal liver function in mice, suggesting that abnormal gut microbiota may be a factor associated with liver cirrhosis.\u003csup\u003e7\u003c/sup\u003e The gut microbiota is formed of a number of complex and abundant species in the human body and produces a variety of metabolites which affect human health (Albhaisiet al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Albillos et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shen et al. 2023).\u003c/p\u003e\u003cp\u003eHepatic encephalopathy (HE) is a symptom of end-stage liver disease with nervous function disorder associated with high morbidity and mortality, as 60\u0026ndash;80% of patients with decompensated liver cirrhosis developed HE with a 1 year survival rate of 20\u0026ndash;42% and a 3 year survival rate of 15\u0026ndash;23%.13 A number of previous studies have shown that the HE is related to the presence of aromatic amino acids in the serum, such as phenylalanine, cucine and tryptophan, and the role of the gut-liver-brain axis in the pathogenesis of nervous dysfunction associated with liver disease (Giuli et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, although the liver cirrhosis was correlated with abnormal gut microbiota and metabolism, some questions remain unclear. For example, the type of microbes that are associated with the progression or diagnosis of patients with liver cirrhosis, the metabolic pathways that are altered in these patients, the connections between intestinal microorganisms and serum ammonia levels and whether probiotic treatment can regulate the imbalanced gut microbiota and aberrant serum ammonia levels.\u003c/p\u003e\u003cp\u003eThe present study investigated the aforementioned issues by analyzing the characteristic gut microbiota and associated metabolic pathways of patients with liver cirrhosis. Additionally, the relationship between intestinal microbes and liver function phenotypes were explored. Saccharomyces boulardii has been used for the treatment of various liver disorders with reducing the severity of many manifestations of cirrhosis and metabolic dysfunction (Maslennikov et al. 2023). These results provided a potential basis for the use of \u003cem\u003eSaccharomyces boulardii\u003c/em\u003e in the treatment of cirrhosis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cem\u003ePatients.\u003c/em\u003e A total of 22 patients with compensatory liver cirrhosis (COM group) and 26 patients with decompensatory liver cirrhosis (DECOM group) diagnosed at the Tianjin Second People\u0026rsquo;s Hospital from January 2019 to December 2020 were selected for inclusion in the present study and the Child-Pugh scores of these patients were analyzed. A total of 21 healthy volunteers were selected as the NORMAL group. A total of 16 patients with decompensatory liver cirrhosis diagnosed from January 2021 to December 2022 were selected for \u003cem\u003eS. boulardii\u003c/em\u003e treatment, including 6 patients in the conventional treatment group (patients 1\u0026ndash;6) given conventional treatment and 10 patients selected for both the \u003cem\u003eS. boulardii\u003c/em\u003e and conventional treatment (patients 7\u0026ndash;16). The latter group were given \u003cem\u003eS. boulardii\u003c/em\u003e (cat. no. S20150051; batch no. 3393; Biocodex) at 0.5 g twice daily, in addition to the conventional treatment. Patients in both groups were given continuous medication for 28 days. The Committee of Tianjin Second People\u0026rsquo;s Hospital (#2017-26) and the study was performed according to the principles of the Declaration of Helsinki. All patients provided written informed consent prior to participation in the study.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDiagnostic criteria.\u003c/em\u003e Patients were diagnosed with cirrhosis using imaging techniques such as ultrasonography, CT or MRI. Positive B-scan ultrasonography suggested the presence of ascites whereas positive CT results showed ascites of cirrhosis. Endoscopic or esophageal barium swallowing X-ray examination showed esophageal and gastric fundus varices.\u003c/p\u003e\u003cp\u003e\u003cem\u003eInclusion criteria\u003c/em\u003e. The inclusion criteria used were as follows: i) Patients who had not received other clinical treatment within 1 month; ii) both patients and healthy volunteers lived in Tianjin, China; and iii) healthy volunteers were included based on normal body function.\u003c/p\u003e\u003cp\u003e\u003cem\u003eExclusion criteria\u003c/em\u003e. Both patients and healthy volunteers was excluded as follows: i) Diagnosis of recent concurrent hepatic encephalopathy, gastrointestinal massive hemorrhage, infection and liver cancer; ii) fatty liver disease, hypertension, diabetes and other systemic diseases; iii) antibiotic use up to 4 weeks before enrollment; iv) patients with peritonitis; v) patients with alcoholic hepatitis, non-alcoholic steatohepatitis, autoimmune liver disease, genetic metabolic liver disease, schistosomiasis, drug-induced hepatitis and other causes of cirrhosis.\u003c/p\u003e\u003cp\u003e\u003cem\u003eGenomic DNA amplification.\u003c/em\u003e A total of 500 mg of feces were taken from patients and healthy volunteers and stored in sterile tubes at -20˚C. Total RNA was extracted using the fecal genome extraction kit (Beijing Tiangen Biochemistry Technology Co., Ltd.) and the variable region of the bacterial 16S rRNA gene V4 was amplified by PCR. High-fidelity PCR premixture (New England Biolabs, Inc.) of Phusion\u0026reg;GC buffer solution and high-efficient high-fidelity enzymes were used. The V4 variable region of the bacterial 16S rRNA gene was amplified by PCR using barcode primers 514F, 5'-GTGCCAGCMGCCGCGGTAA-3' and 805R, 5'-GGACTACHVGGGTWTCTAAT-3). The thermocycling protocol used was as follows: 94˚C for 2 min, followed by 30 cycles of 94˚C for 30 sec, 52˚C for 30 sec and 72˚C for 45 sec, then 72˚C for 5 min. Each 25 \u0026micro;l PCR reaction system consisted of 0.5 \u0026micro;l template DNA, 2.0 \u0026micro;l dNTP mixture (2.5 mM; TaKaRa), 2.5 \u0026micro;l non-10\u0026times;Mg\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;Ex Taq buffer, 1.5 \u0026micro;l Mg\u003csup\u003e2+\u003c/sup\u003e (25 mM), 0.25 \u0026micro;l Lex Taq DNA polymerase (2.5 units), 0.5 \u0026micro;l 10 mol/l primer 514F, 0.5 \u0026micro;l 10 mol/l primer 805R and 17.25 \u0026micro;l water. Sterile double-steamed water was used for dilution. The library was constructed using the TruSeq\u0026reg; DNA PCR-Free Sample Preparation Kit and the variable region of the bacterial rRNA gene V4 was sequenced using the HiSeq2500 PE250 platform.\u003csup\u003e21\u003c/sup\u003e The fastx_clipper tool was used to removed the adaptor sequences and filtered the data such as Phred score threshold or minimal nucleotide, fastx_collapser was used to removed the duplication in fastx_toolkit.\u003c/p\u003e\u003cp\u003e\u003cem\u003eLaboratory testing\u003c/em\u003e. Fasting venous blood was extracted from the patients and centrifugated at 4,000 r/min at 4˚C for 10 min. Serum was collected for glutamic pyruvic transaminase (ALT, Beckman Coulter, USA, #AUZ3363), total bilirubin (TBIL, Nipro, Japan, #H4U04) and albumin (ALB, Beijing Leadman Biochemistry Co., Ltd, China, #24061204) detection, which were measured by the biochemical method adopted by the Hitachi 7180 series full automatic biochemical analyzer test. Additionally, the prothrombin time (PT, Siemens Healthcare Diagnostics Products Co., Ltd, Germany, #572143) was measured using the Japan Sysmex prothrombin analyzer test and blood ammonia (AMM, Ortho-Clinical Diagnostics, inc., USA, #1726926) was measured by the American VITROS 350 biochemical analyzer test. Venous blood was extracted and platelets (PLTs, Sysmex Co., Ltd, Japan, Fluorocell WDF#CV-377-552, Fluorocell WNR#CP-066-715) were detected using the Sysmex XN-10 hematology analyzer.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMetabolomics analysis.\u003c/em\u003e 100 mg fresh fecal samples were ground and homogenized with liquid nitrogen, then suspended in 80% methanol (-20˚C). Samples were incubated at -20˚C for 60 min and centrifuged at 14,000 x g at 4˚C for 20 min. The supernatant was transferred to a fresh tube and placed in a vacuum concentrator to be rotated for drying. The dried metabolites were mixed with 60% methanol and analyzed using liquid/gas chromatography-mass spectrometry (LC-MS/MS). LC-MS/MS analysis was performed using the Vanquish UHPLC system (Thermo Fisher) and the Orbitrap Q ExActive HF-X mass spectrometer (Thermo Fisher) in the data-dependent acquisition mode. Samples were injected into a Hyperil Gold column and eluted for 16 min at a linear gradient flow rate of 0.3 ml/min. The eluents in positive polarity mode were A (0.1% FA aqueous solution) and B (methanol). The eluents in negative polarity mode were A (5 mM ammonium acetate; pH 9.0) and B (methanol). The solvent gradients were as follows: A, 98% + B, 2%, 1.5 min; B, 100%, 12.0 min; B, 100% 14.0 min; A\u0026thinsp;+\u0026thinsp;B, 2% 98% 14.1 min; 98% + 2% B, 16 min. Q ExActive HF-X mass spectrometer was operated in the positive/negative polarity mode with a spray voltage of 3.2 kV, capillary temperature of 320˚C, sheath gas flow rate of 35 arB and an auxiliary gas flow rate of 10 arB. The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.0 (Thermo Fisher) to compare, screen and quantify the peaks value of each metabolite. The main parameters were set as follows: Retention time tolerance, 0.2 min; actual mass tolerance, 5 ppm; signal strength tolerance, 30%; signal-to-noise ratio, 3; minimum strength, 100,000. The peak intensities were converted to total spectral intensities for predicting molecular formulations based on addition ions, molecular ion peaks and fragmentation ions. The peaks were matched with mzCloud (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mzcloud.org/\u003c/span\u003e\u003cspan address=\"https://www.mzcloud.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and ChemSpider databases (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.chemspider.com/\u003c/span\u003e\u003cspan address=\"http://www.chemspider.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to achieve accurate qualitative and relative quantitative results.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e. Data were expressed as x\u0026thinsp;\u0026plusmn;\u0026thinsp;s and analyzed using SPSS 11.0 software. Statistical differences were determined by one way ANOVA analysis with Tukey\u0026rsquo;s post hoc test for normally distributed data and the Mann-Whitney test for data that did not conform to a normal distribution. R software (version 2.15.3) was used for principal coordinate analysis (PCoA). The Pearson method was used for correlation analysis (r\u0026gt;|0.3|; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for correlation). The Tax4Fun software package and Matlab software were used to construct the Thiessen polygon for the function prediction of the gut microbiota. Variable Importance in the Projection (VIP) of the first principal component of the PLS-DA model was used to search for differentially expressed metabolites. The differentially expressed metabolites were screened out by setting the threshold of VIP\u0026thinsp;\u0026gt;\u0026thinsp;2.0, the differentially expressed multiple fold-change (FC) BBB\u0026thinsp;\u0026gt;\u0026thinsp;0 or FC\u0026thinsp;\u0026lt;\u0026thinsp;0.5 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The gene ontology (GO) information of the altered expression protein levels was analyzed using David software (Release 6.8). GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to bioinformatics analysis. The significant difference threshold was set as follows: Enrichment\u0026thinsp;\u0026ge;\u0026thinsp;2.0, multiple enrichment\u0026thinsp;\u0026ge;\u0026thinsp;2.0 and the Benjamini correction P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Cytoscape software (version 3.3.0) was used to build the network. Recevier operating curve (ROC) analysis was performed with GraphPad Prism 6.0. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate a statistically significant difference.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eStructure and characteristics of gut microbiota in patients with liver cirrhosis.\u003c/em\u003e PCoA analysis was used to analyze the similarity of microbial community structure. Although the intestinal microbial community structure of the COM and DECOM groups had a low similarity, the normal and COM group were overlapped (Fig. 1A1). There was two part in DECOM group with significant different community structure via PCoA analysis, therefore, the DECOM1 and DECOM2 groups was established based on the PC1 -0.34 in PCoA analysis of the DECOM group in order to explored the relationships between liver cirrhosis process and gut microbiota alteration (Fig. 1A2). It was demonstrated that the AMM level of the DECOM2 group was significantly higher compared with that of the COM group (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB; other clinical phenotypes were shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Meanwhile, there was a similar tendency in the weighted unweighted pair-group method with the arithmetic mean and heat map analysis for \u0026beta; diversity (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB-C), suggesting that gut microbiota disorder was related to the disturbance of amino acid metabolism.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of clinical phenotype\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd in NORMAL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd in COM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd in DECOM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd in DECOM1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd in DECOM2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u0026thinsp;=\u0026thinsp;15, female\u0026thinsp;=\u0026thinsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u0026thinsp;=\u0026thinsp;17, female\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u0026thinsp;=\u0026thinsp;19, female\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u0026thinsp;=\u0026thinsp;9, female\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u0026thinsp;=\u0026thinsp;10, female\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChild-Pugh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.9\u0026thinsp;\u0026plusmn;\u0026thinsp;100.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.9\u0026thinsp;\u0026plusmn;\u0026thinsp;71.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.7\u0026thinsp;\u0026plusmn;\u0026thinsp;85.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.6\u0026thinsp;\u0026plusmn;\u0026thinsp;38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBIL (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.3\u0026thinsp;\u0026plusmn;\u0026thinsp;38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.3\u0026thinsp;\u0026plusmn;\u0026thinsp;30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.9\u0026thinsp;\u0026plusmn;\u0026thinsp;46.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT (sec)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e242.7\u0026thinsp;\u0026plusmn;\u0026thinsp;60.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.6\u0026thinsp;\u0026plusmn;\u0026thinsp;46.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.4\u0026thinsp;\u0026plusmn;\u0026thinsp;48.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.2\u0026thinsp;\u0026plusmn;\u0026thinsp;58.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMM (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value (NORMAL vs. COM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value (NORMAL vs. DECOM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value (COM vs. DECOM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value (NORMAL vs. DECOM1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value (NORMAL vs. DECOM2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChild-Pugh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBIL (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT (sec)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMM (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value (COM vs. DECOM1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value (COM vs. DECOM2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value (DECOM1 vs. DECOM2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChild-Pugh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBIL (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT (sec)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMM (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThere were 162 unique operational taxonomic units (OTUs) in the COM group, 167 in the DECOM group, 89 in the DECOM1 group and 51 in the DECOM2 group, according to the Venn analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). According to the species annotations and abundance information, it was demonstrated that there was a lower \u0026alpha; diversity by chao1 in the DECOM group compared with that in the NORMAL group (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\n\u003cp\u003eLDA effect size (LEFSE) analysis was used to screen marker microorganism for liver cirrhosis (Fig.\u0026nbsp;1E1 and 1E2), and the correlation of marker microorganism with the Child-Pugh score and ALT, TBIL, ALB, PT, PLT and AMM levels was analyzed (Fig.\u0026nbsp;1E3 and 1E4; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). ROC analysis also demonstrated diagnostic efficiency based on marker microorganism such as \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e for patients with liver cirrhosis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe Spearman\u0026rsquo;s correlation of clinical index and significant different bacterial\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChild-Pugh\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTIBL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAMM\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ephylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirmicutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2601*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2937*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3013*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2737*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProteobacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3602*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4270*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4109*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3679*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3526*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3469*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActinobacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eclass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClostridia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7054*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0762*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5884*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6624*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.592*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4943*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4703*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGamma proteobacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3733*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4399*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4291*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3825*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3659*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3590*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegativicutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4008*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2751*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBacilli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6697*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4495*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3888*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5219*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eunidentified_Actinobacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClostridiales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7054*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5884*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6624*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5920*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4943*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4703*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnterobacteriales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3933*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4011*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3834*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3525*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3393*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelenomonadales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4008*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2751*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLactobacillales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6706*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4504*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3896*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5231*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBacteroidales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBifidobacteriales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efamily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3933*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4011*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3834*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3525*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3393*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRuminococcaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4893*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4493*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4346*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4193*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3734*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4199*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVeillonellaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4084*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2770*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStreptococcaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6633*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4210*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4576*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4991*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2597*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLachnospiraceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5893*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4644*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5717*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4853*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3470*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2981*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBifidobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLactobacillaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3190*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2449*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2872*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBacteroidaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egenus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFaecalibacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3325*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3069*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2532*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2575*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2594*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMegamonas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2769*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3009*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStreptococcus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6687*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4238*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4628*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5034*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2659*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2389*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eunidentified_Enterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3576*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2449*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3419*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2793*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLactobacillus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3204*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2448*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2871*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBifidobacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlautia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVeillonella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4489*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3305*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3946*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3041*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2502*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eunidentified_Ruminococcaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4735*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3699*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4778*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.393*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3245*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2845*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eunidentified_Lachnospiraceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4427*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2685*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.421*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3463*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2842*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoseburia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBacteroides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubdoligranulum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3449*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2503*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3797*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2997*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2719*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003especies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEscherichia_coli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3608*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2451*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3430*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2781*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2994*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStreptococcus_salivarius_subsp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLactobacillus_salivarius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRuminococcus_sp_5_1_39BFAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBifidobacterium_adolescentis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaphylococcus_salivarius_subsp_thermophilus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5083*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3026*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3709*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4093*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3219*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBacteroides_uniformis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2469*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2855*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoseburia_inulinivorans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe ROC of gut microbiota in liver cirrhosis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003esensitivity (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003especificity (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecut-off\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_bacteroides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_blautia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_faecalibacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_streptococcus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_veillonella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003especificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecut-off\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_bacteroides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_blautia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_faecalibacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_streptococcus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_veillonella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003especificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecut-off\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_bacteroides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_blautia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_faecalibacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_streptococcus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_veillonella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003especificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecut-off\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_bacteroides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_blautia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_faecalibacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_streptococcus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg_veillonella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eFunction prediction of gut microbiota in patients with liver cirrhosis\u003c/em\u003e. Tax4Fun is a bioinformatics software package used to predict the gut microbiota function. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUST) was used to predict the bacterial phenotypes based on the genetic information of OTUs in the Greengene database. The distribution and heterology of bacterial function between the normal and cirrhosis groups was shown by Venn and PCA analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA and B). It was predicted that the regulation of carbohydrate metabolism and amino acid biosynthesis and metabolism was an important function for the gut microbiota by heat map and KEGG analyses (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC and D), suggesting a correlation between gut microbiota and blood ammonia levels.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMetabolomics analysis of feces of patients with liver cirrhosis.\u003c/em\u003e To certify the functions predicted by PICRUST, the metabolites in feces were analyzed by metabolomics (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). PCA analysis of fecal metabolites also showed the development of liver cirrhosis, further suggesting the important role of fecal metabolites in the function of gut microbiota (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). The biosynthesis and metabolism of amino acids, phenylalanine and purine, enzymes such as xanthine dehydrogenase and acetyl-CoA synthetase may serve an important role in the development of liver cirrhosis based on metabolite-protein network analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). These data suggested that there was an abnormal metabolism of fatty acids, amino acids, bile acids and vitamins in patients with liver cirrhosis.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe regulated number of metabolite in feces\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompared Samples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNum. of Total Ident.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNum. of Total Sig.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNum. of Sig.Up\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNum. of Sig.down\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOM.vs.NORMAL_pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.vs.NORMAL_pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.vs.COM_pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.1.vs.NORMAL_pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.2.vs.NORMAL_pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.1.vs.COM_pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.2.vs.COM_pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.1.vs.DECOM.2_pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOM.vs.NORMAL_neg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.vs.NORMAL_neg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.vs.COM_neg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.1.vs.NORMAL_neg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.2.vs.NORMAL_neg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.1.vs.COM_neg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.2.vs.COM_neg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECOM.1.vs.DECOM.2_neg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e(1) Num. of Total Ident.: The number of total metabolite identification\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e(2) Num. of Total Sig.: The number of total significant different metabolite\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e(3) Num. of Sig.Up: The number of total significant up-regulated metabolite\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e(4) Num. of Sig.down: The number of total significant down-regulated metabolite\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eTherapeutic effect of S. boulardii on patients with liver cirrhosis.\u003c/em\u003e It was demonstrated that ALT and TBIL levels were significant decreased in both the \u003cem\u003eS. boulardii\u003c/em\u003e and conventional treatment groups. The serum AMM levels of the \u003cem\u003eS. boulardii\u003c/em\u003e group were significantly decreased, however, there was not a significant decrease in serum AMM levels in the conventional treatment group, suggesting probiotic therapy could reduce serum ammonia levels in patients with liver cirrhosis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the clinical phenotypes of patients were shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of clinical phenotype (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd )\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003econventional treatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. boulardii\u003c/em\u003e treatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u0026thinsp;=\u0026thinsp;3, female\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u0026thinsp;=\u0026thinsp;7, female\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChild-Pugh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBIL (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.8\u0026thinsp;\u0026plusmn;\u0026thinsp;25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.8\u0026thinsp;\u0026plusmn;\u0026thinsp;24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT (sec)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.0\u0026thinsp;\u0026plusmn;\u0026thinsp;24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMM (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLiver dysfunction can lead to dysregulation of the gut microbiome via the abnormal production of secondary bile acids and primary bile acids and a decrease in the production of intestinal bile acids and bile flow.\u003c/p\u003e\u003cp\u003eThe gut microbiota was a complex microbial ecosystem which closely connected to the liver via the portal vein and it has emerged as a critical regulator of liver health and disease. Numerous studies have underscored its role in the onset and progression of liver disorders such as alcoholic liver disease, metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction-associated steatohepatitis (MASH), liver fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The gut microbiota plays an important role in metabolism. The diversity, and abundance of microbiota communities in the gut have been shown to change in cirrhosis and affect the development of cirrhosis complications (Xirouchakis et al. 2023; Ren et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the present study, it also was demonstrated that there was a dysregulation of the gut microbiota in patients with liver cirrhosis, such as alterations in the microbial community and a predominance of bacteria associated with disease progression and the aggravation of liver function.\u003c/p\u003e\u003cp\u003eThe present study demonstrated the presence of biomarkers such as \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eStaphylococcus_salivarius_subsp_thermophilus\u003c/em\u003e in Firmicutes and \u003cem\u003eEscherichia_coli\u003c/em\u003e in Proteobacteria by LEFSE analysis. These biomarkers are associated with Child-Pugh score and ALT, TBIL, ALB, PT, PLT and AMM levels. It has been previously reported that \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e and \u003cem\u003eRoseburia\u003c/em\u003e negatively correlated with the Child-Pugh and AMM levels and could promote the proliferation of butyric acid bacteria, maintaining intestinal integrity (Iwaki et al. 2025). The decrease in \u003cem\u003eSubdoligranulum\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e could lead to a deficiency in lactic acid with an increase of intestinal pH, branched chain fatty acids, amino acid fermentation and harmful metabolites, aggravating HE (Liu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eVeillonella\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e degrade amino acids, purines and urea in the intestine and have been shown to be related to liver cirrhosis by aggravating liver damage and increasing blood ammonia (Ponziani et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Haderer et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The relative abundance of \u003cem\u003eStaphylococcus_salivarius_subsp_thermophilus\u003c/em\u003e was also positively correlated with cirrhosis and damaged liver function caused by staphylolysin and enterotoxin. \u003cem\u003eProteobacteria\u003c/em\u003e was identified as a biomarker of decompensated liver cirrhosis in the present study. Therefore, it was considered that the aforementioned microorganisms may serve an important role on liver function injury with gut microbiota dysregulation. It was demonstrated that \u003cem\u003eEscherichia_coli\u003c/em\u003e was positively correlated with liver cirrhosis in the present study. It was also demonstrated that \u003cem\u003eVeillonella\u003c/em\u003e may be a potential biomarker for the diagnosis of the decompensatory stage of liver cirrhosis.\u003c/p\u003e\u003cp\u003eHE is a reversible metabolic disorder caused by central nervous system dysfunction in patients with acute or chronic liver disease due to increased serum AMM and intestinal microbial dysregulation (Afecto et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kj\u0026aelig;rgaard K et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To understand the influence of the gut microbiota on amino acid metabolism, the present study investigated the changes of metabolites in the feces of patients with liver cirrhosis using metabolomics. The KEGG analysis showed that there was a significant abnormal metabolism of glycine, phenylalanine and purine, which are important intermediates in the synthesis of the pseudo neurotransmitter such as phenolethanolamine and octopamine (Diniz et al. 2021; Carnagarin et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cheng et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"SpListChar12\"\u003e\u003cli\u003e\u003cp\u003eS. \u003cem\u003eboulardii\u003c/em\u003e is a probiotic that is conducive to maintaining the intestinal microbiological balance and reducing intestinal mucosal cell damage. Some studies show that S. \u003cem\u003eboulardii\u003c/em\u003e could release various liver disorders and promote the liver function of CCl4-treated rats. Meanwhile, it play an important role on reducing the abundance of \u003cem\u003eEscherichia\u003c/em\u003e (\u003cem\u003eProteobacteria\u003c/em\u003e), increasing the abundance of \u003cem\u003eBacteroidetes\u003c/em\u003e in the gut microbiota, preventing an increase in intestinal barrier permeability, and reduced bacterial translocation and endotoxemia (Ren et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the present study, \u003cem\u003eS. boulardii\u003c/em\u003e treatment was compared with the conventional treatment of patients with decompensated liver cirrhosis. A significant decrease in ALT and AMM levels was demonstrated in the \u003cem\u003eS. boulardii\u003c/em\u003e group compared with the conventional treatment group. Additionally, an improved regulatory effect on intestinal microorganisms was shown in the \u003cem\u003eS. boulardii\u003c/em\u003e group, suggesting its potential as a future therapy for patients with liver cirrhosis.\u003c/p\u003e\u003c/li\u003e\u003c/div\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIt was reported that there was a decreased AMM, inflammation damage, oxidative stress and endotoxemia, meanwhile, there was an increased abundance of the short-chain fatty acids related microorganism such as \u003cem\u003eButyricum\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eBlautia\u003c/em\u003e, a decreased abundance of \u003cem\u003eProteobacteria\u003c/em\u003e by probiotics intervention in liver disorders. We considered that there was a similar mechanism of \u003cem\u003eS. boulardii\u003c/em\u003e on liver cirrhosis therapeutics and it would be certified in further study (Wang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe present study investigated the characteristics and relationships of the gut microbiota and fecal metabolomics in patients with liver cirrhosis to analyze the role of the gut microbiota and the effect \u003cem\u003eS.boulardii\u003c/em\u003e of in liver cirrhosis. It was demonstrated that there was a dysregulation of the gut microbiota and metabolism in patients with liver cirrhosis. The microorganisms such as \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e were correlated with liver function index, Child-Pugh and AMM levels and they may be a potential biomarker. The abnormal gut microbiota, liver function index and serum ammonia levels was revised by \u003cem\u003eS. boulardii\u003c/em\u003e treatment, suggesting its potential as a future therapy for patients with liver cirrhosis. However, above-mentioned conclusion need to be certified by more patient data and the mechanism such as the detailed relationship between gut microbiota and metabolite, also need to be deeper explored.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eInformed Consent:\u003c/h2\u003e\u003cp\u003eAll patients provided written informed consent prior to participation in the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflict of interest disclosure:\u003c/h2\u003e\u003cp\u003eThe remaining authors have no conflicts of interest to report.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding statement:\u003c/h2\u003e\u003cp\u003eThe present study was supported by Integrated Traditional Chinese and Western Medicine of Tianjin Administration of Traditional Chinese Medicine Project (2019125), the Fund of Tianjin Second People's Hospital (YS0015), Tianjin Innovation Consortium Major Science and Technology Project (24ZXKJSY00020).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYZ was responsible for the conception and design of the study. WW and PC were responsible for acquisition and interpretation of data. QY was responsible for drafted and revised the work. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e\u003cp\u003eN/A\u003c/p\u003e\u003ch2\u003eData availability statement:\u003c/h2\u003e\u003cp\u003eThe data generated in the present study may be found in the NCBI Sequence Read Archive (SRA) database under accession number (PRJNA1242070) or at the following URL: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/sra/PRJNA1242070\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/sra/PRJNA1242070\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eDisclosure of Ethical Statements\u003c/h3\u003e\n\u003cp\u003e Approval of the research protocol: Committee of Tianjin Second People's Hospital (2017-026), was performed according to the principles of the Declaration of Helsinki in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfecto E, Ponte A, Fernandes S, Silva J, Gomes C, Correia J, Carvalho J. 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HBV infection-induced liver cirrhosis development in dual-humanised mice with human bone mesenchymal stem cell transplantation. Gut. 2019;68(11):2044\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gutjnl-2018-316091\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2018-316091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gut microbiota, liver cirrhosis, Saccharomyces boulardii","lastPublishedDoi":"10.21203/rs.3.rs-7501319/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7501319/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Metabolism serves an important role in the gut microbiota and regulating the progression of liver cirrhosis. The present study investigated the characteristics of the gut microbiota and associated metabolites in patients with liver cirrhosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: The characteristics of the gut microbiota and fecal metabolites in patients with liver cirrhosis from January 2019 to December 2022 were analyzed using 16S rRNA sequencing and metabolomics with bioinformatic analysis. The effect of \u003cem\u003eSaccharomyces boulardii\u003c/em\u003e on the gut microbiota and phenotype of patients with liver cirrhosis was examined.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: It was demonstrated that there was a low similarity and diversity of the gut microbiota in the patients with liver cirrhosis compared with the normal group. Microorganisms such as \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e were significantly correlated with the liver function index, which may serve an important role in abnormal amino acid biosynthesis and metabolism associated with liver cirrhosis progression, as demonstrated through functional prediction and metabolomics. The abnormal intestinal microorganism and serum ammonia levels was decreased in patients with liver cirrhosis after \u003cem\u003eS. boulardii\u003c/em\u003e treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Therefore, the abnormal amino acid metabolism and serum ammonia levels were induced by gut microbiota disorder in patients with liver cirrhosis and probiotic treatment alleviated this.\u003c/p\u003e","manuscriptTitle":"Effects of Saccharomyces boulardii on the characteristics and metabolomics of the gut microbiota in patients with liver cirrhosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 15:31:53","doi":"10.21203/rs.3.rs-7501319/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-24T19:40:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T11:50:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267085008326329335140253209892453429147","date":"2025-11-11T13:55:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90591967689969184595862072490367163329","date":"2025-11-11T08:36:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T15:13:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281723207174144613090176207885215405817","date":"2025-10-21T10:42:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T10:18:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T08:31:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T08:30:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2025-08-31T14:38:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5b7754b-2362-4cb8-ade8-6c0ad0043e8d","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-01-01T07:38:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 15:31:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7501319","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7501319","identity":"rs-7501319","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}