3MCor: an integrative web server for metabolome-microbiome-metadata correlation analysis.

MOTIVATION: The metabolome and microbiome disorders are highly associated with human health, and there are great demands for dual-omics interaction analysis. Here, we designed and developed an integrative platform, 3MCor, for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders. RESULTS: Many traditional and novel correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical and pairwise analysis. The incorporated network analysis function is conducive to rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. A real-world dataset was used to demonstrate its simple and flexible operation, comprehensive outputs and distinctive contribution to dual-omics studies. AVAILABILITYAND IMPLEMENTATION: 3MCor is available at http://3mcor.cn and the backend R script is available at https://github.com/chentianlu/3MCorServer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Altered bile acid glycine : taurine ratio in the progression of chronic liver disease.

BACKGROUND AND AIM: The onset and progression of chronic liver disease (CLD) is a multistage process spanning years or several decades. Some bile acid (BA) features are identified as indicators for CLD progression. However, BAs are highly influenced by various factors and are stage and/or population specific. Emerging evidences demonstrated the association of structure of conjugated BAs and CLD progression. Here, we aimed to investigate the alteration of conjugated BAs and identify new features for CLD progression. METHODS: Based on liquid chromatography-mass spectrometry platform, 15 BAs were quantified in 1883 participants including healthy controls and CLD patients (non-alcoholic fatty liver [NAFL], non-alcoholic steatohepatitis [NASH], fibrosis, cirrhosis, and three types of liver cancer). Logistic regression was used to construct diagnostic models. Model performances were evaluated in discovery and test sets by area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and kappa index. RESULTS: Five BA glycine : taurine ratios were calculated, and glycocholic acid/taurocholic acid, glycodeoxycholic acid/taurodeoxycholic acid, and glycochenodeoxycholic acid/taurochenocholic acid were identified as candidates. Three diagnostic models were constructed for the differentiation of healthy control and early CLD (NAFL + NASH), early and advanced CLD (fibrosis + cirrhosis + liver cancer), and NAFL and NASH, respectively. The areas under the receiver operating characteristic curve of the models ranged from 0.91 to 0.97. The addition of age and gender improved model performances further. The alterations of the candidates and the performances of the diagnostic models were successfully validated by independent test sets (n = 291). CONCLUSIONS: Our findings revealed stage-specific BA perturbation patterns and provided new biomarkers and tools for the monitoring of liver disease progression.

Salivary Metabolomics Reveals that Metabolic Alterations Precede the Onset of Schizophrenia.

Schizophrenia is a complex and highly heterogeneous mental illness with a prodromal period called clinical high risk (CHR) for psychosis before onset. Metabolomics is greatly promising in analyzing the pathology of complex diseases and exploring diagnostic biomarkers. Therefore, we conducted salivary metabolomics analysis in 83 first-episode schizophrenia (FES) patients, 42 CHR individuals, and 78 healthy controls with ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The mass spectrometry raw data have been deposited on the MetaboLights (ID: MTBLS3463). We found downregulated aromatic amino acid metabolism, disturbed glutamine and nucleotide metabolism, and upregulated tricarboxylic acid cycle in FES patients, which existed even in the CHR stage and became more intense with the onset of the schizophrenia. Moreover, differential metabolites can be considered as potential diagnostic biomarkers and indicate the severity of the different clinical stages of disease. Furthermore, three disordered pathways were closely related to peripheral indicators of inflammatory response, oxidative stress, blood-brain barrier damage, and salivary microbiota. These results indicate that the disorder of oral metabolism occurs earlier than the onset of schizophrenia and is concentrated and intensified with the onset of disease, which may originate from the dysbiotic salivary microbiota and cause the onset of schizophrenia through the peripheral inflammatory response and redox system, suggesting the importance of oral-brain connection in the pathogenesis of schizophrenia.

Bile Acid Profiles Are Distinct among Patients with Different Etiologies of Chronic Liver Disease.

A significant increase of bile acid (BA) levels has been recognized as a general metabolic phenotype of diverse liver diseases. Monitoring of BA profiles has been proposed for etiology differentiation on liver injury. Here, we quantitatively profiled serum BAs of healthy controls and 719 patients with chronic liver disease of five etiologies, hepatitis B virus (HBV), hepatitis C virus (HCV), nonalcoholic steatohepatitis (NASH), alcohol-induced liver disease (ALD), and primary biliary cirrhosis (PBC), and investigated the generality and specificity of different etiologies. The raw data have been deposited into MetaboLights (ID: MTBLS2459). We found that patients with HBV, HCV, and NASH appeared to be more similar, and ALD and PBC patients clustered together. BA profiles, consisting of a total concentration of the 21 quantified BAs [total BAs (TBAs)], 21 BA proportions, and 24 BA relevant variables, were highly different among the etiologies. Specifically, the total BAs was higher in ALD and PBC patients compared with the other three groups. The proportion of conjugated deoxycholates was the highest in HBV-infected patients. The ratio of 12α-hydroxylated (12α-OH) to non-12α-OH BAs was the highest in NASH patients. The proportion of taurine-conjugated BAs was the highest in ALD patients. For PBC patients, the proportion of ursodeoxycholate species was the highest, and the ratio of primary to secondary BAs was the lowest. Comparatively, the difference of BA profiles among cirrhosis patients was consistent but weaker than that of all patients. The correlations between BA profiles and clinical indices were also quite different in different pathological groups, both in all patients and in patients with cirrhosis. Overall, our findings suggested that BA compositions are distinct among patients with different etiologies of chronic liver disease, and some BA-relevant variables are of clinical potentials for liver injury type differentiation, although further validations on more etiologies and populations are needed.

A Metabolite Array Technology for Precision Medicine.

The application of metabolomics in translational research suffers from several technological bottlenecks, such as data reproducibility issues and the lack of standardization of sample profiling procedures. Here, we report an automated high-throughput metabolite array technology that can rapidly and quantitatively determine 324 metabolites including fatty acids, amino acids, organic acids, carbohydrates, and bile acids. Metabolite identification and quantification is achieved using the Targeted Metabolome Batch Quantification (TMBQ) software, the first cross-vendor data processing pipeline. A test of this metabolite array was performed by analyzing serum samples from patients with chronic liver disease (N = 1234). With high detection efficiency and sensitivity in serum, urine, feces, cell lysates, and liver tissue samples and suitable for different mass spectrometry systems, this metabolite array technology holds great potential for biomarker discovery and high throughput clinical testing. Additionally, data generated from such standardized procedures can be used to generate a clinical metabolomics database suitable for precision medicine in next-generation healthcare.

Preparation of Ag3PO4/TiO2(B) Heterojunction Nanobelt with Extended Light Response and Enhanced Photocatalytic Performance.

Photocatalytic degradation, as an emerging method to control environmental pollution, is considered one of the most promising environmental purification technologies. As Tibet is a region with some of the strongest solar radiation in China and even in the world, it is extremely rich in solar energy resources, which is ideal for applying photocatalytic technology to its ecological environment protection and governance. In this study, Na2Ti3O7 nanobelts were prepared via a hydrothermal method and converted to TiO2∙xH2O ion exchange, which was followed by high-temperature calcination to prepare TiO2(B) nanobelts ("B" in TiO2(B) means "Bronze phase"). A simple in situ method was used to generate Ag3PO4 particles on the surface of the TiO2 nanobelts to construct a Ag3PO4/TiO2(B) heterojunction composite photocatalyst. By generating Ag3PO4 nanoparticles on the surface of the TiO2(B) nanobelts to construct heterojunctions, the light absorption range of the photocatalyst was successfully extended from UV (ultraviolet) to the visible region. Furthermore, the recombination of photogenerated electron-hole pairs in the catalyst was inhibited by the construction of the heterojunctions, thus greatly enhancing its light quantum efficiency. Therefore, the prepared Ag3PO4/TiO2(B) heterojunction composite photocatalyst greatly outperformed the TiO2(B) nanobelt in terms of photocatalytic degradation.

IP4M: an integrated platform for mass spectrometry-based metabolomics data mining.

BACKGROUND: Metabolomics data analyses rely on the use of bioinformatics tools. Many integrated multi-functional tools have been developed for untargeted metabolomics data processing and have been widely used. More alternative platforms are expected for both basic and advanced users. RESULTS: Integrated mass spectrometry-based untargeted metabolomics data mining (IP4M) software was designed and developed. The IP4M, has 62 functions categorized into 8 modules, covering all the steps of metabolomics data mining, including raw data preprocessing (alignment, peak de-convolution, peak picking, and isotope filtering), peak annotation, peak table preprocessing, basic statistical description, classification and biomarker detection, correlation analysis, cluster and sub-cluster analysis, regression analysis, ROC analysis, pathway and enrichment analysis, and sample size and power analysis. Additionally, a KEGG-derived metabolic reaction database was embedded and a series of ratio variables (product/substrate) can be generated with enlarged information on enzyme activity. A new method, GRaMM, for correlation analysis between metabolome and microbiome data was also provided. IP4M provides both a number of parameters for customized and refined analysis (for expert users), as well as 4 simplified workflows with few key parameters (for beginners who are unfamiliar with computational metabolomics). The performance of IP4M was evaluated and compared with existing computational platforms using 2 data sets derived from standards mixture and 2 data sets derived from serum samples, from GC-MS and LC-MS respectively. CONCLUSION: IP4M is powerful, modularized, customizable and easy-to-use. It is a good choice for metabolomics data processing and analysis. Free versions for Windows, MAC OS, and Linux systems are provided.

Serum metabolite profiles are associated with the presence of advanced liver fibrosis in Chinese patients with chronic hepatitis B viral infection.

BACKGROUND: Accurate and noninvasive diagnosis and staging of liver fibrosis are essential for effective clinical management of chronic liver disease (CLD). We aimed to identify serum metabolite markers that reliably predict the stage of fibrosis in CLD patients. METHODS: We quantitatively profiled serum metabolites of participants in 2 independent cohorts. Based on the metabolomics data from cohort 1 (504 HBV associated liver fibrosis patients and 502 normal controls, NC), we selected a panel of 4 predictive metabolite markers. Consequently, we constructed 3 machine learning models with the 4 metabolite markers using random forest (RF), to differentiate CLD patients from normal controls (NC), to differentiate cirrhosis patients from fibrosis patients, and to differentiate advanced fibrosis from early fibrosis, respectively. RESULTS: The panel of 4 metabolite markers consisted of taurocholate, tyrosine, valine, and linoelaidic acid. The RF models of the metabolite panel demonstrated the strongest stratification ability in cohort 1 to diagnose CLD patients from NC (area under the receiver operating characteristic curve (AUROC) = 0.997 and the precision-recall curve (AUPR) = 0.994), to differentiate fibrosis from cirrhosis (0.941, 0.870), and to stage liver fibrosis (0.918, 0.892). The diagnostic accuracy of the models was further validated in an independent cohort 2 consisting of 300 CLD patients with chronic HBV infection and 90 NC. The AUCs of the models were consistently higher than APRI, FIB-4, and AST/ALT ratio, with both greater sensitivity and specificity. CONCLUSIONS: Our study showed that this 4-metabolite panel has potential usefulness in clinical assessments of CLD progression in patients with chronic hepatitis B virus infection.

Strategy for Intercorrelation Identification between Metabolome and Microbiome.

Accumulating evidence points to the strong and complicated associations between the metabolome and the microbiome, which play diverse roles in physiology and pathology. Various correlation analysis approaches were applied to identify microbe-metabolite associations. Given the strengths and weaknesses of the existing methods and considering the characteristics of different types of omics data, we designed a special strategy, called Generalized coRrelation analysis for Metabolome and Microbiome (GRaMM), for the intercorrelation discovery between the metabolome and microbiome. GRaMM can properly deal with two types of omics data, the effect of confounders, and both linear and nonlinear correlations by integrating several complementary methods such as the classical linear regression, the emerging maximum information coefficient (MIC), the metabolic confounding effect elimination (MCEE), and the centered log-ratio transformation (CLR). GRaMM contains four sequential computational steps: (1) metabolic and microbial data preprocessing, (2) linear/nonlinear type identification, (3) data correction and correlation detection, and (4) p value correction. The performances of GRaMM, including the accuracy, sensitivity, specificity, false positive rate, applicability, and effects of preprocessing and confounder adjustment steps, were evaluated and compared with three other methods in multiple simulated and real-world datasets. To our knowledge, GRaMM is the first strategy designed for the intercorrelation analysis between metabolites and microbes. The Matlab function and an R package were developed and are freely available for academic use (comply with GNU GPL.V3 license).

polyPK: an R package for pharmacokinetic analysis of multi-component drugs using a metabolomics approach.

Summary: Pharmacokinetics (PK) is a long-standing bottleneck for botanical drug and traditional medicine research. By using an integrated phytochemical and metabolomics approach coupled with multivariate statistical analysis, we propose a new strategy, Poly-PK, to simultaneously monitor the performance of drug constituents and endogenous metabolites, taking into account both the diversity of the drug's chemical composition and its complex effects on the mammalian metabolic pathways. Poly-PK is independent of specific measurement platforms and has been successfully applied in the PK studies of Puerh tea, a traditional Chinese medicine Huangqi decoction and many other multi-component drugs. Here, we introduce an R package, polyPK, the first and only automation of the data analysis pipeline of Poly-PK strategy. polyPK provides 10 functions for data pre-processing, differential compound identification and grouping, traditional PK parameters calculation, multivariate statistical analysis, correlations, cluster analyses and resulting visualization. It may serve a wide range of users, including pharmacologists, biologists and doctors, in understanding the metabolic fate of multi-component drugs. Availability and implementation: polyPK package is freely available from the R archive CRAN (https://CRAN.R-project.org/package=polyPK). Contact: wjia@cc.hawaii.edu or chentianlu@sjtu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.

Evaluation of metabolite-microbe correlation detection methods.

Different correlation detection methods have been specifically designed for the microbiome data analysis considering the compositional data structure and different sequencing depths. Along with the speedy development of omics studies, there is an increasing interest in discovering the biological associations between microbes and host metabolites. This raises the need of finding proper statistical methods that facilitate the correlation analysis across different omics studies. Here, we comprehensively evaluated six different correlation methods, i.e., Pearson correlation, Spearman correlation, Sparse Correlations for Compositional data (SparCC), Correlation inference for Compositional data through Lasso (CCLasso), Mutual Information Coefficient (MIC), and Cosine similarity methods, for the correlations detection between microbes and metabolites. Three simulated and two real-world data sets (from public databases and our lab) were used to examine the performance of each method regarding its specificity, sensitivity, similarity, accuracy, and stability with different sparsity. Our results indicate that although each method has its own pros and cons in different scenarios, Spearman correlation and MIC outperform the others with their overall performances. A strategic guidance was also proposed for the correlation analysis between microbe and metabolite.

Food withdrawal alters the gut microbiota and metabolome in mice.

Food withdrawal as a health-enhancing measure has beneficial effects on aging, disease prevention, and treatment. However, the cellular and molecular mechanisms involving gut microbial changes and metabolic consequences resulting from food withdrawal have yet to be elucidated. In this study, we subjected lean and obese mice to a dietary intervention that consisted of a 4-d complete food withdrawal and an 8-d 50% food withdrawal, and we studied changes in cecal microbiome and host serum metabolome. The abundance of potentially pathogenic Proteobacteria was decreased and Akkermansia muciniphila was elevated by food withdrawal in mice fed a high-fat diet (HFD). Meanwhile, food withdrawal decreased the abundance of metabolites in branched chain amino acid, lipid, and free fatty acid metabolisms in host serum, more so in HFD mice than in normal mice. Microbial predicted function also showed that food withdrawal decreased the abundance of microbes associated with predicted diseases in the HFD group but not in the normal chow group. Correlation between the microbiome data and metabolomics data revealed a strong association between gut microbial and host metabolic changes in response to food withdrawal. In summary, our results showed that food withdrawal was safer and more metabolically beneficial to HFD-induced obese mice than to normal lean mice, and the beneficial effects were primarily derived from the changes in gut microbiota, which were closely associated with the host metabolome.-Zheng, X., Zhou, K., Zhang, Y., Han, X., Zhao, A., Liu, J., Qu, C., Ge, K., Huang, F., Hernandez, B., Yu, H., Panee, J., Chen, T., Jia, W., Jia, W. Food withdrawal alters the gut microbiota and metabolome in mice.

GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies.

Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: https://github.com/WandeRum/GSimp.

MCEE 2.0: more options and enhanced performance.

A confounding factor is an unstudied factor that affects one or more of the variables that are being studied in an investigation, so the presence of a confounder may lead to inaccurate or biased results. It is well recognized that physiological and environmental factors such as race, diet, age, gender, blood pressure, and diurnal cycle affect mammalian metabolism. To eliminate the noise introduced by confounders into metabolomics studies, a GUI-based method denoted metabolic confounding effect elimination (MCEE) was developed and has since been applied successfully in a wide range of metabolomics studies. To keep up with recent developments in computational metabolomics and a growing number of user requests, an upgraded version of MCEE with more options and enhanced performance was designed and developed. Besides the generalized linear model (GLM) method, a multivariate method for selecting affected metabolites-canonical correlation analysis (CCA)-was introduced, which accounts for complicated correlations and collinearity within the metabolome. Multiple confounders are acceptable and can be identified and processed separately or simultaneously. The effectiveness of this new version of MCEE as well as the pros and cons of the two methodological options were examined using three simulated data sets (a basic model, a model with different sample size ratios, and a sparse model) and two real-world data sets (a human type 2 diabetes mellitus data set and a human arthritis data set). As well as presenting the results of this examination of the new version of MCEE, some instructions on appropriate method selection and parameter setting are provided here. The freely available MATLAB code for MCEE with a GUI has also been updated accordingly at https://github.com/chentianlu/MCEE-2.0 . Graphical abstract.

Strategy for an Association Study of the Intestinal Microbiome and Brain Metabolome Across the Lifespan of Rats.

There is increased appreciation for the diverse roles of the microbiome-gut-brain axis on mammalian growth and health throughout the lifespan. Numerous studies have demonstrated that the gut microbiome and their metabolites are extensively involved in the communication between brain and gut. Association study of brain metabolome and gut microbiome is an active field offering large amounts of information on the interaction of microbiome, brain and gut but data size and complicated hierarchical relationships were found to be major obstacles to the formation of significant, reproducible conclusions. This study addressed a two-level strategy of brain metabolome and gut microbiome association analysis of male Wistar rats in the process of growth, employing several analytical platforms and various bioinformatics methods. Trajectory analysis showed that the age-related brain metabolome and gut microbiome had similarity in overall alteration patterns. Four high taxonomical level correlated pairs of "metabolite type-bacterial phylum", including "lipids-Spirochaetes", "free fatty acids (FFAs)-Firmicutes", "bile acids (BAs)-Firmicutes", and "Neurotransmitters-Bacteroidetes", were screened out based on unit- and multivariant correlation analysis and function analysis. Four groups of specific "metabolite-bacterium" association pairs from within the above high level key pairs were further identified. The key correlation pairs were validated by an independent animal study. This two-level strategy is effective in identifying principal correlations in big data sets obtained from the systematic multiomics study, furthering our understanding on the lifelong connection between brain and gut.

The ratio of dihomo-γ-linolenic acid to deoxycholic acid species is a potential biomarker for the metabolic abnormalities in obesity.

Bile acid (BA) signaling regulates fatty acid metabolism. BA dysregulation plays an important role in the development of metabolic disease. However, BAs in relation to fatty acids have not been fully investigated in obesity-related metabolic disorders. A targeted metabolomic measurement of serum BA and free fatty acid profiles was applied to sera of 381 individuals in 2 independent studies. The results showed that the ratio of dihomo-γ-linolenic acid (DGLA) to deoxycholic acid (DCA) species (DCAS) was significantly increased in obese individuals with type 2 diabetes (T2DM) from a case-control study and decreased in the remission group of obese subjects with T2DM after metabolic surgery. The changes were closely associated with their metabolic status. These results were consistently confirmed in both serum and liver of mice with diet-induced obesity, implying that such a metabolic alteration in circulation reflects changes occurring in the liver. In vitro studies of human liver L-02 cell lines under BA treatment revealed that DCA and its conjugated form, TDCA, significantly inhibited mRNA expression of fatty acid transport protein 5 in the presence of DGLA, which was involved in hepatocyte DGLA uptake. Thus, the DGLA:DCAS ratio may be a promising biomarker for metabolic abnormalities in obesity.-Lei, S., Huang, F., Zhao, A., Chen, T., Chen, W., Xie, G., Zheng, X., Zhang, Y., Yu, H., Zhang, P., Rajani, C., Bao, Y., Jia, W., Jia, W. The ratio of dihomo-γ-linolenic acid to deoxycholic acid species is a potential biomarker for the metabolic abnormalities in obesity.

Serum stearic acid/palmitic acid ratio as a potential predictor of diabetes remission after Roux-en-Y gastric bypass in obesity.

Endogenous fatty acid metabolism that results in elongation and desaturation lipid products is thought to play a role in the development of type 2 diabetes mellitus (T2DM). In this study, we evaluated the potential of estimated elongase and desaturase activities for use as predictive markers for T2DM remission after Roux-en-Y gastric bypass (RYGB). The results of a targeted metabolomics approach from 2 independent studies were used to calculate 24 serum FA concentration ratios (product/precursor). Gene expression data from an open public data set was also analyzed. In a longitudinal study of 38 obese diabetic patients with RYGB, we found higher baseline stearic acid/palmitic acid (S/P) ratio. This ratio reflects an elovl6-encoded elongase enzyme activity that has been found to be associated with greater possibility for diabetes remission after RYGB [odds ratio, 2.16 (95% CI 1.10-4.26)], after adjustment for age, gender, body mass index, diabetes duration, glycosylated hemoglobin A1c, and fasting C-peptide. Our results were validated by examination of postsurgical elovl6 gene expression in morbidly obese patients. The association of S/P with the metabolic status of obese individuals was further validated in a cross-sectional cohort of 381 participants. In summary, higher baseline S/P was associated with greater probability of diabetes remission after RYGB and may serve as a diagnostic marker in preoperative patient assessment. - Zhao, L., Ni, Y., Yu, H., Zhang, P., Zhao, A., Bao, Y., Liu, J., Chen, T., Xie, G., Panee, J., Chen, W., Rajani, C., Wei, R., Su, M., Jia, W., Jia, W. Serum stearic acid/palmitic acid ratio as a potential predictor of diabetes remission after Roux-en-Y gastric bypass in obesity.

MCEE: a data preprocessing approach for metabolic confounding effect elimination.

It is well recognized that physiological and environmental factors such as race, age, gender, and diurnal cycles often have a definite influence on metabolic results that statistically manifests as confounding variables. Currently, removal or controlling of confounding effects relies heavily on experimental design. There are no available data processing techniques focusing on the compensation of their effects. We therefore proposed a new method, Metabolic confounding effect elimination (MCEE), to remove the influence of specified confounding factors and make the data more accurate. The method consists of three steps: metabolites grouping, confounder-related metabolites selection, and metabolites modification. Its effectiveness and advantages were evaluated comprehensively by several simulated models and real datasets, and were compared with two typical methods, the principal component analysis (PCA)- and the direct orthogonal signal correction (DOSC)-based methods. MCEE is simple, effective, and safe, and is independent of sample number, association degree, and missing value. Hence, it may serve as a good complement to existing metabolomics data preprocessing methods and aid in better understanding the metabolic and biological status of interest. Graphical Abstract Algorithm flow and demo performance of MCEE.

Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice.

BACKGROUND: Intestinal bacteria are known to regulate bile acid (BA) homeostasis via intestinal biotransformation of BAs and stimulation of the expression of fibroblast growth factor 19 through intestinal nuclear farnesoid X receptor (FXR). On the other hand, BAs directly regulate the gut microbiota with their strong antimicrobial activities. It remains unclear, however, how mammalian BAs cross-talk with gut microbiome and shape microbial composition in a dynamic and interactive way. RESULTS: We quantitatively profiled small molecule metabolites derived from host-microbial co-metabolism in mice, demonstrating that BAs were the most significant factor correlated with microbial alterations among all types of endogenous metabolites. A high-fat diet (HFD) intervention resulted in a rapid and significant increase in the intestinal BA pool within 12 h, followed by an alteration in microbial composition at 24 h, providing supporting evidence that BAs are major dietary factors regulating gut microbiota. Feeding mice with BAs along with a normal diet induced an obese phenotype and obesity-associated gut microbial composition, similar to HFD-fed mice. Inhibition of hepatic BA biosynthesis under HFD conditions attenuated the HFD-induced gut microbiome alterations. Both inhibition of BAs and direct suppression of microbiota improved obese phenotypes. CONCLUSIONS: Our study highlights a liver-BA-gut microbiome metabolic axis that drives significant modifications of BA and microbiota compositions capable of triggering metabolic disorders, suggesting new therapeutic strategies targeting BA metabolism for metabolic diseases.

Enhanced Hydrolysis of Cellulose in Ionic Liquid Using Mesoporous ZSM-5.

Mesoporous ZSM-5 prepared by alkaline treatment was demonstrated as an efficient catalyst for the cellulose hydrolysis in ionic liquid (IL), affording a high yield of reducing sugar. It was demonstrated that mesoporous ZSM-5 (SiO2/Al2O3 = 38) had 76.2% cellulose conversion and 49.6% yield of total reducing sugar (TRS). In comparison, the conventional ZSM-5 had a mere 41.3% cellulose conversion with 33.2% yield of TRS. The results indicated that the important role of mesopores in zeolites in elevating the TRS yield may be due to the diffusional alleviation of cellulose macromolecules. The effects of reaction time, temperature, and the ratio of catalyst to cellulose were investigated for optimal reaction conditions. It was found that IL could enter the inner channel of mesoporous ZSM-5 to promote the generation of H⁺ from Brönsted acid sites, which facilitated hydrolysis. Moreover, the mesoporous ZSM-5 showed excellent reusability for catalytic cycles by means of calcination of the used one, promising for its practical applications in the hydrolysis of cellulose.