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.

The microbial metabolome in metabolic-associated fatty liver disease.

Metabolism-associated fatty liver disease (MAFLD) is defined as the presence of excess fat in the liver in the absence of excess alcohol consumption and metabolic dysfunction. It has also been described as the hepatic manifestation of metabolic syndrome. The incidence of MAFLD has been reported to be 43-60% in diabetics, ~90% in patients with hyperlipidemia, and 91% in morbidly obese patients. Risk factors that have been associated with the development of MAFLD include male gender, increasing age, obesity, insulin resistance, diabetes, and hyperlipidemia. All of these risk factors have been linked to alterations of the gut microbiota, that is, gut dysbiosis. MAFLD can progress to non-alcoholic steatohepatitis with the presence of inflammation and ballooning, which can deteriorate into cirrhosis, MAFLD-related hepatocellular carcinoma, and liver failure. In this review, we will be focused on the role of the gut microbial metabolome in the development, progression, and potential treatment of MAFLD.

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.

Deformable torso phantoms of Chinese adults for personalized anatomy modelling.

In recent years, there has been increasing demand for personalized anatomy modelling for medical and industrial applications, such as ergonomics device development, clinical radiological exposure simulation, biomechanics analysis, and 3D animation character design. In this study, we constructed deformable torso phantoms that can be deformed to match the personal anatomy of Chinese male and female adults. The phantoms were created based on a training set of 79 trunk computed tomography (CT) images (41 males and 38 females) from normal Chinese subjects. Major torso organs were segmented from the CT images, and the statistical shape model (SSM) approach was used to learn the inter-subject anatomical variations. To match the personal anatomy, the phantoms were registered to individual body surface scans or medical images using the active shape model method. The constructed SSM demonstrated anatomical variations in body height, fat quantity, respiratory status, organ geometry, male muscle size, and female breast size. The masses of the deformed phantom organs were consistent with Chinese population organ mass ranges. To validate the performance of personal anatomy modelling, the phantoms were registered to the body surface scan and CT images. The registration accuracy measured from 22 test CT images showed a median Dice coefficient over 0.85, a median volume recovery coefficient (RCvlm ) between 0.85 and 1.1, and a median averaged surface distance (ASD) < 1.5 mm. We hope these phantoms can serve as computational tools for personalized anatomy modelling for the research community.

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.

Influence of pH on inhibition of Streptococcus mutans by Streptococcus oligofermentans.

Streptococcus oligofermentans is a novel strain of oral streptococcus that can specifically inhibit the growth of Streptococcus mutans. The aims of this study were to assess the growth of S. oligofermentans and the ability of S. oligofermentans to inhibit growth of Streptococcus mutans at different pH values. Growth inhibition was investigated in vitro using an interspecies competition assay. The 4-aminoantipyine method was used to measure the initial production rate and the total yield of hydrogen peroxide in S. oligofermentans. S. oligofermentans grew best at pH 7.0 and showed the most pronounced inhibitory effect when it was inoculated earlier than S. mutans. In terms of the total yield and the initial production rate of hydrogen peroxide by S. oligofermentans, the effects of the different culture pH values were as follows: pH 7.0 > 6.5 > 6.0 > 7.5 > 5.5 = 8.0 (i.e. there was no significant difference between pH 5.5 and pH 8.0). Environmental pH and the sequence of inoculation significantly affected the ability of S. oligofermentans to inhibit the growth of S. mutans. The degree of inhibition may be attributed to the amount of hydrogen peroxide produced.

Effect of ultrasound-guided bilateral superficial cervical plexus block versus perioperative intravenous lidocaine infusion on postoperative quality of recovery in patients undergoing thyroidectomy: A randomised double-blind comparative trial.

Background and Aims: Recent studies have found that ultrasound-guided (USG) bilateral superficial cervical plexus block (BSCPB) and intravenous infusion of lidocaine (IVL) have the potential to improve the quality of postoperative recovery. This study aimed to investigate and compare their effects on postoperative quality of recovery in patients undergoing thyroidectomy. Methods: A total of 135 patients were randomised to Group N: BSCPB with 10 mL 0.75% ropivacaine on each side, Group L: intravenous lidocaine (1.5 mg/kg for 10 min, followed by 1.5 mg/kg/h) and Group C: intravenous saline combined with BSCPB saline. The primary objective was quality of recovery-40 (QoR-40). Other parameters compared were numeric rating pain scale (NRS) score, haemodynamic data, opioid dosage and incidence of adverse effects. Statistical analysis was performed using the one-way analysis of variance (ANOVA), the Kruskal-Wallis test and the Chi-square test. Results: Compared to Group C, both groups N and L had higher QoR-40 total scores as well as scores indicating physical comfort, emotional state and pain dimensions on postoperative day (POD) 1 and POD2 (P < 0.001). The QoR-40 total and pain dimension scores in Group N were higher on POD1 and POD2 (P < 0.05). The NRS scores and the change in haemodynamics were lower in Group N compared to groups L and C (P < 0.05). The results of other parameters were lower in groups N and L than in Group C (P < 0.05). Conclusion: USG BSCPB and IVL are comparable in improving the quality of postoperative recovery in patients undergoing thyroidectomy.

Effects of perioperative intravenous lidocaine and esketamine on the quality of recovery and emotional state of patients after thyroidectomy: A randomised, double-blind, controlled trial.

Background and Aims: Perioperative intravenous (IV) infusions of lidocaine and esketamine reduce postoperative pain, but there are few studies on the quality of recovery and patients' emotional states postoperatively. We aimed to explore the effects of perioperative IV lidocaine and esketamine on the quality of recovery and emotional state after thyroidectomy. Methods: In this randomised trial, 137 patients undergoing thyroidectomy were randomly assigned to three groups: a lidocaine group (Group L), an esketamine group (Group E) and a normal saline placebo group (Group C). The primary outcome was the Quality of Recovery 40 (QoR-40) on postoperative days (PODs) 1 and 2. The secondary outcomes included Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) scores on days 1 and 2 after surgery, pain scores, opioid consumption and incidence of postoperative nausea and vomiting (PONV). Statistical analysis was performed using the one-way analysis of variance (ANOVA), the Kruskal-Wallis and Chi-square tests. Results: The global QoR-40 scores in groups L and E on POD 1 and POD 2 were significantly higher than in group C (P < 0.001). The SAS and SDS scores on POD 1 and POD 2 in groups L and E were significantly lower than in group C (P < 0.05). There were statistically significant differences in Numerical Rating Scale (NRS) scores among the three groups at 1 h, 2 h, 6 h and 12 h (P < 0.05). Conclusion: Perioperative IV lidocaine and esketamine improve the quality of postoperative recovery and the emotional state of patients undergoing thyroidectomy.

Serum Bile Acids Improve Prediction of Alzheimer's Progression in a Sex-Dependent Manner.

Sex disparities in serum bile acid (BA) levels and Alzheimer's disease (AD) prevalence have been established. However, the precise link between changes in serum BAs and AD development remains elusive. Here, authors quantitatively determined 33 serum BAs and 58 BA features in 4 219 samples collected from 1 180 participants from the Alzheimer's Disease Neuroimaging Initiative. The findings revealed that these BA features exhibited significant correlations with clinical stages, encompassing cognitively normal (CN), early and late mild cognitive impairment, and AD, as well as cognitive performance. Importantly, these associations are more pronounced in men than women. Among participants with progressive disease stages (n = 660), BAs underwent early changes in men, occurring before AD. By incorporating BA features into diagnostic and predictive models, positive enhancements are achieved for all models. The area under the receiver operating characteristic curve improved from 0.78 to 0.91 for men and from 0.76 to 0.83 for women for the differentiation of CN and AD. Additionally, the key findings are validated in a subset of participants (n = 578) with cerebrospinal fluid amyloid-beta and tau levels. These findings underscore the role of BAs in AD progression, offering potential improvements in the accuracy of AD prediction.

Metabolic phenotyping reveals an emerging role of ammonia abnormality in Alzheimer's disease.

The metabolic implications in Alzheimer's disease (AD) remain poorly understood. Here, we conducted a metabolomics study on a moderately aging Chinese Han cohort (n = 1397; mean age 66 years). Conjugated bile acids, branch-chain amino acids (BCAAs), and glutamate-related features exhibited strong correlations with cognitive impairment, clinical stage, and brain amyloid-ß deposition (n = 421). These features demonstrated synergistic performances across clinical stages and subpopulations and enhanced the differentiation of AD stages beyond demographics and Apolipoprotein E ε4 allele (APOE-ε4). We validated their performances in eight data sets (total n = 7685) obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) and Religious Orders Study and Memory and Aging Project (ROSMAP). Importantly, identified features are linked to blood ammonia homeostasis. We further confirmed the elevated ammonia level through AD development (n = 1060). Our findings highlight AD as a metabolic disease and emphasize the metabolite-mediated ammonia disturbance in AD and its potential as a signature and therapeutic target for AD.

Gut bacteria-driven homovanillic acid alleviates depression by modulating synaptic integrity.

The gut-brain axis is implicated in depression development, yet its underlying mechanism remains unclear. We observed depleted gut bacterial species, including Bifidobacterium longum and Roseburia intestinalis, and the neurotransmitter homovanillic acid (HVA) in individuals with depression and mouse depression models. Although R. intestinalis does not directly produce HVA, it enhances B. longum abundance, leading to HVA generation. This highlights a synergistic interaction among gut microbiota in regulating intestinal neurotransmitter production. Administering HVA, B. longum, or R. intestinalis to mouse models with chronic unpredictable mild stress (CUMS) and corticosterone (CORT)-induced depression significantly improved depressive symptoms. Mechanistically, HVA inhibited synaptic autophagic death by preventing excessive degradation of microtubule-associated protein 1 light chain 3 (LC3) and SQSTM1/p62 proteins, protecting hippocampal neurons' presynaptic membrane. These findings underscore the role of the gut microbial metabolism in modulating synaptic integrity and provide insights into potential novel treatment strategies for depression.

Unveiling Insights into Ovarian Cancer Metabolism through Space- and Time-Resolved Analysis.

High-grade serous carcinoma (HGSC) represents a formidable challenge in the realm of ovarian cancer, notorious for its elusive early detection, poor prognosis and limited understanding of the intricacies of its pathogenesis [...].

Hyodeoxycholic acid alleviates non-alcoholic fatty liver disease through modulating the gut-liver axis.

Non-alcoholic fatty liver disease (NAFLD) is regarded as a pandemic that affects about a quarter of the global population. Recently, host-gut microbiota metabolic interactions have emerged as distinct mechanistic pathways implicated in the development of NAFLD. Here, we report that a group of gut microbiota-modified bile acids (BAs), hyodeoxycholic acid (HDCA) species, are negatively correlated with the presence and severity of NAFLD. HDCA treatment has been shown to alleviate NAFLD in multiple mouse models by inhibiting intestinal farnesoid X receptor (FXR) and upregulating hepatic CYP7B1. Additionally, HDCA significantly increased abundances of probiotic species such as Parabacteroides distasonis, which enhances lipid catabolism through fatty acid-hepatic peroxisome proliferator-activated receptor alpha (PPARα) signaling, which in turn upregulates hepatic FXR. These findings suggest that HDCA has therapeutic potential for treating NAFLD, with a unique mechanism of simultaneously activating hepatic CYP7B1 and PPARα.

Bile Acid-Microbiome Interaction Promotes Gastric Carcinogenesis.

Bile reflux gastritis (BRG) is associated with the development of gastric cancer (GC), but the specific mechanism remains elusive. Here, a comprehensive study is conducted to explore the roles of refluxed bile acids (BAs) and microbiome in gastric carcinogenesis. The results show that conjugated BAs, interleukin 6 (IL-6), lipopolysaccharide (LPS), and the relative abundance of LPS-producing bacteria are increased significantly in the gastric juice of both BRG and GC patients. A secondary BA, taurodeoxycholic acid (TDCA), is significantly and positively correlated with the LPS-producing bacteria in the gastric juice of these patients. TDCA promotes the proliferation of normal gastric epithelial cells (GES-1) through activation of the IL-6/JAK1/STAT3 pathway. These results are further verified in two mouse models, one by gavage of TDCA, LPS, and LPS-producing bacteria (Prevotella melaninogenica), respectively, and the other by bile reflux (BR) surgery, mimicking clinical bile refluxing. Moreover, the bile reflux induced gastric precancerous lesions observed in the post BR surgery mice can be prevented by treatment with cryptotanshinone, a plant-derived STAT3 inhibitor. These results reveal an important underlying mechanism by which bile reflux promotes gastric carcinogenesis and provide an alternative strategy for the prevention of GC associated with BRG.

Gut microbiota-bile acid crosstalk contributes to the rebound weight gain after calorie restriction in mice.

Calorie restriction (CR) and fasting are common approaches to weight reduction, but the maintenance is difficult after resuming food consumption. Meanwhile, the gut microbiome associated with energy harvest alters dramatically in response to nutrient deprivation. Here, we reported that CR and high-fat diet (HFD) both remodeled the gut microbiota with similar microbial composition, Parabacteroides distasonis was most significantly decreased after CR or HFD. CR altered microbiota and reprogramed metabolism, resulting in a distinct serum bile acid profile characterized by depleting the proportion of non-12α-hydroxylated bile acids, ursodeoxycholic acid and lithocholic acid. Downregulation of UCP1 expression in brown adipose tissue and decreased serum GLP-1 were observed in the weight-rebound mice. Moreover, treatment with Parabacteroides distasonis or non-12α-hydroxylated bile acids ameliorated weight regain via increased thermogenesis. Our results highlighted the gut microbiota-bile acid crosstalk in rebound weight gain and Parabacteroides distasonis as a potential probiotic to prevent rapid post-CR weight gain.

Hyocholic acid species improve glucose homeostasis through a distinct TGR5 and FXR signaling mechanism.

Hyocholic acid (HCA) and its derivatives are found in trace amounts in human blood but constitute approximately 76% of the bile acid (BA) pool in pigs, a species known for its exceptional resistance to type 2 diabetes. Here, we show that BA depletion in pigs suppressed secretion of glucagon-like peptide-1 (GLP-1) and increased blood glucose levels. HCA administration in diabetic mouse models improved serum fasting GLP-1 secretion and glucose homeostasis to a greater extent than tauroursodeoxycholic acid. HCA upregulated GLP-1 production and secretion in enteroendocrine cells via simultaneously activating G-protein-coupled BA receptor, TGR5, and inhibiting farnesoid X receptor (FXR), a unique mechanism that is not found in other BA species. We verified the findings in TGR5 knockout, intestinal FXR activation, and GLP-1 receptor inhibition mouse models. Finally, we confirmed in a clinical cohort, that lower serum concentrations of HCA species were associated with diabetes and closely related to glycemic markers.

Hyocholic acid species as novel biomarkers for metabolic disorders.

Hyocholic acid (HCA) is a major bile acid (BA) species in the BA pool of pigs, a species known for its exceptional resistance to spontaneous development of diabetic phenotypes. HCA and its derivatives are also present in human blood and urine. We investigate whether human HCA profiles can predict the development of metabolic disorders. We find in the first cohort (n = 1107) that both obesity and diabetes are associated with lower serum concentrations of HCA species. A separate cohort study (n = 91) validates this finding and further reveals that individuals with pre-diabetes are associated with lower levels of HCA species in feces. Serum HCA levels increase in the patients after gastric bypass surgery (n = 38) and can predict the remission of diabetes two years after surgery. The results are replicated in two independent, prospective cohorts (n = 132 and n = 207), where serum HCA species are found to be strong predictors for metabolic disorders in 5 and 10 years, respectively. These findings underscore the association of HCA species with diabetes, and demonstrate the feasibility of using HCA profiles to assess the future risk of developing metabolic abnormalities.

Anti-Adipogenic Effect of Theabrownin Is Mediated by Bile Acid Alternative Synthesis via Gut Microbiota Remodeling.

Theabrownin is one of the most bioactive compounds in Pu-erh tea. Our previous study revealed that the hypocholesterolemic effect of theabrownin was mediated by the modulation of bile salt hydrolase (BSH)-enriched gut microbiota and bile acid metabolism. In this study, we demonstrated that theabrownin ameliorated high-fat-diet (HFD)-induced obesity by modifying gut microbiota, especially those with 7α-dehydroxylation on the species level, and these changed microbes were positively correlated with secondary bile acid (BA) metabolism. Thus, altered intestinal BAs resulted in shifting bile acid biosynthesis from the classic to the alternative pathway. This shift changed the BA pool by increasing non-12α-hydroxylated-BAs (non-12OH-BAs) and decreasing 12α-hydroxylated BAs (12OH-BAs), which improved energy metabolism in white and brown adipose tissue. This study showed that theabrownin was a potential therapeutic modality for obesity and other metabolic disorders via gut microbiota-driven bile acid alternative synthesis.