The promise of metabolic phenotyping in gastroenterology and hepatology

Photoreceptor metabolic window unveils eye-body interactions

Photoreceptors are specialized neurons at the core of the retina's functionality, with optical accessibility and exceptional sensitivity to systemic metabolic stresses. Here we show the ability of risk-free, in vivo photoreceptor assessment as a window into systemic health and identify shared metabolic underpinnings of photoreceptor degeneration and multisystem health outcomes. A thinner photoreceptor layer thickness is significantly associated with an increased risk of future mortality and 13 multisystem diseases, while systematic analyses of circulating metabolomics enable the identification of 109 photoreceptor-related metabolites, which in turn elevate or reduce the risk of these health outcomes. To translate these insights into a practical tool, we developed an artificial intelligence (AI)-driven photoreceptor metabolic window framework and an accompanying interpreter that comprehensively captures the metabolic landscape of photoreceptor-systemic health linkages and simultaneously predicts 16 multisystem health outcomes beyond established approaches while retaining interpretability. Our work, replicated across cohorts of diverse ethnicities, reveals the potential of photoreceptors to inform systemic health and advance a multisystem perspective on human health by revealing eye-body connections and shared metabolic influences.

Unraveling the mechanisms of hepatogenous diabetes and its therapeutic perspectives

The review focused mainly on the pathogenesis of hepatogenous diabetes (HD) in liver cirrhosis (LC). This review reveals parallels between the mechanisms of metabolic dysfunction observed in LC and type II diabetes (T2DM), suggesting a shared pathway leading to HD. It underscores the role of insulin in HD pathogenesis, highlighting key factors such as insulin signaling, glucose metabolism, insulin resistance (IR), and the influence of adipocytes. Furthermore, the impact of adipose tissue accumulation, fatty acid metabolism, and pro-inflammatory cytokines like Tumor necrosis factor-α (TNF-α) on IR are discussed in the context of HD. Altered signaling pathways, disruptions in the endocrine system, liver inflammation, changes in muscle mass and composition, and modifications to the gut microbiota collectively contribute to the complex interplay linking cirrhosis and HD. This study highlights how important it is to identify and treat this complex condition in cirrhotic patients by thoroughly analyzing the link between cirrhosis, IR, and HD. It also emphasizes the vitality of targeted interventions. Cellular and molecular investigations into IR have revealed potential therapeutic targets for managing and preventing HD.

Metabolomic Applications in Gut Microbiota-Host Interactions in Human Diseases

The human gut microbiota, consisting of trillions of microorganisms, encodes diverse metabolic pathways that impact numerous aspects of host physiology. One key way in which gut bacteria interact with the host is through the production of small metabolites. Several of these microbiota-dependent metabolites, such as short-chain fatty acids, have been shown to modulate host diseases. In this review, we examine how disease-associated metabolic signatures are identified using metabolomic platforms, and where metabolomics is applied in gut microbiota-disease interactions. We further explore how integration of metagenomic and metabolomic data in human studies can facilitate biomarkers discoveries in precision medicine.

Microbe-disease associations prediction by graph regularized non-negative matrix factorization with L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms

Microbes are involved in a wide range of biological processes and are closely associated with disease. Inferring potential disease-associated microbes as the biomarkers or drug targets may help prevent, diagnose and treat complex human diseases. However, biological experiments are time-consuming and expensive. In this study, we introduced a new method called iPALM-GLMF, which modelled microbe-disease association prediction as a problem of non-negative matrix factorization with graph dual regularization terms andL 2 , 1 $$ {L}_{2,1} $$ norm regularization terms. The graph dual regularization terms were used to capture potential features in the microbe and disease space, and theL 2 , 1 $$ {L}_{2,1} $$ norm regularization terms were used to ensure the sparsity of the feature matrices obtained from the non-negative matrix factorization and to improve the interpretability. To solve the model, iPALM-GLMF used a non-negative double singular value decomposition to initialize the matrix factorization and adopted an inertial Proximal Alternating Linear Minimization iterative process to obtain the final matrix factorization results. As a result, iPALM-GLMF performed better than other existing methods in leave-one-out cross-validation and fivefold cross-validation. In addition, case studies of different diseases demonstrated that iPALM-GLMF could effectively predict potential microbial-disease associations. iPALM-GLMF is publicly available at https://github.com/LiangzheZhang/iPALM-GLMF.

The Metabolomic Footprint of Liver Fibrosis

Both experimental and clinical liver fibrosis leave a metabolic footprint that can be uncovered and defined using metabolomic approaches. Metabolomics combines pattern recognition algorithms with analytical chemistry, in particular, 1H and 13C nuclear magnetic resonance spectroscopy (NMR), gas chromatography-mass spectrometry (GC-MS) and various liquid chromatography-mass spectrometry (LC-MS) platforms. The analysis of liver fibrosis by each of these methodologies is reviewed separately. Surprisingly, there was little general agreement between studies within each of these three groups and also between groups. The metabolomic footprint determined by NMR (two or more hits between studies) comprised elevated lactate, acetate, choline, 3-hydroxybutyrate, glucose, histidine, methionine, glutamine, phenylalanine, tyrosine and citrate. For GC-MS, succinate, fumarate, malate, ascorbate, glutamate, glycine, serine and, in agreement with NMR, glutamine, phenylalanine, tyrosine and citrate were delineated. For LC-MS, only β-muricholic acid, tryptophan, acylcarnitine, p-cresol, valine and, in agreement with NMR, phosphocholine were identified. The metabolomic footprint of liver fibrosis was upregulated as regards glutamine, phenylalanine, tyrosine, citrate and phosphocholine. Several investigators employed traditional Chinese medicine (TCM) treatments to reverse experimental liver fibrosis, and a commentary is given on the chemical constituents that may possess fibrolytic activity. It is proposed that molecular docking procedures using these TCM constituents may lead to novel therapies for liver fibrosis affecting at least one-in-twenty persons globally, for which there is currently no pharmaceutical cure. This in-depth review summarizes the relevant literature on metabolomics and its implications in addressing the clinical problem of liver fibrosis, cirrhosis and its sequelae.

High-throughput identification of gut microbiome-dependent metabolites

A significant hurdle that has limited progress in microbiome science has been identifying and studying the diverse set of metabolites produced by gut microbes. Gut microbial metabolism produces thousands of difficult-to-identify metabolites, which present a challenge to study their roles in host biology. In recent years, mass spectrometry-based metabolomics has become one of the core technologies for identifying small metabolites. However, metabolomics expertise, ranging from sample preparation to instrument use and data analysis, is often lacking in academic labs. Most targeted metabolomics methods provide high levels of sensitivity and quantification, while they are limited to a panel of predefined molecules that may not be informative to microbiome-focused studies. Here we have developed a gut microbe-focused and wide-spectrum metabolomic protocol using liquid chromatography-mass spectrometry and bioinformatic analysis. This protocol enables users to carry out experiments from sample collection to data analysis, only requiring access to a liquid chromatography-mass spectrometry instrument, which is often available at local core facilities. By applying this protocol to samples containing human gut microbial metabolites, spanning from culture supernatant to human biospecimens, our approach enables high-confidence identification of >800 metabolites that can serve as candidate mediators of microbe-host interactions. We expect this protocol will lower the barrier to tracking gut bacterial metabolism in vitro and in mammalian hosts, propelling hypothesis-driven mechanistic studies and accelerating our understanding of the gut microbiome at the chemical level.

Prostate cancer-derived extracellular vesicles metabolic biomarkers: Emerging roles for diagnosis and prognosis

Prostate cancer (PCa) is a global health concern, ranking as the most common cancer among men in Western countries. Traditional diagnostic methods are invasive with adverse effects on patients. Due to the heterogeneous nature of PCa and their multifocality, tissue biopsies often yield false-negative results. To address these challenges, researchers are exploring innovative approaches, particularly in the realms of proteomics and metabolomics, to identify more reliable biomarkers and improve PCa diagnosis. Liquid biopsy (LB) has emerged as a promising non-invasive strategy for PCa early detection, biopsy selection, active surveillance for low-risk cases, and post-treatment and progression monitoring. Extracellular vesicles (EVs) are lipid-bilayer nanovesicles released by all cell types and play an important role in intercellular communication. EVs have garnered attention as a valuable biomarker resource in LB for PCa-specific biomarkers, enhancing diagnosis, prognostication, and treatment guidance. Metabolomics provides insight into the body's metabolic response to both internal and external stimuli, offering quantitative measurements of biochemical alterations. It excels at detecting non-genetic influences, aiding in the discovery of more accurate cancer biomarkers for early detection and disease progression monitoring. This review delves into the potential of EVs as a resource for LB in PCa across various clinical applications. It also explores cancer-related metabolic biomarkers, both within and outside EVs in PCa, and summarises previous metabolomic findings in PCa diagnosis and risk assessment. Finally, the article addresses the challenges and future directions in the evolving field of EV-based metabolomic analysis, offering a comprehensive overview of its potential in advancing PCa management.

Changes in fecal lipidome after treatment with ivacaftor without changes in microbiome or bile acids

BACKGROUND

Alterations in gastrointestinal health are prominent manifestations of cystic fibrosis (CF) and can independently impact pulmonary function. Ivacaftor has been associated with robust improvements in pulmonary function and weight gain, but less is known about the impact of ivacaftor on the fecal microbiome, lipidome, and bile acids.

METHODS

Stool samples from 18 patients with CF and gating mutations (ages 6-61 years, 13 pancreatic insufficient) were analyzed for fecal microbiome and lipidome composition as well as bile acid concentrations at baseline and after 3 months of treatment with ivacaftor. Microbiome composition was also assessed in a healthy reference cohort.

RESULTS

Alpha and beta diversity of the microbiome were different between CF and reference cohort at baseline, but no treatment effect was seen in the CF cohort between baseline and 3 months. Seven lipids increased with treatment. No differences were seen in bile acid concentrations after treatment in CF. At baseline, 403 lipids and unconjugated bile acids were different between pancreatic insufficient (PI-CF) and sufficient (PS-CF) groups and 107 lipids were different between PI-CF and PS-CF after 3 months of treatment.

CONCLUSIONS

The composition and diversity of the fecal microbiome were different in CF as compared to a healthy reference, and did not change after 3 months of ivacaftor. We detected modest differences in the fecal lipidome with treatment. Differences in lipid and bile acid profiles between PS-CF and PI-CF were attenuated after 3 months of treatment.

High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.

Associations of severe liver diseases with cataract using data from UK Biobank: a prospective cohort study

Background

Liver disease is linked to series of extrahepatic multisystem manifestations. However, little is known about the associations between liver and eye diseases, especially cataract, the global leading cause of blindness. We aimed to investigate whether severe liver diseases, including non-alcoholic fatty liver disease (NAFLD), alcoholic liver disease (ALD), viral hepatitis, and liver fibrosis and cirrhosis, were associated with an increased risk of the cataract.

Methods

A total of 326,558 participants without cataract at baseline enrolled in the UK Biobank between 2006 and 2010 were included in this prospective study. The exposures of interest were severe liver diseases (defined as hospital admission), including NAFLD, ALD, viral hepatitis and liver fibrosis and cirrhosis. The outcome was incident cataract. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs). Each liver disease was first treated as a binary time-varying variable to investigate its association with cataract, and then was treated as a ternary time-varying variable to examine the recent (liver disease within 0-5 years) vs. long-term (liver disease > 5 years) state associations with the risk of cataract.

Findings

After a median follow-up of 13.3 years (interquartile range, 12.5-14.0 years), 37,064 individuals were documented as developing cataract. Higher risk of cataract was found in those with severe NAFLD (HR, 1.47; 95% CI, 1.33-1.61), ALD (HR, 1.57; 95% CI, 1.28-1.94) and liver fibrosis and cirrhosis (HR, 1.58; 95% CI, 1.35-1.85), but not in individuals with viral hepatitis when exposure was treated as a binary time-varying variable (P = 0.13). When treating exposure as a ternary time-varying variable, an association between recently diagnosed viral hepatitis and cataract was also observed (HR, 1.55; 95% CI, 1.07-2.23). Results from the combined model suggested they were independent risk factors for incident cataract. No substantial changes were found in further sensitivity analyses.

Interpretation

Severe liver diseases, including NAFLD, ALD, liver fibrosis and cirrhosis and recently diagnosed viral hepatitis, were associated with cataract. The revelation of liver-eye connection suggests the importance of ophthalmic care in the management of liver disease, and the intervention precedence of patients with liver disease in the early screening and diagnosis of cataract.

Funding

National Natural Science Foundation of China, Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission, Clinical Research Plan of Shanghai Shenkang Hospital Development Center, Shanghai Municipal Key Clinical Specialty Program, the Guangdong Basic and Applied Basic Research Foundation and Shenzhen Science and Technology Program.

Gut Microbiota and Insulin Resistance: Understanding the Mechanism of Better Treatment of Type 2 Diabetes Mellitus

Gut microbiota refers to the population of trillions of microorganisms present in the human intestine. The gut microbiota in the gastrointestinal system is important for an individual's good health and well-being. The possibility of an intrauterine colonization of the placenta further suggests that the fetal environment before birth may also affect early microbiome development. Various factors influence the gut microbiota. Dysbiosis of microbiota may be associated with various diseases. Insulin regulates blood glucose levels, and disruption of the insulin signaling pathway results in insulin resistance. Insulin resistance or hyperinsulinemia is a pathological state in which the insulin-responsive cells have a diminished response to the hormone compared to normal physiological responses, resulting in reduced glucose uptake by the tissue cells. Insulin resistance is an important cause of type 2 diabetes mellitus. While there are various factors responsible for the etiology of insulin resistance, dysbiosis of gut microbiota may be an important contributing cause for metabolic disturbances. We discuss the mechanisms in skeletal muscles, adipose tissue, liver, and intestine by which insulin resistance can occur due to gut microbiota's metabolites. A better understanding of gut microbiota may help in the effective treatment of type 2 diabetes mellitus and metabolic syndrome.

Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance

BACKGROUND

Enormous clinical and biomedical researches have demonstrated that microbes are crucial to human health. Identifying associations between microbes and diseases can not only reveal potential disease mechanisms, but also facilitate early diagnosis and promote precision medicine. Due to the data perturbation and unsatisfactory latent representation, there is a significant room for improvement.

RESULTS

In this work, we proposed a novel framework, Multi-scale Variational Graph AutoEncoder embedding Wasserstein distance (MVGAEW) to predict disease-related microbes, which had the ability to resist data perturbation and effectively generate latent representations for both microbes and diseases from the perspective of distribution. First, we calculated multiple similarities and integrated them through similarity network confusion. Subsequently, we obtained node latent representations by improved variational graph autoencoder. Ultimately, XGBoost classifier was employed to predict potential disease-related microbes. We also introduced multi-order node embedding reconstruction to enhance the representation capacity. We also performed ablation studies to evaluate the contribution of each section of our model. Moreover, we conducted experiments on common drugs and case studies, including Alzheimer's disease, Crohn's disease, and colorectal neoplasms, to validate the effectiveness of our framework.

CONCLUSIONS

Significantly, our model exceeded other currently state-of-the-art methods, exhibiting a great improvement on the HMDAD database.

The Role of Mass Spectrometry in Hepatocellular Carcinoma Biomarker Discovery

Hepatocellular carcinoma (HCC) is the main liver malignancy and has a high mortality rate. The discovery of novel biomarkers for early diagnosis, prognosis, and stratification purposes has the potential to alleviate its disease burden. Mass spectrometry (MS) is one of the principal technologies used in metabolomics, with different experimental methods and machine types for different phases of the biomarker discovery process. Here, we review why MS applications are useful for liver cancer, explain the MS technique, and briefly summarise recent findings from metabolomic MS studies on HCC. We also discuss the current challenges and the direction for future research.

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

Background

Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe-disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.

Methods

We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe-disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.

Results

GPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.

Conclusion

The proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.

KGNMDA: A Knowledge Graph Neural Network Method for Predicting Microbe-Disease Associations

Accumulated studies discovered that various microbes in human bodies were closely related to complex human diseases and could provide new insight into drug development. Multiple computational methods were constructed to predict microbes that were potentially associated with diseases. However, most previous methods were based on single characteristics of microbes or diseases, that lacked important biological information related to microorganisms or diseases. Therefore, we constructed a knowledge graph centered on microorganisms and diseases from several existed databases to provide knowledgeable information for microbes and diseases. Then, we adopted a graph neural network method to learn representations of microbes and diseases from the constructed knowledge graph. After that, we introduced the Gaussian kernel similarity features of microbes and diseases to generate final representations of microbes and diseases. At last, we proposed a score function on final representations of microbes and diseases to predict scores of microbe-disease associations. Comprehensive experiments on the Human Microbe-Disease Association Database (HMDAD) dataset had demonstrated that our approach outperformed baseline methods. Furthermore, we implemented case studies on two important diseases (asthma and inflammatory bowel disease), the result demonstrated that our proposed model was effective in revealing the relationship between diseases and microbes. The source code of our model and the data were available on https://github.com/ChangzhiJiang/KGNMDA_master.

MSF-LRR: Multi-Similarity Information Fusion Through Low-Rank Representation to Predict Disease-Associated Microbes

An Increase in microbial activity is shown to be intimately connected with the pathogenesis of diseases. Considering the expense of traditional verification methods, researchers are working to develop high-efficiency methods for detecting potential disease-related microbes. In this article, a new prediction method, MSF-LRR, is established, which uses Low-Rank Representation (LRR) to perform multi-similarity information fusion to predict disease-related microbes. Considering that most existing methods only use one class of similarity, three classes of microbe and disease similarity are added. Then, LRR is used to obtain low-rank structural similarity information. Additionally, the method adaptively extracts the local low-rank structure of the data from a global perspective, to make the information used for the prediction more effective. Finally, a neighbor-based prediction method that utilizes the concept of collaborative filtering is applied to predict unknown microbe-disease pairs. As a result, the AUC value of MSF-LRR is superior to other existing algorithms under 5-fold cross-validation. Furthermore, in case studies, excluding originally known associations, 16 and 19 of the top 20 microbes associated with Bacterial Vaginosis and Irritable Bowel Syndrome, respectively, have been confirmed by the recent literature. In summary, MSF-LRR is a good predictor of potential microbe-disease associations and can contribute to drug discovery and biological research.

Optimized Systematic Review Tool: Application to Candidate Biomarkers for the Diagnosis of Hepatocellular Carcinoma

This review aims to develop an appropriate review tool for systematically collating metabolites that are dysregulated in disease and applies the method to identify novel diagnostic biomarkers for hepatocellular carcinoma (HCC). Studies that analyzed metabolites in blood or urine samples where HCC was compared with comparison groups (healthy, precirrhotic liver disease, cirrhosis) were eligible. Tumor tissue was included to help differentiate primary and secondary biomarkers. Searches were conducted on Medline and EMBASE. A bespoke "risk of bias" tool for metabolomic studies was developed adjusting for analytic quality. Discriminant metabolites for each sample type were ranked using a weighted score accounting for the direction and extent of change and the risk of bias of the reporting publication. A total of 84 eligible studies were included in the review (54 blood, 9 urine, and 15 tissue), with six studying multiple sample types. High-ranking metabolites, based on their weighted score, comprised energy metabolites, bile acids, acylcarnitines, and lysophosphocholines. This new review tool addresses an unmet need for incorporating quality of study design and analysis to overcome the gaps in standardization of reporting of metabolomic data. Validation studies, standardized study designs, and publications meeting minimal reporting standards are crucial for advancing the field beyond exploratory studies.

Novel Prognostic Signatures of Hepatocellular Carcinoma Based on Metabolic Pathway Phenotypes

Hepatocellular carcinoma is a disastrous cancer with an aberrant metabolism. In this study, we aimed to assess the role of metabolism in the prognosis of hepatocellular carcinoma. Ten metabolism-related pathways were identified to classify the hepatocellular carcinoma into two clusters: Metabolism_H and Metabolism_L. Compared with Metabolism_L, patients in Metabolism_H had lower survival rates with more mutated TP53 genes and more immune infiltration. Moreover, risk scores for predicting overall survival based on eleven differentially expressed metabolic genes were developed by the least absolute shrinkage and selection operator (LASSO)-Cox regression model in The Cancer Genome Atlas (TCGA) dataset, which was validated in the International Cancer Genome Consortium (ICGC) dataset. The immunohistochemistry staining of liver cancer patient specimens also identified that the 11 genes were associated with the prognosis of liver cancer patients. Multivariate Cox regression analyses indicated that the differentially expressed metabolic gene-based risk score was also an independent prognostic factor for overall survival. Furthermore, the risk score (AUC = 0.767) outperformed other clinical variables in predicting overall survival. Therefore, the metabolism-related survival-predictor model may predict overall survival excellently for HCC patients.

A Hypothesis From Metabolomics Analysis of Diabetic Retinopathy: Arginine-Creatine Metabolic Pathway May Be a New Treatment Strategy for Diabetic Retinopathy

Diabetic retinopathy is one of the serious complications of diabetes, which the leading causes of blindness worldwide, and its irreversibility renders the existing treatment methods unsatisfactory. Early detection and timely intervention can effectively reduce the damage caused by diabetic retinopathy. Metabolomics is a branch of systems biology and a powerful tool for studying pathophysiological processes, which can help identify the characteristic metabolic changes marking the progression of diabetic retinopathy, discover potential biomarkers to inform clinical diagnosis and treatment. This review provides an update on the known metabolomics biomarkers of diabetic retinopathy. Through comprehensive analysis of biomarkers, we found that the arginine biosynthesis is closely related to diabetic retinopathy. Meanwhile, creatine, a metabolite with arginine as a precursor, has attracted our attention due to its important correlation with diabetic retinopathy. We discuss the possibility of the arginine-creatine metabolic pathway as a therapeutic strategy for diabetic retinopathy.

Rodent models and metabolomics in non-alcoholic fatty liver disease: What can we learn?

Non-alcoholic fatty liver disease (NAFLD) prevalence has increased drastically in recent decades, affecting up to 25% of the world’s population. NAFLD is a spectrum of different diseases that starts with asymptomatic steatosis and continues with development of an inflammatory response called steatohepatitis, which can progress to fibrosis. Several molecular and metabolic changes are required for the hepatocyte to finally vary its function; hence a “multiple hit” hypothesis seems a more accurate proposal. Previous studies and current knowledge suggest that in most cases, NAFLD initiates and progresses through most of nine hallmarks of the disease, although the triggers and mechanisms for these can vary widely. The use of animal models remains crucial for understanding the disease and for developing tools based on biological knowledge. Among certain requirements to be met, a good model must imitate certain aspects of the human NAFLD disorder, be reliable and reproducible, have low mortality, and be compatible with a simple and feasible method. Metabolism studies in these models provides a direct reflection of the workings of the cell and may be a useful approach to better understand the initiation and progression of the disease. Metabolomics seems a valid tool for studying metabolic pathways and crosstalk between organs affected in animal models of NAFLD and for the discovery and validation of relevant biomarkers with biological understanding. In this review, we provide a brief introduction to NAFLD hallmarks, the five groups of animal models available for studying NAFLD and the potential role of metabolomics in the study of experimental NAFLD.

Differences in amino acid and lipid metabolism distinguish Crohn's from idiopathic/cryptoglandular perianal fistulas by tissue metabonomic profiling and may offer clues to underlying pathogenesis

INTRODUCTION

Few studies have investigated perianal fistula etiopathogenesis, and although the cryptoglandular theory is widely accepted in idiopathic cases, in Crohn's disease, it is thought to involve the interplay between microbiological, immunological and genetic factors. A pilot study was conducted to assess for metabolic variations in Crohn's perianal fistula tissue that might differ from that of idiopathic (cryptoglandular) perianal fistula tissue as a comparator. The goal was to identify any potential biomarkers of disease, which may improve the understanding of pathogenesis.

AIMS AND METHODS

Fistula tract biopsies were obtained from 30 patients with idiopathic perianal fistula and 20 patients with Crohn's anal fistula. Two different assays were used in an ultra-high-performance liquid chromatography system coupled with a mass spectrometric detector to achieve broad metabolome coverage. Univariate and multivariate statistical data analyses were used to identify differentiating metabolic features corresponding to the perianal fistula phenotype (i.e. Crohn's disease vs. idiopathic).

RESULTS

Significant orthogonal partial least squares discriminant analysis predictive models (validated with cross-validated-analysis of variance P value <0.05) differentiated metabolites from tissue samples from Crohn's vs. idiopathic anal fistula patients using both metabolic profiling platforms. A total of 41 metabolites were identified, suggesting alterations in pathways, including amino acid, carnitine and lipid metabolism.

CONCLUSION

Metabonomics may reveal biomarkers of Crohn's perianal fistula. Further work in larger numbers is required to validate the findings of these studies as well as cross-correlation with microbiome work to better understand the impact of host-gut/environment interactions in the pathophysiology of Crohn's and idiopathic perianal fistulas and identify novel therapeutic targets.

Metabolomics and health: from nutritional crops and plant-based pharmaceuticals to profiling of human biofluids

During the past decade metabolomics has emerged as one of the fastest developing branches of “-omics” technologies. Metabolomics involves documentation, identification, and quantification of metabolites through modern analytical platforms in various biological systems. Advanced analytical tools, such as gas chromatography–mass spectrometry (GC/MS), liquid chromatography–mass spectroscopy (LC/MS), and non-destructive nuclear magnetic resonance (NMR) spectroscopy, have facilitated metabolite profiling of complex biological matrices. Metabolomics, along with transcriptomics, has an influential role in discovering connections between genetic regulation, metabolite phenotyping and biomarkers identification. Comprehensive metabolite profiling allows integration of the summarized data towards manipulation of biosynthetic pathways, determination of nutritional quality markers, improvement in crop yield, selection of desired metabolites/genes, and their heritability in modern breeding. Along with that, metabolomics is invaluable in predicting the biological activity of medicinal plants, assisting the bioactivity-guided fractionation process and bioactive leads discovery, as well as serving as a tool for quality control and authentication of commercial plant-derived natural products. Metabolomic analysis of human biofluids is implemented in clinical practice to discriminate between physiological and pathological state in humans, to aid early disease biomarker discovery and predict individual response to drug therapy. Thus, metabolomics could be utilized to preserve human health by improving the nutritional quality of crops and accelerating plant-derived bioactive leads discovery through disease diagnostics, or through increasing the therapeutic efficacy of drugs via more personalized approach. Here, we attempt to explore the potential value of metabolite profiling comprising the above-mentioned applications of metabolomics in crop improvement, medicinal plants utilization, and, in the prognosis, diagnosis and management of complex diseases.

Association Mining to Identify Microbe Drug Interactions Based on Heterogeneous Network Embedding Representation

Accurately identifying microbe-drug associations plays a critical role in drug development and precision medicine. Considering that the conventional wet-lab method is time-consuming, labor-intensive and expensive, computational approach is an alternative choice. The increasing availability of numerous biological data provides a great opportunity to systematically understand complex interaction mechanisms between microbes and drugs. However, few computational methods have been developed for microbe drug prediction. In this work, we leverage multiple sources of biomedical data to construct a heterogeneous network for microbes and drugs, including drug-drug interactions, microbe-microbe interactions and microbe-drug associations. And then we propose a novel Heterogeneous Network Embedding Representation framework for Microbe-Drug Association prediction, named (HNERMDA), by combining metapath2vec with bipartite network recommendation. In this framework, we introduce metapath2vec, a heterogeneous network representation learning method, to learn low-dimensional embedding representations for microbes and drugs. Following that, we further design a bias bipartite network projection recommendation algorithm to improve prediction accuracy. Comprehensive experiments on two datasets, named MDAD and aBiofilm, demonstrated that our model consistently outperformed five baseline methods in three types of cross-validations. Case study on two popular drugs (i.e., Ciprofloxacin and Pefloxacin) further validated the effectiveness of our HNERMDA model in inferring potential target microbes for drugs.

Novel approaches to liver disease diagnosis and modeling

Lack of a prompt and accurate diagnosis remains on top of the list of challenges faced by patients with rare liver diseases. Although rare liver diseases affect a significant percentage of the population as a group, when taken singularly they represent unique diseases and the approaches used for diagnosis of common liver diseases are insufficient. However, the development of new methods for the acquisition of molecular and clinical data (i.e., genomic, proteomics, metabolomics) and computational tools for their analysis and integration, together with advances in modeling diseases using stem cell-based technology [i.e., induced pluripotent stem cells (iPSCs) and tissue organoids] represent a promising and powerful tool to improve the clinical management of these patients. This is the goal of precision medicine, a novel approach of modern medicine that aims at delivering a specific treatment based on disease-specific biological insights and individual profile. This review will discuss the application and advances of these technologies and how they represent a new opportunity in hepatology.

Metabonomics and the Gut Microbiome Associated With Primary Response to Anti-TNF Therapy in Crohn's Disease

BACKGROUND AND AIMS

Anti-tumour necrosis factor [anti-TNF] therapy is indicated for treatment of moderate to severe inflammatory bowel disease [IBD], but has a primary non-response rate of around 30%. We aim to use metabonomic and metataxonomic profiling to identify predictive biomarkers of anti-TNF response in Crohn's disease.

METHODS

Patients with luminal Crohn's disease, commencing anti-TNF therapy, were recruited with urine, faeces, and serum samples being collected at baseline and 3-monthly. Primary response was defined according to a combination of clinical and objective markers of inflammation. Samples were measured using three UPLC-MS assays: lipid, bile acid, and Hydrophillic Interaction Liquid Chromatography [HILIC] profiling with 16S rRNA gene sequencing of faeces.

RESULTS

Samples were collected from 76 Crohn's disease patients who were anti-TNF naïve and from 13 healthy controls. There were 11 responders, 37 non-responders, and 28 partial responders in anti-TNF-treated Crohn's patients. Histidine and cysteine were identified as biomarkers of response from polar metabolite profiling [HILIC] of serum and urine. Lipid profiling of serum and faeces found phosphocholines, ceramides, sphingomyelins, and triglycerides, and bile acid profiling identified primary bile acids to be associated with non-response to anti-TNF therapy, with higher levels of phase 2 conjugates in non-responders. Receiver operating curves for treatment response demonstrated 0.94 +/ -0.10 [faecal lipid], 0.81 +/- 0.17 [faecal bile acid], and 0.74 +/- 0.15 [serum bile acid] predictive ability for anti-TNF response in Crohn's disease.

CONCLUSIONS

This prospective, longitudinal cohort study of metabonomic and 16S rRNA gene sequencing analysis demonstrates that a range of metabolic biomarkers involving lipid, bile acid, and amino acid pathways may contribute to prediction of response to anti-TNF therapy in Crohn's disease.

PODCAST

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Microbes and complex diseases: from experimental results to computational models

Studies have shown that the number of microbes in humans is almost 10 times that of cells. These microbes have been proven to play an important role in a variety of physiological processes, such as enhancing immunity, improving the digestion of gastrointestinal tract and strengthening metabolic function. In addition, in recent years, more and more research results have indicated that there are close relationships between the emergence of the human noncommunicable diseases and microbes, which provides a novel insight for us to further understand the pathogenesis of the diseases. An in-depth study about the relationships between diseases and microbes will not only contribute to exploring new strategies for the diagnosis and treatment of diseases but also significantly heighten the efficiency of new drugs development. However, applying the methods of biological experimentation to reveal the microbe-disease associations is costly and inefficient. In recent years, more and more researchers have constructed multiple computational models to predict microbes that are potentially associated with diseases. Here, we start with a brief introduction of microbes and databases as well as web servers related to them. Then, we mainly introduce four kinds of computational models, including score function-based models, network algorithm-based models, machine learning-based models and experimental analysis-based models. Finally, we summarize the advantages as well as disadvantages of them and set the direction for the future work of revealing microbe-disease associations based on computational models. We firmly believe that computational models are expected to be important tools in large-scale predictions of disease-related microbes.

Predicting human microbe-disease associations via graph attention networks with inductive matrix completion

MOTIVATION

human microbes play a critical role in an extensive range of complex human diseases and become a new target in precision medicine. In silico methods of identifying microbe-disease associations not only can provide a deep insight into understanding the pathogenic mechanism of complex human diseases but also assist pharmacologists to screen candidate targets for drug development. However, the majority of existing approaches are based on linear models or label propagation, which suffers from limitations in capturing nonlinear associations between microbes and diseases. Besides, it is still a great challenge for most previous methods to make predictions for new diseases (or new microbes) with few or without any observed associations.

RESULTS

in this work, we construct features for microbes and diseases by fully exploiting multiply sources of biomedical data, and then propose a novel deep learning framework of graph attention networks with inductive matrix completion for human microbe-disease association prediction, named GATMDA. To our knowledge, this is the first attempt to leverage graph attention networks for this important task. In particular, we develop an optimized graph attention network with talking-heads to learn representations for nodes (i.e. microbes and diseases). To focus on more important neighbours and filter out noises, we further design a bi-interaction aggregator to enforce representation aggregation of similar neighbours. In addition, we combine inductive matrix completion to reconstruct microbe-disease associations to capture the complicated associations between diseases and microbes. Comprehensive experiments on two data sets (i.e. HMDAD and Disbiome) demonstrated that our proposed model consistently outperformed baseline methods. Case studies on two diseases, i.e. asthma and inflammatory bowel disease, further confirmed the effectiveness of our proposed model of GATMDA.

AVAILABILITY

python codes and data set are available at: https://github.com/yahuilong/GATMDA.

CONTACT

luojiawei@hnu.edu.cn.

NTSHMDA: Prediction of Human Microbe-Disease Association Based on Random Walk by Integrating Network Topological Similarity

Accumulating clinic evidences have demonstrated that the microbes residing in human bodies play a significantly important role in the formation, development, and progression of various complex human diseases. Identifying latent related microbes for disease could provide insight into human disease mechanisms and promote disease prevention, diagnosis, and treatment. In this paper, we first construct a heterogeneous network by connecting the disease similarity network and the microbe similarity network through known microbe-disease association network, and then develop a novel computational model to predict human microbe-disease associations based on random walk by integrating network topological similarity (NTSHMDA). Specifically, each microbe-disease association pair is regarded as a distinct relationship level and, thus, assigned different weights based on network topological similarity. The experimental results show that NTSHMDA outperforms some state-of-the-art methods with average AUCs of 0.9070, 0.8896 ± 0.0038 in the frameworks of Leave-one-out cross validation and 5-fold cross validation, respectively. In case studies, 9, 18, 38 and 9, 18, 45 out of top-10, 20, 50 candidate microbes are verified by recently published literatures for asthma and inflammatory bowel disease, respectively. In conclusion, NTSHMDA has potential ability to identify novel disease-microbe associations and can also provide valuable information for drug discovery and biological researches.

Porto-sinusoidal vascular disease: proposal and description of a novel entity

Portal hypertension in the absence of portal vein thrombosis and without cirrhosis, but with mild or moderate alterations of liver histology (eg, obliterative venopathy, nodular regenerative hyperplasia, or incomplete septal cirrhosis) is being increasingly recognised. Owing to the heterogeneity of causes and histological findings, a substantial number of terms have been used to describe such idiopathic non-cirrhotic portal hypertension. Patients with the same clinical and histological features exist, but without portal hypertension at the time of diagnosis. Therefore, improved criteria are needed to define this form of liver disease. Here, we propose the term porto-sinusoidal vascular disease, since all lesions found involve the portal venules or sinusoids. The definition of this entity is based on the characteristic absence of cirrhosis with or without signs of portal hypertension or histological lesions. The presence of known causes of liver disease does not rule out porto-sinusoidal vascular disease, but specific causes of vascular liver disease are excluded from its definition. The diagnosis of porto-sinusoidal vascular disease is based on liver biopsy and might include signs specific for portal hypertension with normal or mildly elevated liver stiffness values and no complete portal vein thrombosis. We provide simple diagnostic criteria, because agreement on a uniform nomenclature is an essential requirement for future collaborative studies.

WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network

BACKGROUND

An increasing number of biological and clinical evidences have indicated that the microorganisms significantly get involved in the pathological mechanism of extensive varieties of complex human diseases. Inferring potential related microbes for diseases can not only promote disease prevention, diagnosis and treatment, but also provide valuable information for drug development. Considering that experimental methods are expensive and time-consuming, developing computational methods is an alternative choice. However, most of existing methods are biased towards well-characterized diseases and microbes. Furthermore, existing computational methods are limited in predicting potential microbes for new diseases.

RESULTS

Here, we developed a novel computational model to predict potential human microbe-disease associations (MDAs) based on Weighted Meta-Graph (WMGHMDA). We first constructed a heterogeneous information network (HIN) by combining the integrated microbe similarity network, the integrated disease similarity network and the known microbe-disease bipartite network. And then, we implemented iteratively pre-designed Weighted Meta-Graph search algorithm on the HIN to uncover possible microbe-disease pairs by cumulating the contribution values of weighted meta-graphs to the pairs as their probability scores. Depending on contribution potential, we described the contribution degree of different types of meta-graphs to a microbe-disease pair with bias rating. Meta-graph with higher bias rating will be assigned greater weight value when calculating probability scores.

CONCLUSIONS

The experimental results showed that WMGHMDA outperformed some state-of-the-art methods with average AUCs of 0.9288, 0.9068 ±0.0031 in global leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. In the case studies, 9, 19, 37 and 10, 20, 45 out of top-10, 20, 50 candidate microbes were manually verified by previous reports for asthma and inflammatory bowel disease (IBD), respectively. Furthermore, three common human diseases (Crohn's disease, Liver cirrhosis, Type 1 diabetes) were adopted to demonstrate that WMGHMDA could be efficiently applied to make predictions for new diseases. In summary, WMGHMDA has a high potential in predicting microbe-disease associations.

Tissue and serum metabolomic phenotyping for diagnosis and prognosis of hepatocellular carcinoma

More than two-thirds of patients with hepatocellular carcinoma (HCC) cannot receive curative therapy and have poor survival due to late diagnosis and few prognostic directions. In our study, nontargeted and targeted metabolomics analyses were conducted by liquid chromatography-mass spectrometry to characterize metabolic features of HCC and identify diagnostic and prognostic biomarker candidate incorporating liver tissue and serum metabolites. A total of 552 subjects, including 432 with liver tissue and 120 with serum specimens, were recruited in China. In the discovery cohort, a series of 138 metabolites were identified to discriminate HCC tissues from matched nontumor tissues. Retinol presented with the highest area under the curve (AUC) of 0.991 and associated with Edmondson grade. In the validation cohort, all metabolites in retinol metabolism pathway were examined and the levels of retinol and retinal in tumor tissue and serum decreased in the order of normal to cirrhosis to HCC of Edmondson Grades I to IV. Retinol and retinal levels could also differentiate between HCC and cirrhosis, with AUCs of 0.996 and 0.994, respectively, in tissue and 0.812 and 0.744, respectively, in serum. The AUC of the combined retinol and retinal panel in serum was 0.852. Univariate and multivariate Cox regression identified this panel as an independent predictor for HCC and showed that low expression of retinol and retinal correlated with decreased survival time. In conclusion, the retinol metabolic signature had considerable diagnostic and prognostic value for identifying HCC patients who would benefit from prompt therapy and optimal prognostic direction.

Myricetin Ameliorates Ethanol-Induced Lipid Accumulation in Liver Cells by Reducing Fatty Acid Biosynthesis

SCOPE

Alcoholic liver disease is a serious threat to human health. The development of drug candidates from complementary and alternative medicines is an attractive approach. Myricetin can be found in fruit, vegetables, and herbs. This study investigates the protective effect of myricetin on ethanol-induced injury in mouse liver cells.

METHODS AND RESULTS

Oil-red O staining, assays of oxidative stress and measurements of inflammatory markers in mouse AML12 liver cells collectively demonstrate that myricetin elicits a curative effect on ethanol-induced injury. Next, the role of myricetin in the metabolic regulation of ethanol pathology in liver cells is assessed by gas chromatography coupled with mass spectrometry. Myricetin inhibits ethanol-stimulated fatty acid biosynthesis. Additionally, dodecanoic acid may be proposed as a potential biomarker related to ethanol pathology or myricetin therapy. It is also observed that myricetin enhances ethanol-induced inhibition of the mitochondrial electron transport chain. Moreover, fumaric acid is found to be a candidate biomarker related to ethanol toxicity or myricetin therapy. Quantitative reverse-transcription-PCR shows that ethanol-induced fatty acid synthase and sterol regulatory element-binding protein-1c mRNA levels are alleviated by myricetin. Finally, myricetin increases ethanol-induced inhibition of phosphorylation of AMP-activated protein kinase.

CONCLUSION

These results elucidate the pharmacological mechanism of myricetin on ethanol-induced lipid accumulation.

Autotaxin, bile acid profile and effect of ileal bile acid transporter inhibition in primary biliary cholangitis patients with pruritus

BACKGROUND AND AIMS

Pruritus is a common symptom in patients with primary biliary cholangitis (PBC) for which ileal bile acid transporter (IBAT) inhibition is emerging as a potential therapy. We explored the serum metabonome and gut microbiota profile in PBC patients with pruritus and investigated the effect of GSK2330672, an IBAT inhibitor.

METHODS

We studied fasting serum bile acids (BAs), autotaxin and faecal microbiota in 22 PBC patients with pruritus at baseline and after 2 weeks of GSK2330672 treatment. Control group included 31 asymptomatic PBC patients and 18 healthy volunteers. BA profiling was done by ultra performance liquid chromatography coupled to a mass spectrometry (UPLC-MS). Faecal microbiomes were analysed by 16S ribosomal RNA gene sequencing.

RESULTS

In PBC patients with pruritus, serum levels of total and glyco-conjugated primary BAs and autotaxin were significantly elevated. Autotaxin activity correlated significantly with tauro- and glyco-conjugated cholic acid (CA) and chenodeoxycholic acid (CDCA), both at baseline and after GSK2330672. GSK2330672 significantly reduced autotaxin and all tauro- and glyco- conjugated BAs and increased faecal levels of CA (P = 0.048) and CDCA (P = 0.027). Gut microbiota of PBC patients with pruritus was similar to control groups. GSK2330672 increased the relative abundance of Firmicutes (P = 0.033) and Clostridia (P = 0.04) and decreased Bacteroidetes (P = 0.033) and Bacteroidia (P = 0.04).

CONCLUSIONS

Pruritus in PBC does not show a distinct gut bacterial profile but is associated with elevated serum bile acid and autotaxin levels which decrease after IBAT inhibition. In cholestatic pruritus, a complex interplay between BAs and autotaxin is likely and may be modified by IBAT inhibition.

Metabolomics in the study of retinal health and disease

Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.

Prediction of Microbe-Disease Associations by Graph Regularized Non-Negative Matrix Factorization

More and more evidence shows that microbes play crucial roles in human health and disease. The exploration of the relationship between microbes and diseases will help people to better understand the underlying pathogenesis and have important implications for disease diagnosis and prevention. However, the known associations between microbes and diseases are very less. We proposed a new method called non-negative matrix factorization microbe-disease associations (NMFMDA), which used Gaussian interaction profile kernel similarity measure, to calculate microbial similarity and disease similarity, and applied a logistic function to regulate disease similarity. And, based on the known microbe-disease associations, a graph-regularized non-negative matrix factorization model was utilized to simultaneously identify potential microbe-disease associations. Moreover, fivefold cross-validation was utilized to evaluate the performance of our method. It reached the reliable area under receiver operating characteristic curve (AUC) of 0.8891, higher than other state-of-the-art methods. Finally, the case studies on three complex human diseases (i.e., asthma, inflammatory bowel disease, and colon cancer) demonstrated the good performance of our method. In summary, our method can be considered as an effective computational model for predicting potential disease-microbe associations.

Systems biology in hepatology: approaches and applications

Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.

Metabolic alterations in urine extracellular vesicles are associated to prostate cancer pathogenesis and progression

Urine contains extracellular vesicles (EVs) that concentrate molecules and protect them from degradation. Thus, isolation and characterisation of urinary EVs could increase the efficiency of biomarker discovery. We have previously identified proteins and RNAs with differential abundance in urinary EVs from prostate cancer (PCa) patients compared to benign prostate hyperplasia (BPH). Here, we focused on the analysis of the metabolites contained in urinary EVs collected from patients with PCa and BPH. Targeted metabolomics analysis of EVs was performed by ultra-high-performance liquid chromatography–mass spectrometry. The correlation between metabolites and clinical parameters was studied, and metabolites with differential abundance in PCa urinary EVs were detected and mapped into cellular pathways. We detected 248 metabolites belonging to different chemical families including amino acids and various lipid species. Among these metabolites, 76 exhibited significant differential abundance between PCa and BPH. Interestingly, urine EVs recapitulated many of the metabolic alterations reported in PCa, including phosphathidylcholines, acyl carnitines, citrate and kynurenine. Importantly, we found elevated levels of the steroid hormone, 3beta-hydroxyandros-5-en-17-one-3-sulphate (dehydroepiandrosterone sulphate) in PCa urinary EVs, in line with the potential elevation of androgen synthesis in this type of cancer. This work supports urinary EVs as a non-invasive source to infer metabolic changes in PCa.

Tsumura-Suzuki obese diabetic mice-derived hepatic tumors closely resemble human hepatocellular carcinomas in metabolism-related genes expression and bile acid accumulation

BACKGROUND AND AIMS

Tsumura-Suzuki obese diabetic (TSOD) is a good model of metabolic syndrome showing typical lesions found in nonalcoholic fatty liver disease and nonalcoholic steatohepatitis, and develops spontaneous hepatic tumors with a high frequency. Majority of the developing tumors overexpress glutamine synthetase (GS), which is used as a marker of hepatocellular carcinoma (HCC). The aim of this study is to assess the status of expression of metabolism-related genes and the level of bile acids in the TSOD mice-derived tumors and to determine the association with metabolic dysregulation between human HCC and TSOD mice-derived tumors.

METHODS

GS-positive hepatic tumors or adjacent normal tissues from 71-week-old male TSOD mice were subjected to immunohistochemical staining, quantitative RT-PCR (qRT-PCR), quantitation of cholic acid and taurocholic acid.

RESULTS

We found that downregulation of the rate-limiting enzyme for betaine synthesis (BADH), at both mRNA and protein levels in GS-positive TSOD mice-derived tumors. Furthermore, the bile acid receptor FXR and the bile acid excretion pump BSEP (Abcb11) were found to be downregulated, whereas BAAT and Akr1c14, involved in primary bile acid synthesis and bile acid conjugation, were found to be upregulated at mRNA level in GS-positive TSOD mice-derived tumors. BAAT and Akr1c14 were also overexpressed at protein levels. Total cholic acid was found to be increased in GS-positive TSOD mice-derived tumors.

CONCLUSION

Our results strongly support the significance of TSOD mice as a model of spontaneously developing HCC.

Aldose reductase inhibitor protects mice from alcoholic steatosis by repressing saturated fatty acid biosynthesis

Alcoholic liver injury results in morbidity and mortality worldwide, but there are currently no effective and safe therapeutics. Previously we demonstrated that aldose reductase (AR) inhibitor ameliorated alcoholic hepatic steatosis. To clarify the mechanism whereby AR inhibitor improves alcoholic hepatic steatosis, herein we investigated the effect of AR inhibitor on hepatic metabolism in mice fed a Lieber-DeCarli liquid diet with 5% ethanol. Nontargeted metabolomics showed carbohydrates and lipids were characteristic categories in ethanol diet-fed mice with or without AR inhibitor treatment, whereas AR inhibitor mainly affected carbohydrates and peptides. Ethanol-induced galactose metabolism and fatty acid biosynthesis are important for the induction of hepatic steatosis, while AR inhibitor impaired galactose metabolism without perturbing fatty acid biosynthesis. In parallel with successful treatment of steatosis, AR inhibitor suppressed ethanol-activated galactose metabolism and saturated fatty acid biosynthesis. Sorbitol in galactose metabolism and stearic acid in saturated fatty acid biosynthesis were potential biomarkers responsible for ethanol or ethanol plus AR inhibitor treatment. In vitro analysis confirmed that exogenous addition of sorbitol augmented ethanol-induced steatosis and stearic acid. These findings not only reveal metabolic patterns associated with disease and treatment, but also shed light on functional biomarkers contribute to AR inhibition therapy.

The utility of biomarkers in hepatocellular carcinoma: review of urine-based 1H-NMR studies - what the clinician needs to know

Hepatocellular carcinoma (HCC) is the fifth most common malignancy, the third most common cause of cancer death, and the most common primary liver cancer. Overall, there is a need for more reliable biomarkers for HCC, as those currently available lack sensitivity and specificity. For example, the current gold-standard biomarker, serum alpha-fetoprotein, has a sensitivity of roughly only 70%. Cancer cells have different characteristic metabolic signatures in biofluids, compared to healthy cells; therefore, metabolite analysis in blood or urine should lead to the detection of suitable candidates for the detection of HCC. With the advent of metabonomics, this has increased the potential for new biomarker discovery. In this article, we look at approaches used to identify biomarkers of HCC using proton nuclear magnetic resonance (¹H-NMR) spectroscopy of urine samples. The various multivariate statistical analysis techniques used are explained, and the process of biomarker identification is discussed, with a view to simplifying the knowledge base for the average clinician.

A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network

Since the microbiome has a significant impact on human health and disease, microbe-disease associations can be utilized as a valuable resource for understanding disease pathogenesis and promoting disease diagnosis and prognosis. Accordingly, it is necessary for researchers to achieve a comprehensive and deep understanding of the associations between microbes and diseases. Nevertheless, to date, little work has been achieved in implementing novel human microbe-disease association prediction models. In this paper, we develop a novel computational model to predict potential microbe-disease associations by bi-random walk on the heterogeneous network (BiRWHMDA). The heterogeneous network was constructed by connecting the microbe similarity network and the disease similarity network via known microbe-disease associations. Microbe similarity and disease similarity were calculated by the Gaussian interaction profile kernel similarity measure; moreover, a logistic function was applied to regulate disease similarity. Additionally, leave-one-out cross validation and 5-fold cross validation were implemented to evaluate the predictive performance of our method; both cross validation methods performed well. The leave-one-out cross validation experiment results illustrate that our method outperforms other previously proposed methods. Furthermore, case studies on asthma and inflammatory bowel disease prove the favorable performance of our method. In conclusion, our method can be considered as an effective computational model for predicting novel microbe-disease associations.

Diagnosis of coinfection by schistosomiasis and viral hepatitis B or C using 1H NMR-based metabonomics

BACKGROUND

Diagnosis of liver involvement due to schistosomiasis in asymptomatic patients from endemic areas previously diagnosed with chronic hepatitis B (HBV) or C (HCV) and periportal fibrosis is challenging. H-1 Nuclear Magnetic Resonance (NMR)-based metabonomics strategy is a powerful tool for providing a profile of endogenous metabolites of low molecular weight in biofluids in a non-invasive way. The aim of this study was to diagnose periportal fibrosis due to schistosomiasis mansoni in patients with chronic HBV or HCV infection through NMR-based metabonomics models.

METHODOLOGY/PRINCIPAL FINDINGS

The study included 40 patients divided into two groups: (i) 18 coinfected patients with schistosomiasis mansoni and HBV or HCV; and (ii) 22 HBV or HCV monoinfected patients. The serum samples were analyzed through H-1 NMR spectroscopy and the models were based on Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA). Ultrasonography examination was used to ascertain the diagnosis of periportal fibrosis. Exploratory analysis showed a clear separation between coinfected and monoinfected samples. The supervised model built from PLS-DA showed accuracy, R2 and Q2 values equal to 100%, 98.1% and 97.5%, respectively. According to the variable importance in the projection plot, lactate serum levels were higher in the coinfected group, while the signals attributed to HDL serum cholesterol were more intense in the monoinfected group.

CONCLUSIONS/SIGNIFICANCE

The metabonomics models constructed in this study are promising as an alternative tool for diagnosis of periportal fibrosis by schistosomiasis in patients with chronic HBV or HCV infection from endemic areas for Schistosoma mansoni.

A Synthetic-Biology-Inspired Therapeutic Strategy for Targeting and Treating Hepatogenous Diabetes

Hepatogenous diabetes is a complex disease that is typified by the simultaneous presence of type 2 diabetes and many forms of liver disease. The chief pathogenic determinant in this pathophysiological network is insulin resistance (IR), an asymptomatic disease state in which impaired insulin signaling in target tissues initiates a variety of organ dysfunctions. However, pharmacotherapies targeting IR remain limited and are generally inapplicable for liver disease patients. Oleanolic acid (OA) is a plant-derived triterpenoid that is frequently used in Chinese medicine as a safe but slow-acting treatment in many liver disorders. Here, we utilized the congruent pharmacological activities of OA and glucagon-like-peptide 1 (GLP-1) in relieving IR and improving liver and pancreas functions and used a synthetic-biology-inspired design principle to engineer a therapeutic gene circuit that enables a concerted action of both drugs. In particular, OA-triggered short human GLP-1 (shGLP-1) expression in hepatogenous diabetic mice rapidly and simultaneously attenuated many disease-specific metabolic failures, whereas OA or shGLP-1 monotherapy failed to achieve corresponding therapeutic effects. Collectively, this work shows that rationally engineered synthetic gene circuits are capable of treating multifactorial diseases in a synergistic manner by multiplexing the targeting efficacies of single therapeutics.

Simultaneous Quantification of Amino Metabolites in Multiple Metabolic Pathways Using Ultra-High Performance Liquid Chromatography with Tandem-mass Spectrometry

Metabolites containing amino groups cover multiple pathways and play important roles in redox homeostasis and biosyntheses of proteins, nucleotides and neurotransmitters. Here, we report a new method for simultaneous quantification of 124 such metabolites. This is achieved by derivatization-assisted sensitivity enhancement with 5-aminoisoquinolyl-N-hydroxysuccinimidyl carbamate (5-AIQC) followed with comprehensive analysis using ultra-high performance liquid chromatography and electrospray ionization tandem mass spectrometry (UHPLC-MS/MS). In an one-pot manner, this quantification method enables simultaneous coverage of 20 important metabolic pathways including protein biosynthesis/degradation, biosyntheses of catecholamines, arginine and glutathione, metabolisms of homocysteine, taurine-hypotaurine etc. Compared with the reported ones, this method is capable of simultaneously quantifying thiols, disulfides and other oxidation-prone analytes in a single run and suitable for quantifying aromatic amino metabolites. This method is also much more sensitive for all tested metabolites with LODs well below 50 fmol (at sub-fmol for most tested analytes) and shows good precision for retention time and quantitation with inter-day and intra-day relative standard deviations (RSDs) below 15% and good recovery from renal cancer tissue, rat urine and plasma. The method was further applied to quantify the amino metabolites in silkworm hemolymph from multiple developmental stages showing its applicability in metabolomics and perhaps some clinical chemistry studies.

Biology of the Microbiome 2: Metabolic Role

The human microbiome is a new frontier in biology and one that is helping to define what it is to be human. Recently, we have begun to understand that the "communication" between the host and its microbiome is via a metabolic superhighway. By interrogating and understanding the molecules involved we may start to know who the main players are, and how we can modulate them and the mechanisms of health and disease.

Metabolomic Fingerprinting in the Comprehensive Study of Liver Changes Associated with Onion Supplementation in Hypercholesterolemic Wistar Rats

The consumption of functional ingredients has been suggested to be a complementary tool for the prevention and management of liver disease. In this light, processed onion can be considered as a source of multiple bioactive compounds with hepatoprotective properties. The liver fingerprint of male Wistar rats (n = 24) fed with three experimental diets (control (C), high-cholesterol (HC), and high-cholesterol enriched with onion (HCO) diets) was obtained through a non-targeted, multiplatform metabolomics approach to produce broad metabolite coverage. LC-MS, CE-MS and GC-MS results were subjected to univariate and multivariate analyses, providing a list of significant metabolites. All data were merged in order to figure out the most relevant metabolites that were modified by the onion ingredient. Several relevant metabolic changes and related metabolic pathways were found to be impacted by both HC and HCO diet. The model highlighted several metabolites (such as hydroxybutyryl carnitine and palmitoyl carnitine) modified by the HCO diet. These findings could suggest potential impairments in the energy−lipid metabolism, perturbations in the tricarboxylic acid cycle (TCA) cycle and β-oxidation modulated by the onion supplementation in the core of hepatic dysfunction. Metabolomics shows to be a valuable tool to evaluate the effects of complementary dietetic approaches directed to hepatic damage amelioration or non-alcoholic fatty liver disease (NAFLD) prevention.

Metabolic Phenotyping of Diet and Dietary Intake

Nutrition provides the building blocks for growth, repair, and maintenance of the body and is key to maintaining health. Exposure to fast foods, mass production of dietary components, and wider importation of goods have challenged the balance between diet and health in recent decades, and both scientists and clinicians struggle to characterize the relationship between this changing dietary landscape and human metabolism with its consequent impact on health. Metabolic phenotyping of foods, using high-density data-generating technologies to profile the biochemical composition of foods, meals, and human samples (pre- and postfood intake), can be used to map the complex interaction between the diet and human metabolism and also to assess food quality and safety. Here, we outline some of the techniques currently used for metabolic phenotyping and describe key applications in the food sciences, ending with a broad outlook at some of the newer technologies in the field with a view to exploring their potential to address some of the critical challenges in nutritional science.