Novel methodologies in metabolic profiling with a focus on molecular diagnostic applications

Combined untargeted metabolomics and network pharmacology approaches to reveal the therapeutic role of withanolide B in psoriasis

Psoriasis is a refractory inflammatory skin disorder in which keratinocyte hyperproliferation is a crucial pathogenic factor. Up to now, it is commonly acknowledged that psoriasis has a tight connection with metabolic disorders. Withanolides from Datura metel L. (DML) have been proved to possess anti-inflammatory and anti-proliferative properties in multiple diseases including psoriasis. Withanolide B (WB) is one of the abundant molecular components in DML. However, existing experimental studies regarding the potential effects and mechanisms of WB on psoriasis still remain lacking. Present study aimed to integrate network pharmacology and untargeted metabolomics strategies to investigate the therapeutic effects and mechanisms of WB on metabolic disorders in psoriasis. In our study, we observed that WB might effectively improve the symptoms of psoriasis and alleviate the epidermal hyperplasia in imiquimod (IMQ)-induced psoriasis-like mice. Both network pharmacology and untargeted metabolomics results suggested that arachidonic acid metabolism and arginine and proline metabolism pathways were linked to the treatment of psoriasis with WB. Meanwhile, we also found that WB may affect the expression of regulated enzymes 5-lipoxygenase (5-LOX), 12-LOX, ornithine decarboxylase 1 (ODC1) and arginase 1 (ARG1) in the arachidonic acid metabolism and arginine and proline metabolism pathways. In summary, this paper showed the potential metabolic mechanisms of WB against psoriasis and suggested that WB would have greater potential in psoriasis treatment.

Metabolomics Approaches for the Diagnosis, Treatment, and Better Disease Management of Viral Infections

Metabolomics is an analytical approach that involves profiling and comparing the metabolites present in biological samples. This scoping review article offers an overview of current metabolomics approaches and their utilization in evaluating metabolic changes in biological fluids that occur in response to viral infections. Here, we provide an overview of metabolomics methods including high-throughput analytical chemistry and multivariate data analysis to identify the specific metabolites associated with viral infections. This review also focuses on data interpretation and applications designed to improve our understanding of the pathogenesis of these viral diseases.

Hyperoside Nanomicelles Alleviate Atherosclerosis by Modulating the Lipid Profile and Intestinal Flora Structure in High-Fat-Diet-Fed Apolipoprotein-E-Deficient Mice

Atherosclerosis (AS) is a serious threat to human health and the main pathological basis of cardiovascular disease. Hyperoside (Hyp), a flavonoid found mainly in traditional Chinese herbs, can exert antitumor, anti-inflammatory, antioxidant, and cardiovascular-protective effects. Herein, we prepared hybrid nanomicelles (HFT) comprising Hyp loaded into pluronic F-127 and polyethylene glycol 1000 vitamin E succinate and assessed their effects on AS. To establish an AS model, apolipoprotein-E-deficient (ApoE^-/-) mice were fed a high-fat diet. We then analyzed the effects of HFT on AS-induced changes in aortic tissues and metabolic markers, simultaneously assessing changes in gut flora community structure. In mice with AS, HFT significantly reduced the aortic plaque area; decreased levels of total cholesterol, triglyceride, low-density lipoprotein cholesterol, inflammatory factors, and inducible nitric oxide synthase (NOS); increased high-density lipoprotein cholesterol, endothelial NOS, superoxide dismutase, catalase, and glutathione levels; and promoted the proliferation of beneficial gut bacteria. HFT could regulate intestinal flora structure and lipid metabolism and inhibit inflammatory responses. These beneficial effects may be mediated by inhibiting nuclear factor kappa B signal activation, reducing inflammatory factor expression and improving gut microflora structure and dyslipidemia. The present study provides an empirical basis for the development and clinical application of new dosage forms of Hyp.

Optimization of a GC-MS Injection-Port Derivatization Methodology to Enhance Metabolomics Analysis Throughput in Biological Samples

Advances in metabolomics analysis and data treatment increase the knowledge of complex biological systems. One of the most used methodologies is gas chromatography-mass spectrometry (GC-MS) due to its robustness, high separation efficiency, and reliable peak identification through curated databases. However, methodologies are not standardized, and the derivatization steps in GC-MS can introduce experimental errors and take considerable time, exposing the samples to degradation. Here, we propose the injection-port derivatization (IPD) methodology to increase the throughput in plasma metabolomics analysis by GC-MS. The IPD method was evaluated and optimized for different families of metabolites (organic acids, amino acids, fatty acids, sugars, sugar phosphates, etc.) in terms of residence time, injection-port temperature, and sample/derivatization reagent ratio. Finally, the method's usefulness was validated in a study consisting of a cohort of obese patients with or without nonalcoholic steatohepatitis. Our results show a fast, reproducible, precise, and reliable method for the analysis of biological samples by GC-MS. Raw data are publicly available at MetaboLights with Study Identifier MTBLS5151.

Lactic Acid Bacteria Exopolysaccharides Producers: A Sustainable Tool for Functional Foods

Lactic acid bacteria (LAB) used in the food industry, mainly for the production of dairy products, are able to synthetize exopolysaccharides (EPS). EPS play a central role in the assessment of rheological and sensory characteristics of dairy products since they positively influence texture and organoleptic properties. Besides these, EPS have gained relevant interest for pharmacological and nutraceutical applications due to their biocompatibility, non-toxicity and biodegradability. These bioactive compounds may act as antioxidant, cholesterol-lowering, antimicrobial and prebiotic agents. This review provides an overview of exopolysaccharide-producing LAB, with an insight on the factors affecting EPS production, their dairy industrial applications and health benefits.

The Relationship Between the Gut Microbiome and Neurodegenerative Diseases

Many recent studies have shown that the gut microbiome plays important roles in human physiology and pathology. Also, microbiome-based therapies have been used to improve health status and treat diseases. In addition, aging and neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease, have become topics of intense interest in biomedical research. Several researchers have explored the links between these topics to study the potential pathogenic or therapeutic effects of intestinal microbiota in disease. But the exact relationship between neurodegenerative diseases and gut microbiota remains unclear. As technology advances, new techniques for studying the microbiome will be developed and refined, and the relationship between diseases and gut microbiota will be revealed. This article summarizes the known interactions between the gut microbiome and neurodegenerative diseases, highlighting assay techniques for the gut microbiome, and we also discuss the potential therapeutic role of microbiome-based therapies in diseases.

Library-assisted nonlinear blind separation and annotation of pure components from a single 1H nuclear magnetic resonance mixture spectra

Due to its capability for high-throughput screening ¹H nuclear magnetic resonance (NMR) spectroscopy is commonly used for metabolite research. The key problem in ¹H NMR spectroscopy of multicomponent mixtures is overlapping of component signals and that is increasing with the number of components, their complexity and structural similarity. It makes metabolic profiling, that is carried out through matching acquired spectra with metabolites from the library, a hard problem. Here, we propose a method for nonlinear blind separation of highly correlated components spectra from a single ¹H NMR mixture spectra. The method transforms a single nonlinear mixture into multiple high-dimensional reproducible kernel Hilbert Spaces (mRKHSs). Therein, highly correlated components are separated by sparseness constrained nonnegative matrix factorization in each induced RKHS. Afterwards, metabolites are identified through comparison of separated components with the library comprised of 160 pure components. Thereby, a significant number of them are expected to be related with diabetes type 2. Conceptually similar methodology for nonlinear blind separation of correlated components from two or more mixtures is presented in the Supplementary material. Single-mixture blind source separation is exemplified on: (i) annotation of five components spectra separated from one ¹H NMR model mixture spectra; (ii) annotation of fifty five metabolites separated from one ¹H NMR mixture spectra of urine of subjects with and without diabetes type 2. Arguably, it is for the first time a method for blind separation of a large number of components from a single nonlinear mixture has been proposed. Moreover, the proposed method pinpoints urinary creatine, glutamic acid and 5-hydroxyindoleacetic acid as the most prominent metabolites in samples from subjects with diabetes type 2, when compared to healthy controls.

Untargeted Metabolomics Reveal Lipid Alterations upon 2-Deoxyglucose Treatment in Human HaCaT Keratinocytes

The glucose analogue 2-deoxyglucose (2-DG) impedes cancer progression in animal models and is currently being assessed as an anticancer therapy, yet the mode of action of this drug of high clinical significance has not been fully delineated. In an attempt to better characterize its pharmacodynamics, an integrative UPLC-Q-Exactive-based joint metabolomic and lipidomic approach was undertaken to evaluate the metabolic perturbations induced by this drug in human HaCaT keratinocyte cells. R-XCMS data processing and subsequent multivariate pattern recognition, metabolites identification, and pathway analyses identified eight metabolites that were most significantly changed upon a 3 h 2-DG exposure. Most of these dysregulated features were emphasized in the course of lipidomic profiling and could be identified as ceramide and glucosylceramide derivatives, consistently with their involvement in cell death programming. Even though metabolomic analyses did not generally afford such clear-cut dysregulations, some alterations in phosphatidylcholine and phosphatidylethanolamine derivatives could be highlighted as well. Overall, these results support the adequacy of the proposed analytical workflow and might contribute to a better understanding of the mechanisms underlying the promising effects of 2-DG.

Metabolomics for clinical use and research in chronic kidney disease

Chronic kidney disease (CKD) has a high prevalence in the general population and is associated with high mortality; a need therefore exists for better biomarkers for diagnosis, monitoring of disease progression and therapy stratification. Moreover, very sensitive biomarkers are needed in drug development and clinical research to increase understanding of the efficacy and safety of potential and existing therapies. Metabolomics analyses can identify and quantify all metabolites present in a given sample, covering hundreds to thousands of metabolites. Sample preparation for metabolomics requires a very fast arrest of biochemical processes. Present key technologies for metabolomics are mass spectrometry and proton nuclear magnetic resonance spectroscopy, which require sophisticated biostatistic and bioinformatic data analyses. The use of metabolomics has been instrumental in identifying new biomarkers of CKD such as acylcarnitines, glycerolipids, dimethylarginines and metabolites of tryptophan, the citric acid cycle and the urea cycle. Biomarkers such as c-mannosyl tryptophan and pseudouridine have better performance in CKD stratification than does creatinine. Future challenges in metabolomics analyses are prospective studies and deconvolution of CKD biomarkers from those of other diseases such as metabolic syndrome, diabetes mellitus, inflammatory conditions, stress and cancer.

Exopolysaccharides Produced by Lactic Acid Bacteria and Bifidobacteria as Fermentable Substrates by the Intestinal Microbiota

The functional food market, including products formulated to maintain a "healthy" gut microbiota, i.e. probiotics and prebiotics, has increased enormously since the end of the last century. In order to favor the competitiveness of this sector, as well as to increase our knowledge of the mechanisms of action upon human health, new probiotic strains and prebiotic substrates are being studied. This review discusses the use of exopolysaccharides (EPS), both homopolysaccharides (HoPS) and heteropolysaccharides (HePS), synthesized by lactic acid bacteria and bifidobacteria as potential prebiotics. These extracellular carbohydrate polymers synthesized by some gut inhabitants seem to be resistant to gastrointestinal digestion; these are susceptible as well to biodegradability by the intestinal microbiota depending on both the physicochemical characteristics of EPS and the pool of glycolytic enzymes harbored by microbiota. Therefore, although the chemical composition of these HoPS and HePS is different, both can be fermentable substrates by intestinal inhabitants and good candidates as prebiotic substrates. However, there are limitations for their use as additives in the food industry due to, on the one hand, their low production yield and, on the other hand, a lack of clinical studies demonstrating the functionality of these biopolymers.

Key elements of metabolomics in the study of biomarkers of diabetes

Metabolomics is instrumental in the analysis of disease mechanisms and biomarkers of disease. The human metabolome is influenced by genetics and environmental interactions and reveals characteristic signatures of disease. Population studies with metabolomics require special study designs and care needs to be taken with pre-analytics. Gas chromatography coupled to mass spectrometry, liquid chromatography coupled to mass spectrometry or NMR are popular techniques used for metabolomic analyses in human cohorts. Metabolomics has been successfully used in the biomarker search for disease prediction and progression, for analyses of drug action and for the development of companion diagnostics. Several metabolites or metabolite classes identified by metabolomics have gained much attention in the field of diabetes research in the search for early disease detection, differentiation of progressor types and compliance with medication. This review summarises a presentation given at the 'New approaches beyond genetics' symposium at the 2015 annual meeting of the EASD. It is accompanied by another review from this symposium by Bernd Mayer (DOI: 10.1007/s00125-016-4032-2 ) and an overview by the Session Chair, Leif Groop (DOI: 10.1007/s00125-016-4014-4 ).

Recent advances in lipidomics for disease research

Lipidomics is an important branch of metabolomics, which aims at the detailed analysis of lipid species and their multiple roles in the living system. In recent years, the development of various analytical methods for effective identification and characterization of lipids has greatly promoted the process of lipidomics. Meanwhile, as many diseases demonstrate a remarkable alteration in lipid profiles compared with that of healthy people, lipidomics has been extensively introduced to disease research. The comprehensive lipid profiling provides a chance to discover novel biomarkers for specific disease. In addition, it plays a crucial role in the study of lipid metabolism, which could illuminate the pathogenesis of diseases. In this review, after brief discussion of analytical methods for lipidomics in clinical research, we focus on the recent advances of lipidomics related to four types of diseases, including cancer, atherosclerosis, diabetes mellitus, and Alzheimer's disease.

Recent highlights of metabolomics in chinese medicine syndrome research

Chinese medicine syndrome (CMS, “ZHENG” in Chinese) is an understanding of the regularity of disease occurrence and development as well as a certain stage of a comprehensive response of patients with body condition. However, because of the complexity of CMS and the limitation of present investigation method, the research for deciphering the scientific basis and systematic features of CMS is difficult to go further. Metabolomics enables mapping of early biochemical changes in disease and hence provides an opportunity to develop predictive biomarkers. Moreover, its method and design resemble those of traditional Chinese medicine (TCM) which focuses on human disease via the integrity of close relationship between body and syndromes. In the systemic context, metabolomics has a convergence with TCM syndrome; therefore it could provide useful tools for exploring essence of CMS disease, facilitating personalized TCM, and will help to in-depth understand CMS. The integration of the metabolomics and CMS aspects will give promise to bridge the gap between Chinese and Western medicine and help catch the traditional features of CMS. In this paper, particular attention will be paid to the past successes in applications of robust metabolomic approaches to contribute to low-molecular-weight metabolites (biomarkers) discovery in CMS research and development.

Identification of biomarkers for apoptosis in cancer cell lines using metabolomics: tools for individualized medicine

BACKGROUND

Metabolomics is a versatile unbiased method to search for biomarkers of human disease. In particular, one approach in cancer therapy is to promote apoptosis in tumour cells; this could be improved with specific biomarkers of apoptosis for monitoring treatment. We recently observed specific metabolic patterns in apoptotic cell lines; however, in that study, apoptosis was only induced with one pro-apoptotic agent, staurosporine.

OBJECTIVE

The aim of this study was to find novel biomarkers of apoptosis by verifying our previous findings using two further pro-apoptotic agents, 5-fluorouracil and etoposide, that are commonly used in anticancer treatment.

METHODS

Metabolic parameters were assessed in HepG2 and HEK293 cells using the newborn screening assay adapted for cell culture approaches, quantifying the levels of amino acids and acylcarnitines with mass spectrometry.

RESULTS

We were able to identify apoptosis-specific changes in the metabolite profile. Moreover, the amino acids alanine and glutamate were both significantly up-regulated in apoptotic HepG2 and HEK293 cells irrespective of the apoptosis inducer.

CONCLUSION

Our observations clearly indicate the potential of metabolomics in detecting metabolic biomarkers applicable in theranostics and for monitoring drug efficacy.

Selection of Analytical Methodology for Metabolomics

The main challenge in metabolomics analysis is the separation, identification and quantification of a large number of known or unknown metabolites in complex samples. The correct selection of sample collection and preparation method, type of separation methodology and detection are all crucial steps in the analysis of metabolomics. This chapter provides an overview of and general guidelines for strategies involved in the analysis of metabolomics, and different chromatographic techniques used in metabolomics studies are briefly presented. The methods are compared and the main features of the separation methods are listed. Finally, general conclusions and future perspectives are provided.

Metabolomics in nutrition research: current status and perspectives

Metabolomics is the study of metabolites present in biological samples such as biofluids, tissue/cellular extracts and culture media. Combining metabolomic data with multivariate data analysis tools allows us to study alterations in metabolic pathways following different perturbations. Examples of perturbations can be disease state, drug or nutritional interventions with successful applications in the fields of drug toxicology, biomarker development and nutrition research. Application of metabolomics to nutrition research is increasing and applications range from assessing novel biomarkers of dietary intake to application of metabolomics in intervention studies. The present review highlights the use of metabolomics in nutrition research.

Metabolomics in the studies of islet autoimmunity and type 1 diabetes

The metabolome is sensitive to genetic and environmental factors contributing to complex diseases such as type 1 diabetes (T1D). Metabolomics is the study of biochemical and physiological processes involving metabolites. It is therefore one of the key platforms for the discovery and study of pathophysiological phenomena leading to T1D and the development of T1D-associated complications. Although the application of metabolomics in T1D research is still rare, metabolomic research has already advanced across the full spectrum, from disease progression to the development of diabetic complications. Metabolomic studies in T1D have contributed to an improved etiopathogenic understanding and demonstrated their potential in the clinic. For example, metabolomic data from recent T1D studies suggest that a specific metabolic profile, or metabotype, precedes islet autoimmunity and the development of overt T1D. These early metabolic changes are attributed to many biochemical pathways, thus suggesting a systemic change in metabolism which may be inborn. Based on this evidence, the role of the metabolome in the progression to T1D is therefore to facilitate specific biochemical processes associated with T1D, and to contribute to the development of a vulnerable state in which disease is more likely to be triggered. This may have important implications for the understanding of T1D pathophysiology and early disease detection and prevention.

Metabolomics platforms for genome wide association studies--linking the genome to the metabolome

Genome-wide association studies (GWAS) reveal links between genetic variance and predisposition to disease. With the advent of modern 'omics-technologies', GWAS can now identify the genetic factors that influence intermediate traits on pathways to disease, such as blood concentrations of carbohydrates, lipids, amino acids, and secondary metabolites, hormones and signal molecules. At the example of recent GWAS with metabolic traits (mGWAS) we review the high-throughput screening approaches that are available to further advance the field.