Ultraperformance liquid chromatography-mass spectrometry based comprehensive metabolomics combined with pattern recognition and network analysis methods for characterization of metabolites and metabolic pathways from biological data sets

Metabolomics and proteomics insights into subacute ruminal acidosis etiology and inhibition of proliferation of yak rumen epithelial cells in vitro

BACKGROUND

Untargeted metabolomics and proteomics were employed to investigate the intracellular response of yak rumen epithelial cells (YRECs) to conditions mimicking subacute rumen acidosis (SARA) etiology, including exposure to short-chain fatty acids (SCFA), low pH5.5 (Acid), and lipopolysaccharide (LPS) exposure for 24 h.

RESULTS

These treatments significantly altered the cellular morphology of YRECs. Metabolomic analysis identified significant perturbations with SCFA, Acid and LPS treatment affecting 259, 245 and 196 metabolites (VIP > 1, P < 0.05, and fold change (FC) ≥ 1.5 or FC ≤ 0.667). Proteomic analysis revealed that treatment with SCFA, Acid, and LPS resulted in differential expression of 1251, 1396, and 242 proteins, respectively (FC ≥ 1.2 or ≤ 0.83, P < 0.05, FDR < 1%). Treatment with SCFA induced elevated levels of metabolites involved in purine metabolism, glutathione metabolism, and arginine biosynthesis, and dysregulated proteins associated with actin cytoskeleton organization and ribosome pathways. Furthermore, SCFA reduced the number, morphology, and functionality of mitochondria, leading to oxidative damage and inhibition of cell survival. Gene expression analysis revealed a decrease the genes expression of the cytoskeleton and cell cycle, while the genes expression associated with inflammation and autophagy increased (P < 0.05). Acid exposure altered metabolites related to purine metabolism, and affected proteins associated with complement and coagulation cascades and RNA degradation. Acid also leads to mitochondrial dysfunction, alterations in mitochondrial integrity, and reduced ATP generation. It also causes actin filaments to change from filamentous to punctate, affecting cellular cytoskeletal function, and increases inflammation-related molecules, indicating the promotion of inflammatory responses and cellular damage (P < 0.05). LPS treatment induced differential expression of proteins involved in the TNF signaling pathway and cytokine-cytokine receptor interaction, accompanied by alterations in metabolites associated with arachidonic acid metabolism and MAPK signaling (P < 0.05). The inflammatory response and activation of signaling pathways induced by LPS treatment were also confirmed through protein interaction network analysis. The integrated analysis reveals co-enrichment of proteins and metabolites in cellular signaling and metabolic pathways.

CONCLUSIONS

In summary, this study contributes to a comprehensive understanding of the detrimental effects of SARA-associated factors on YRECs, elucidating their molecular mechanisms and providing potential therapeutic targets for mitigating SARA.

An integrated approach for studying the metabolic profiling of herbal medicine in mice using high-resolution mass spectrometry and metabolomics data processing tools

Analysis of exposure to traditional Chinese medicine (TCM) in vivo based on mass spectrometry is helpful for the screening of effective ingredients of TCM and the development of new drugs. The method of screening biomarkers through metabolomics technology is a nontargeted research method to explore the differential components between two sets of biological samples. By taking this advantage, this study aims to takes Forsythia suspensa, which is a TCM also known as Lian Qiao (LQ), as the research object and to study its in vivo exposure by using metabolomics technology. By comparing the significant differences between biological samples before and after administration, it could be focused on the components that were significantly upregulated, where a complete set of analysis strategies for nontargeted TCM in vivo exposure mass spectrometry was established. Furthermore, the threshold parameters for peak extraction, parameter selection during statistical data analysis, and sample concentration multiples in this method have also been optimized. More interestingly, by using the established analysis strategy, we found 393 LQ-related chemical components in mice after administration, including 102 prototypes and 291 LQ-related metabolites, and plotted their metabolic profiles in vivo. In short, this study has obtained a complete mass spectrum of LQ exposure in mice in vivo for the first time, which provides a reference for research on the active ingredients of LQ in vivo. More importantly, compared with other methods, the analysis strategy of nontargeted exposure of TCM in vivo-based mass spectrometry, constructed by using this research method, has good universality and does not require self-developed postprocessing software. It is worth mentioning that, for the identification and characterization of trace amounts of metabolites in vivo, this analysis strategy has no discrimination and has a detection capability similar to that of highly exposed components.

Metabolite profiling and identification in living cells by coupling stable isotope tracing and induced electrospray mass spectrometry

Direct observation of metabolites in living cells by mass spectrometry offers a bright future for biological studies but also suffers a severe challenge to untargeted peak assignment to tentative metabolite candidates. In this study, we developed a method combining stable isotope tracing and induced electrospray mass spectrometry for living-cells metabolite measurement and identification. By using ¹³C₆-glucose and ammonium chloride-¹⁵N as the sole carbon and nitrogen sources for cell culture, Escherichia coli synthesized metabolites with ¹⁵N and ¹³C elements. Tracing the number of carbon and nitrogen atoms could offer a complementary dimension for candidate peak searching. As a result, the identification confidence of metabolites achieved a universal improvement based on carbon/nitrogen labelling and filtration.

Mass Spectroscopy as an Analytical Tool to Harness the Production of Secondary Plant Metabolites: The Way Forward for Drug Discovery

The molecular map of diverse biological molecules linked with structure, function, signaling, and regulation within a cell can be elucidated using an analytically demanding omic approach. The latest trend of using "metabolomics" technologies has explained the natural phenomenon of opening a new avenue to understand and enhance bioactive compounds' production. Examination of sequenced plant genomes has revealed that a considerable portion of these encodes genes of secondary metabolism. In addition to genetic and molecular tools developed in the current era, the ever-increasing knowledge about plant metabolism's biochemistry has initiated an approach for wisely designed, more productive genetic engineering of plant secondary metabolism for improved defense systems and enhanced biosynthesis of beneficial metabolites. Secondary plant metabolites are natural products synthesized by plants that are not directly involved with their average growth and development but play a vital role in plant defense mechanisms. Plant secondary metabolites are classified into four major classes: terpenoids, phenolic compounds, alkaloids, and sulfur-containing compounds. More than 200,000 secondary metabolites are synthesized by plants having a unique and complex structure. Secondary plant metabolites are well characterized and quantified by omics approaches and therefore used by humans in different sectors such as agriculture, pharmaceuticals, chemical industries, and biofuel. The aim is to establish metabolomics as a comprehensive and dynamic model of diverse biological molecules for biomarkers and drug discovery. In this chapter, we aim to illustrate the role of metabolomic technology, precisely liquid chromatography-mass spectrometry, capillary electrophoresis mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance spectroscopy, specifically as a research tool in the production and identification of novel bioactive compounds for drug discovery and to obtain a unified insight of secondary metabolism in plants.

Innovative in Silico Approaches for Characterization of Genes and Proteins

Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.

Authentication of Herbal Medicines Based on Modern Analytical Technology Combined with Chemometrics Approach: A Review

Since ancient times, herbal medicines (HMs) have been widely popular with consumers as a "natural" drug for health care and disease treatment. With the emergence of problems, such as increasing demand for HMs and shortage of resources, it often occurs the phenomenon of shoddy exceed and mixing the false with the genuine in the market. There is an urgent need to evaluate the quality of HMs to ensure their important role in health care and disease treatment, and to reduce the possibility of threat to human health. Modern analytical technology is can be analyzed for analyzing chemical components of HMs or their preparations. Reflecting complex chemical components' characteristic curves in the analysis sample, and the comprehensive effect of active ingredients of HMs. In this review, modern analytical technology (chromatography, spectroscopy, mass spectrometry), chemometrics methods (unsupervised, supervised) and their advantages, disadvantages, and applicability were introduced and summarized. In addition, the authentication application of modern analytical technology combined with chemometrics methods in four aspects, including origin, processing methods, cultivation methods, and adulteration of HMs have also been discussed and illustrated by a few typical studies. This article offers a general workflow of analytical methods that have been applied for HMs authentication and explains that the accuracy of authentication in favor of the quality assurance of HMs. It was provided reference value for the development and application of modern HMs.

OUP accepted manuscript

Currently, the only definitive method for diagnosing ovarian cancer involves histological examination of tissue obtained at time of surgery or by invasive biopsy. Blood has traditionally been the biofluid of choice in ovarian cancer biomarker discovery; however, there has been a growing interest in exploring urinary biomarkers, particularly as it is non-invasive. In this systematic review, we present the diagnostic accuracy of urinary biomarker candidates for the detection of ovarian cancer. A comprehensive literature search was performed using the MEDLINE/PubMed and EMBASE, up to 1 April 2021. All included studies reported the diagnostic accuracy using sensitivity and/or specificity and/or receiver operating characteristics (ROC) curve. Risk of bias and applicability of included studies were assessed using the QUADAS-2 tool. Twenty-seven studies were included in the narrative synthesis. Protein/peptide biomarkers were most commonly described (n = 18), with seven studies reporting composite scores of multiple protein-based targets. The most frequently described urinary protein biomarker was HE4 (n = 5), with three studies reporting a sensitivity and specificity > 80%. Epigenetic (n = 1) and metabolomic/organic compound biomarkers (n = 8) were less commonly described. Overall, six studies achieved a sensitivity and specificity of >90% and/or an AUC > 0.9. Evaluation of urinary biomarkers for the detection of ovarian cancer is a dynamic and growing field. Currently, the most promising biomarkers are those that interrogate metabolomic pathways and organic compounds, or quantify multiple proteins. Such biomarkers require external validation in large, prospective observational studies before they can be implemented into clinical practice.

Improving data quality in liquid chromatography-mass spectrometry metabolomics of human urine

Signal variation is a common drawback in untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS), mainly due to the complexity of biological matrices and reduced sample preparation, which results in the accumulation of sample components in the column and the ion source. Here we propose a simple, easy to implement approach to improve data quality in untargeted metabolomics by LC-MS. This approach involves the use of a divert valve to direct the column effluent to waste at the beginning of the chromatographic run and during column cleanup and equilibration, in combination with longer column cleanups in between injections. Our approach was tested using urine samples collected from patients after renal transplantation. Analytical responses were contrasted before and after introducing these modifications by analyzing a batch of untargeted metabolomics data. A significant improvement in peak area repeatability was observed for the quality controls, with relative standard deviations (RSDs) for several metabolites decreasing from ∼60% to ∼10% when our approach was introduced. Similarly, RSDs of peak areas for internal standards improved from ∼40% to ∼10%. Furthermore, calibrant solutions were more consistent after introducing these modifications when comparing peak areas of solutions injected at the beginning and the end of each analytical sequence. Therefore, we recommend the use of a divert valve and extended column cleanup as a powerful strategy to improve data quality in untargeted metabolomics, especially for very complex types of samples where minimum sample preparation is required, such as in this untargeted metabolomics study with urine from renal transplanted patients.

Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases

Metabolomics is a branch of ‘omics’ sciences that utilises a couple of analytical tools for the identification of small molecules (metabolites) in a given sample. The overarching goal of metabolomics is to assess these metabolites quantitatively and qualitatively for their diagnostic, therapeutic, and prognostic potentials. Its use in various aspects of life has been documented. We have also published, howbeit in animal models, a few papers where metabolomic approaches were used in the study of metabolic disorders, such as metabolic syndrome, diabetes, and obesity. As the goal of every research is to benefit humankind, the purpose of this review is to provide insights into the applicability of metabolomics in medicine vis-à-vis its role in biomarker discovery for disease diagnosis and management. Here, important biomarkers with proven diagnostic and therapeutic relevance in the management of disease conditions, such as Alzheimer’s disease, dementia, Parkinson’s disease, inborn errors of metabolism (IEM), diabetic retinopathy, and cardiovascular disease, are noted. The paper also discusses a few reasons why most metabolomics-based laboratory discoveries are not readily translated to the clinic and how these could be addressed going forward.

Functional metabolomics using UPLC-Q/TOF-MS combined with ingenuity pathway analysis as a promising strategy for evaluating the efficacy and discovering amino acid metabolism as a potential therapeutic mechanism-related target for geniposide against alcoholic liver disease

Metabolomics has been used as a strategy to evaluate the efficacy of and potential targets for natural products.

Important Metabolites in Maintaining Folate Cycle, Homocysteine, and Polyamine Metabolism Associated with Ranibizumab Treatment in Cultured Human Tenon's Fibroblasts

The anti-fibrotic properties of ranibizumab have been well documented. As an antagonist to vascular endothelial growth factor (VEGF), ranibizumab works by binding and neutralizing all active VEGF-A, thus limiting progressive cell growth and proliferation. Ranibizumab application in ocular diseases has shown remarkable desired effects; however, to date, its antifibrotic mechanism is not well understood. In this study, we identified metabolic changes in ranibizumab-treated human Tenon's fibroblasts (HTFs). Cultured HTFs were treated for 48 h with 0.5 mg/mL of ranibizumab and 0.5 mg/mL control IgG antibody which serves as a negative control. Samples from each group were injected into Agilent 6520 Q-TOF liquid chromatography/mass spectrometer (LC/MS) system to establish the metabolite expression in both ranibizumab treated cells and control group. Data obtained was analyzed using Agilent Mass Hunter Qualitative Analysis software to identify the most regulated metabolite following ranibizumab treatment. At p-value < 0.01 with the cut off value of two-fold change, 31 identified metabolites were found to be significantly upregulated in ranibizumab-treated group, with six of the mostly upregulated having insignificant role in fibroblast cell cycle and wound healing regulations. Meanwhile, 121 identified metabolites that were downregulated, and seven of the mostly downregulated are significantly involved in cell cycle and proliferation. Our findings suggest that ranibizumab abrogates the tissue scarring and wound healing process by regulating the expression of metabolites associated with fibrotic activity. In particular, we found that vitamin Bs are important in maintaining normal folate cycle, nucleotide synthesis, and homocysteine and spermidine metabolism. This study provides an insight into ranibizumab's mechanism of action in HTFs from the perspective of metabolomics.

Network Pharmacology in Research of Chinese Medicine Formula: Methodology, Application and Prospective

Chinese medicine (CM) is usually prescribed as CM formula to treat disease. The lack of effective research approach makes it difficult to elucidate the molecular mechanisms of CM formula owing to its complicated chemical compounds. Network pharmacology is increasingly applied in CM formula research in recent years, which is identified suitable for the study of CM formula. In this review, we summarized the methodology of network pharmacology, including network construction, network analysis and network verification. The aim of constructing a network is to achieve the interaction between the bioactive compounds and targets and the interaction between various targets, and then find out and validate the key nodes via network analysis and network verification. Besides, we reviewed the application in CM formula research, mainly including targets discovery, bioactive compounds screening, toxicity evaluation, mechanism research and quality control research. Finally, we proposed prospective in the future and limitations of network pharmacology, expecting to provide new strategy and thinking on study for CM formula.

Applications and potential mechanisms of herbal medicines for rheumatoid arthritis treatment: a systematic review

In this review, we systematically discuss the role of traditional Chinese medicine (TCM) in rheumatoid arthritis (RA) disease treatment. TCM classifies the subtypes of RA through its own theoretical method, which is beneficial for more accurate diagnosis and treatment with Chinese herbal medicines (CHMs) that are more suitable for different syndromes. TCM mainly uses a flexible combination of CHMs to play an important role in RA treatment. The main components of these extracts can be subdivided into alkaloids, flavonoids, triterpenes, saponins and other compounds. Using a platform of transgenic and induced arthritis models, we explore the potential mechanisms of TCM against RA with the help of omics analysis techniques and methods. These mechanisms are mainly CHM and its extracts can inhibit RA patients and experimental animal models, including synovitis, vascular proliferation and bone injury; this involves many biological signal exchange targets and pathways. In conclusion, the role of TCM in RA treatment mainly involves reducing the expression and secretion of pro-inflammatory factors, thus decreasing the degree of abnormal immune response.

In this review, we systematically discuss the role of traditional Chinese medicine (TCM) in rheumatoid arthritis (RA) disease treatment.

Metabolomics-based parallel discovery of xenobiotics and induced endogenous metabolic dysregulation in clinical toxicology

Intoxication by xenobiotics triggers the perturbation of metabolic fingerprints in biofluids, including the accumulation of xenobiotic compounds and the dysregulation of endogenous metabolites. In this work, an untargeted metabolomics workflow was developed to simultaneously profile both xenobiotic and endogenous metabolites for the identification of the xenobiotic origin and an in-depth understanding of the intoxication mechanism. This workflow was demonstrated in a real-world clinical case. Plasma samples were collected from four intoxicated children and another three healthy children. Untargeted metabolomics analysis was performed using ultraperformance liquid chromatography (UPLC) coupled to a high-resolution mass spectrometer (HRMS) with data-independent MS^E acquisition. LC-MS^E data was processed using an untargeted metabolomics data interpretation workflow, in which the identities of xenobiotics and altered endogenous metabolic features were determined via database searching. Five xenobiotic chemicals and 19 endogenous metabolites were found to be dysregulated. Combined with the clinical evidence, penfluridol was confirmed as the xenobiotic toxin. Furthermore, a mechanistic hypothesis was developed to explain the dysregulation of the four endogenous acyl-carnitines. This workflow can be readily applied to a wide range of clinical toxicology cases, offering a powerful and convenient means of simultaneous discovery of intoxication source and the understanding of intoxication mechanisms.

Study on Cardiotoxicity and Mechanism of "Fuzi" Extracts Based on Metabonomics

To investigate the toxicity of water and ethanol "Fuzi" (FZ) extracts and to explore the toxicity mechanism in rats. Water and ethanol extracts were prepared. Three groups of rats received the water extract, ethanol extract, or water by oral gavage for seven days. Pathological section staining of heart tissue. Colorimetric analysis was used to determine serum lactate dehydrogenase. The metabolic expression of small molecules in rats was measured by a metabolomics method. Western blotting was used to detect the expression of phosphoinositide 3-kinase (PI3K), protein kinase B (Akt), mammalian target of rapamycin (mTOR), transforming growth factor-β1 (TGF-β1), and caspase-3. Immunohistochemistry was used to detect the expression of CTnI, mTOR, and TGF-β1. The water and ethanol FZ extracts exert cardiotoxic effects via activating the PI3K/Akt/mTOR signaling pathway to induce cardiomyocyte apoptosis.

High-throughput metabolomics for discovering metabolic biomarkers from intestinal tumorigenesis in APC min/+ mice based on liquid chromatography/mass spectrometry

As a major public health concern, colon cancer is one of the most common cancer types, which is also the second cause of cancer death in developed countries and the third most common cancer in other parts of the world. It was reported that patients diagnosed at early stage have a chance to obtain 5-year survival rates at least compared to patients with late stage. Facing the multistep process in intestinal tumorigenesis, there is an urgent need to develop more effective early detection strategies for ameliorating the patient clinical outcome. Metabolomics open up a novel avenue of seeking valuable potential biomarkers for assessing disease severity and prognosticating course by dynamic snapshot of small molecule metabolites. The study aims to provide deeper insights into the discovery, identification and functional pathways analysis of differentially expressed metabolites in intestinal tumorigenesis in APC ^min/+ mice used by the serum metabolomics, and bring about useful information for further effective prevention and treatment of the disease. 17 marker metabolites and related metabolism pathway were identified using non-targeted metabolomics based on liquid chromatography/mass spectrometry (LC/MS) associated with multivariate statistical analysis. The ingenuity pathway analysis platform involved multiple-pathways was applied to metabolic network analysis for further understanding the relationship between functional metabolic pathways and disease.

Side effects of methotrexate therapy for rheumatoid arthritis: A systematic review

Methotrexate (MTX) is used as an anchor disease-modifying anti-rheumatic drugs (DMARDs) in treating rheumatoid arthritis (RA) because of its potent efficacy and tolerability. MTX benefits a large number of RA patients but partially suffered from side effects. A variety of side effects can be associated with MTX when treating RA patients, from mild to severe or discontinuation of the treatment. In this report, we reviewed the possible side effects that MTX might cause from the most common gastrointestinal toxicity effects to less frequent malignant diseases. In order to achieve regimen with less side effects, the administration of MTX with appropriate dose and a careful pretreatment inspection is necessary. Further investigations are required when combining MTX with other drugs so as to enhance the efficacy and reduce side effects at the same time. The management of MTX treatment is also discussed to provide strategies for occurred side effects. Thus, this review will provide scholars with a comprehensive understanding the side effects of MTX administration by RA patients.

Metabolomic profiling of rat urine after oral administration of the prescription antipyretic Hao Jia Xu Re Qing Granules by UPLC/Q-TOF-MS

Hao Jia Xu Re Qing Granules (HJ), is an effective clinically used antipyretic based on traditional Chinese medicine. Although its antipyretic therapeutic effectiveness is obvious, its therapeutic mechanism has not been comprehensively explored yet. In this research, we first identified potential biomarkers which may be relevant for the antipyretic effect of HJ based on urine metabolomics using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). A rat model of fever was established using the yeast-induced febrile response. Total-ion-current metabolic profiles of different groups were acquired and the data were processed by multivariate statistical analysis-partial least-squares discriminant analysis. As envisioned, the results revealed changes of urine metabolites related to the antipyretic effect. Fourteen potential biomarkers were selected from the urine samples based on the results of Student's t-test, "shrinkage t", variable importance in projection and partial least-squares discriminant analysis. N-Acetylleucine, kynurenic acid, indole-3-ethanol, nicotinuric acid, pantothenic acid and tryptophan were the most significant biomarkers found in the urine samples, and may be crucially related to the antipyretic effect of HJ. Consequently, we propose the hypothesis that the significant antipyretic effect the HJ may be related to the inhibition of tryptophan metabolism. This research thus provides strong theoretical support and further direction to explain the antipyretic mechanism of HJ, laying the foundation for future studies.

Metabolomic and lipidomic profile in men with obstructive sleep apnoea: implications for diagnosis and biomarkers of cardiovascular risk

The use of metabolomic and lipidomic strategies for selecting potential biomarkers for obstructive sleep apnoea (OSA) has been little explored. We examined adult male patients with OSA (defined by an apnoea-hypopnoea index ≥15 events/hour), as well as age-, gender-, and fat-composition-matched volunteers without OSA. All subjects were subjected to clinical evaluation, sleep questionnaires for detecting the risk of OSA (Berlin and NoSAS score), metabolomic analysis by gas chromatography coupled to mass spectrometry and lipidomic analysis with liquid chromatography followed by detection by MALDI-MS. This study included 37 patients with OSA and 16 controls. From the 6 metabolites and 22 lipids initially selected, those with the best association with OSA were glutamic acid, deoxy sugar and arachidonic acid (metabolites), and glycerophosphoethanolamines, sphingomyelin and lyso-phosphocholines (lipids). For the questionnaires, the NoSAS score performed best with screening for OSA (area under the curve [AUC] = 0.724, p = 0.003). The combination of the NoSAS score with metabolites or lipids resulted in an AUC for detecting OSA of 0.911 and 0.951, respectively. In conclusion, metabolomic and lipidomic strategies suggested potential early biomarkers in OSA that could also be helpful in screening for this sleep disorder beyond traditional questionnaires.

A high throughput metabolomics method and its application in female serum samples in a normal menstrual cycle based on liquid chromatography-mass spectrometry

Periodical changes of steroid hormones have a great impact on the homeostasis of the female. However, there are few studies concerning the metabolome changes during the cycle. To study the periodic metabolic changes, a female cohort was enrolled with time-series serum samples collected during a menstrual cycle. To meet the requirement of the large-scale sample analysis, a high throughput metabolomics method was established by using an efficient sample preparation on a 96 well filter plate and a rapid LC condition in 12 min, which reduces about 70% of the samples preprocessing time and 60% analysis time. Evaluation of metabolite coverage and separation performances reflected that the method was robust for the large-scale metabolomics study. Using this method, we found that 12.6% of total detected ions including lipids, amino acids, citric acid, and so on were significantly changed during a menstrual cycle. Some metabolites were found periodically changed, which is similar to hormones (estrone and progesterone) during the cycle. These results show the novel high throughput method can be applied in large-scale metabolomics studies.

Distinguishing Smilax glabra and Smilax china rhizomes by flow-injection mass spectrometry combined with principal component analysis

Flow-injection mass spectrometry (FIMS) coupled with a chemometric method is proposed in this study to profile and distinguish between rhizomes of Smilax glabra (S. glabra) and Smilax china (S. china). The proposed method employed an electrospray-time-of-flight MS. The MS fingerprints were analyzed using principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) with the aid of SIMCA software. Findings showed that the two kinds of samples perfectly fell into their own classes. Further predictive study showed desirable predictability and the tested samples were successfully and reliably identified. The study demonstrated that the proposed method could serve as a powerful tool for distinguishing between S. glabra and S. china.

Advances in mass spectrometry-based metabolomics for investigation of metabolites

Metabolomics is the systematic study of all the metabolites present within a biological system, which consists of a mass of molecules, having a variety of physical and chemical properties and existing over an extensive dynamic range in biological samples. Diverse analytical techniques are needed to achieve higher coverage of metabolites. The application of mass spectrometry (MS) in metabolomics has increased exponentially since the discovery and development of electrospray ionization and matrix-assisted laser desorption ionization techniques. Significant advances have also occurred in separation-based MS techniques (gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry, and ion mobility-mass spectrometry), as well as separation-free MS techniques (direct infusion-mass spectrometry, matrix-assisted laser desorption ionization-mass spectrometry, mass spectrometry imaging, and direct analysis in real time mass spectrometry) in the past decades. This review presents a brief overview of the recent advanced MS techniques and their latest applications in metabolomics. The software/websites for MS result analyses are also reviewed.

Metabolomics is the systematic study of all the metabolites present within a biological system, supply functional information and has received extensive attention in the field of life sciences.

Computational Systems Biology of Metabolism in Infection

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.

Robust Regression Analysis of GCMS Data Reveals Differential Rewiring of Metabolic Networks in Hepatitis B and C Patients

About one in 15 of the world’s population is chronically infected with either hepatitis virus B (HBV) or C (HCV), with enormous public health consequences. The metabolic alterations caused by these infections have never been directly compared and contrasted. We investigated groups of HBV-positive, HCV-positive, and uninfected healthy controls using gas chromatography-mass spectrometry analyses of their plasma and urine. A robust regression analysis of the metabolite data was conducted to reveal correlations between metabolite pairs. Ten metabolite correlations appeared for HBV plasma and urine, with 18 for HCV plasma and urine, none of which were present in the controls. Metabolic perturbation networks were constructed, which permitted a differential view of the HBV- and HCV-infected liver. HBV hepatitis was consistent with enhanced glucose uptake, glycolysis, and pentose phosphate pathway metabolism, the latter using xylitol and producing threonic acid, which may also be imported by glucose transporters. HCV hepatitis was consistent with impaired glucose uptake, glycolysis, and pentose phosphate pathway metabolism, with the tricarboxylic acid pathway fueled by branched-chain amino acids feeding gluconeogenesis and the hepatocellular loss of glucose, which most probably contributed to hyperglycemia. It is concluded that robust regression analyses can uncover metabolic rewiring in disease states.

Characterization of rapid extraction protocols for high-throughput metabolomics

RATIONALE

In the last five years, high-throughput metabolomics has significantly advanced scientific research and holds the potential to promote strides in the fields of clinical metabolomics and personalized medicine. While innovations in the field of flow-injection mass spectrometry and three-minute metabolomics methods now allow investigators to process hundreds to thousands of samples per day, time-sensitive clinical applications, particularly in the emergency department, are limited by a lack of rapid extraction methods.

METHODS

Here we characterized the efficacy of fast liquid-liquid extractions for characterization of hydrophilic compounds through ultra-high-pressure liquid chromatography/mass spectrometry. Internal stable-isotope-labeled standards were used to quantitatively characterize markers of energy and oxidative metabolism in human whole blood, plasma and red blood cells - three common matrices of clinical relevance.

RESULTS

For all the tested matrices, vortexing time (4-60 min) did not significantly affect extraction yields for the tested hydrophilic metabolites. Coefficients of variations <<20% for all tested compounds, except for the redox-sensitive metabolite cystine (accumulating over time). Internal standards and second extractions confirmed recoveries >80% for all tested metabolites, except for basic amino acids and polyamines, which showed reproducible yields ranging from 50 to 75%. Global profiling and absolute quantitation of 24 metabolites revealed similarities between the plasma and red blood cell metabolomes.

CONCLUSIONS

Rapid extraction (~4 min) of hydrophilic compounds is a viable and potentially automatable strategy to perform quantitative analysis of whole blood, plasma and red blood cells for research or clinical applications.

Metabolomics and proteomics technologies to explore the herbal preparation affecting metabolic disorders using high resolution mass spectrometry

An integrative metabolomics and proteomics approach can provide novel insights in the understanding of biological systems. We have integrated proteome and metabolome data sets for a holistic view of the molecular mechanisms in disease. Using quantitative iTRAQ-LC-MS/MS proteomics coupled with UPLC-Q-TOF-HDMS based metabolomics, we determined the protein and metabolite expression changes in the kidney-yang deficiency syndrome (KYDS) rat model and further investigated the intervention effects of the Jinkui Shenqi Pill (JSP). The VIP-plot of the orthogonal PLS-DA (OPLS-DA) was used for discovering the potential biomarkers to clarify the therapeutic mechanisms of JSP in treating KYDS. The results showed that JSP can alleviate the kidney impairment induced by KYDS. Sixty potential biomarkers, including 5-l-glutamyl-taurine, phenylacetaldehyde, 4,6-dihydroxyquinoline, and xanthurenic acid etc., were definitely up- or down-regulated. The regulatory effect of JSP on the disturbed metabolic pathways was proved by the established metabonomic method. Using pathway analyses, we identified the disturbed metabolic pathways such as taurine and hypotaurine metabolism, pyrimidine metabolism, tyrosine metabolism, tryptophan metabolism, histidine metabolism, steroid hormone biosynthesis, etc. Furthermore, using iTRAQ-based quantitative proteomics analysis, seventeen differential proteins were identified and significantly altered by the JSP treatment. These proteins appear to be involved in Wnt, chemokine, PPAR, and MAPK signaling pathways, etc. Functional pathway analysis revealed that most of the proteins were found to play a key role in the regulation of metabolism pathways. Bioinformatics analysis with the IPA software found that these differentially-expressed moleculars had a strong correlation with the α-adrenergic signaling, FGF signaling, etc. Our data indicate that high-throughput metabolomics and proteomics can provide an insight on the herbal preparations affecting the metabolic disorders using high resolution mass spectrometry.

A three-minute method for high-throughput quantitative metabolomics and quantitative tracing experiments of central carbon and nitrogen pathways

RATIONALE

The implementation of mass spectrometry (MS)-based metabolomics is advancing many areas of biomedical research. The time associated with traditional chromatographic methods for resolving metabolites prior to mass analysis has limited the potential to perform large-scale, highly powered metabolomics studies and clinical applications.

METHODS

Here we describe a three-minute method for the rapid profiling of central metabolic pathways through UHPLC/MS, tracing experiments in vitro and in vivo, and targeted quantification of compounds of interest using spiked in heavy labeled standards.

RESULTS

This method has shown to be linear, reproducible, selective, sensitive, and robust for the semi-targeted analysis of central carbon and nitrogen metabolism. Isotopically labeled internal standards are used for absolute quantitation of steady-state metabolite levels and de novo synthesized metabolites in tracing studies. We further propose exploratory applications to biofluids, cell and tissue extracts derived from relevant biomedical/clinical samples.

CONCLUSIONS

While limited to the analysis of central carbon and nitrogen metabolism, this method enables the analysis of hundreds of samples per day derived from diverse biological matrices. This approach makes it possible to analyze samples from large patient populations for translational research, personalized medicine, and clinical metabolomics applications. Copyright © 2017 John Wiley & Sons, Ltd.

Metabolomics combined with pattern recognition and bioinformatics analysis methods for the development of pharmacodynamic biomarkers on liver fibrosis

Metabolomics combined with pattern recognition and network analysis maybe an attractive strategy for the pharmacodynamics biomarkers development on liver fibrosis.

Plasma metabolic profiling and novel metabolite biomarkers for diagnosing prostate cancer

Prostate cancer (PCa) is the second leading cause of cancer death among men and associated with profound metabolic changes.

High-throughput metabolomics enables biomarker discovery in prostate cancer

Prostate cancer (PCa) is the most frequently diagnosed cancer and the second leading cause of cancer death among men in the world.

Recent advances in pharmacokinetics approach for herbal medicine

Traditional Chinese Medicine (TCM), an indispensable part of herbal medicine, has been used for treating many diseases and/or symptoms for thousands of years.

Technological advances in current metabolomics and its application in tradition Chinese medicine

During the last few years, many metabolomics technologies have been established in biomedical research for analyzing the changes of metabolite levels.

High-throughput chinmedomics-based prediction of effective components and targets from herbal medicine AS1350

This work was designed to explore the effective components and targets of herbal medicine AS1350 and its effect on “Kidney-Yang Deficiency Syndrome” (KYDS) based on a chinmedomics strategy which is capable of directly discovering and predicting the effective components, and potential targets, of herbal medicine. Serum samples were analysed by UPLC-MS combined with pattern recognition analysis to identify the biomarkers related to the therapeutic effects. Interestingly, the effectiveness of AS1350 against KYDS was proved by the chinmedomics method and regulated the biomarkers and targeting of metabolic disorders. Some 48 marker metabolites associated with alpha-linolenic acid metabolism, fatty acid metabolism, sphingolipids metabolism, phospholipid metabolism, steroid hormone biosynthesis, and amino acid metabolism were identified. The correlation coefficient between the constituents in vivo and the changes of marker metabolites were calculated by PCMS software and the potential effective constituents of AS1350 were also confirmed. By using chinmedomics technology, the components in AS1350 protecting against KYDS by re-balancing metabolic disorders of fatty acid metabolism, lipid metabolism, steroid hormone biosynthesis, etc. were deduced. These data indicated that the phenotypic characterisations of AS1350 altering the metabolic signatures of KYDS were multi-component, multi-pathway, multi-target, and overall regulation in nature.

Recovery of pan-genotypic and genotype-specific amino acid alterations in chronic hepatitis C after viral clearance: transition at the crossroad of metabolism and immunity

Recovery of amino acid (AA) metabolism and the associated clinical implications in chronic hepatitis C (CHC) patients with sustained virological response (SVR) following anti-hepatitis C virus (HCV) therapy remains elusive. A prospective cohort study was conducted on 222 CHC patients with SVR. Eighty-two age-matched male genotype 1 (G1) and G2 patients underwent paired serum metabolomics analyses with liquid chromatography-tandem mass spectrometry to examine AAs before and 24 weeks after anti-HCV therapy. Before anti-HCV therapy, G1 patients had a higher HCV RNA level than G2 patients. Twenty-four weeks post-therapy versus pre-therapy, repeated-measures ANOVA showed that the levels of alanine aminotransferase and most AAs decreased while those of lipids, glutamine and putrescine increased in CHC patients. The methionine sulfoxide/methionine ratio decreased, while the asymmetric dimethylarginine/arginine, glutamine/glutamate, citrulline/arginine, ornithine/arginine, kynurenine/tryptophan, tyrosine/phenylalanine and Fisher's ratios increased. Genotype-specific subgroup analyses showed that valine and serotonin/tyrosine increased in G1 and that kynurenine and tyrosine/phenylalanine increased and sarcosine decreased in G2 patients. Viral clearance in CHC patients pan-genotypically restored fuel utilization by decelerating the tricarboxylic acid cycle. Following improvement in liver function, the urea, nitric oxide, methionine, and polyamine cycles were accelerated. The cardiometabolic risk attenuated, but the augmented kynurenine pathway activity could increase the oncogenesis risk. The trends in neurotransmitter formation differed between G1 and G2 patients after SVR. Moreover, the HCV-suppressing effect of valine was evident in G1 patients; with the exception of prostate cancer, the oncogenesis risk increased, particularly in G2 patients, at least within 24 weeks post-anti-HCV therapy.

A Novel Method for Evaluating the Cardiotoxicity of Traditional Chinese Medicine Compatibility by Using Support Vector Machine Model Combined with Metabonomics

Traditional biochemical and histopathological tests have been used to evaluate the safety of traditional Chinese medicine (TCM) compatibility for a long time. But these methods lack high sensitivity and specificity. In the previous study, we have found ten biomarkers related to cardiotoxicity and established a support vector machine (SVM) prediction model. Results showed a good sensitivity and specificity. Therefore, in this study, we used SVM model combined with metabonomics UPLC/Q-TOF-MS technology to build a rapid and sensitivity and specificity method to predict the cardiotoxicity of TCM compatibility. This study firstly applied SVM model to the prediction of cardiotoxicity in TCM compatibility containing Aconiti Lateralis Radix Praeparata and further identified whether the cardiotoxicity increased after Aconiti Lateralis Radix Praeparata combined with other TCM. This study provides a new idea for studying the evaluation of the cardiotoxicity caused by compatibility of TCM.

A quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis C

Background

High-throughput technologies have the potential to identify non-invasive biomarkers of liver pathology and improve our understanding of basic mechanisms of liver injury and repair. A metabolite profiling approach was employed to determine associations between alterations in serum metabolites and liver histology in patients with chronic hepatitis C virus (HCV) infection.

Methods

Sera from 45 non-diabetic patients with chronic HCV were quantitatively analyzed using ¹H-NMR spectroscopy. A metabolite profile of advanced fibrosis (METAVIR F3-4) was established using orthogonal partial least squares discriminant analysis modeling and validated using seven-fold cross-validation and permutation testing. Bioprofiles of moderate to severe steatosis (≥33 %) and necroinflammation (METAVIR A2-3) were also derived. The classification accuracy of these profiles was determined using areas under the receiver operator curves (AUROCSs) measuring against liver biopsy as the gold standard.

Results

In total 63 spectral features were profiled, of which a highly significant subset of 21 metabolites were associated with advanced fibrosis (variable importance score >1 in multivariate modeling; R² = 0.673 and Q² = 0.285). For the identification of F3–4 fibrosis, the metabolite bioprofile had an AUROC of 0.86 (95 % CI 0.74–0.97). The AUROCs for the bioprofiles for moderate to severe steatosis were 0.87 (95 % CI 0.76–0.97) and for grade A2–3 inflammation were 0.73 (0.57–0.89).

Conclusion

This proof-of-principle study demonstrates the utility of a metabolomics profiling approach to non-invasively identify biomarkers of liver fibrosis, steatosis and inflammation in patients with chronic HCV. Future cohorts are necessary to validate these findings.

Electronic supplementary material

The online version of this article (doi:10.1186/s40169-016-0109-2) contains supplementary material, which is available to authorized users.

Metabolomics-proteomics profiles delineate metabolic changes in kidney fibrosis disease

Kidney fibrosis (KF) is a common process that leads to the progression of various types of kidney disease including kidney-yang deficiency syndrome, however, little is known regarding the underlying biology of this disorder. Fortunately, integrated omics approaches provide the molecule fingerprints related to the disease. In an attempt to address this issue, we integrated metabolomics-proteomics profiles analyzed pathogenic mechanisms of KF based on rat model. A total 37 serum differential metabolites were contributed to KF progress, involved several important metabolic pathways. Using iTRAQ-based quantitative proteomics analysis, 126 differential serum proteins were identified and provide valuable insight into the underlying mechanisms of KF. These proteins appear to be involved in complement and coagulation cascades, regulation of actin cytoskeleton, MAPK signaling pathway, RNA transport, etc. Interestingly, pathway/network analysis of integrated proteomics and metabolomics data firstly reveals that these signaling pathways were closely related with KF. It further indicated that most of these proteins play a pivotal role in the regulation of metabolism pathways.

Combining biochemical with (1)H NMR-based metabolomics approach unravels the antidiabetic activity of genipin and its possible mechanism

Diabetes mellitus is a typical heterogeneous metabolic disorder characterized by abnormal metabolism of carbohydrates, lipids and proteins. Genipin possesses a wide spectrum of biological activities including ameliorating effects on diabetes, but the definite mechanism of this effect remains unknown. To investigate the antidiabetic activities of genipin and explore the biochemical changes of serum endogenous metabolites on diabetic rats induced by alloxan, (1)H NMR spectroscopy coupled with multivariate data analysis was used to. All rats were randomly divided into six groups including negative control (NC) group, diabetic mellitus (DM) group, metformin hydrochloride group, high dose group of genipin, middle dose group of genipin and low dose group of genipin. Diabetes was induced by a single intraperitoneal injection of 120mg/kg body weight of alloxan. Serum samples were collected for the (1)H NMR-based metabolomics and clinical biochemical analysis. Daily oral administration of genipin (25, 50 and 100mg/kg body weight) and metformin hydrochloride (125mg/kg) for two weeks showed beneficial effects on blood glucose level (P<0.01). Significant differences in the metabolic profile as well as the result of biochemical parameters between the diabetic group and the control group were observed. The PLS-DA scores and corresponding loading plots demonstrated that genipin significantly restored the abnormal metabolic state. Detailed analysis of the altered metabolite levels indicated that genipin significantly ameliorated the disturbance in glucose metabolism, tricarboxylic acid cycle, lipid metabolism and amino acid metabolism. Genipin showed the best anti-diabetic effects at a dose of 100mg/kg in rats. This finding indicates that chemical and metabolomic approaches could be powerful tools for the investigation of the biochemical changes in pathological conditions or drug treatment.

Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.

Innovations in Canine and Feline Nutrition: Technologies for Food and Nutrition Assessment

Pet owners have increasing concerns about the nutrition of their pets, and they desire foods and treats that are safe, traceable, and of high nutritive value. To meet these high expectations, detailed chemical composition characterization of ingredients well beyond that provided by proximate analysis will be required, as will information about host physiology and metabolism. Use of faster and more precise analytical methodology and novel technologies that have the potential to improve pet food safety and quality will be implemented. In vitro and in vivo assays will continue to be used as screening tools to evaluate nutrient quality and adequacy in novel ingredients prior to their use in animal diets. The use of molecular and high-throughput technologies allows implementation of noninvasive studies in dogs and cats to investigate the impact of dietary interventions by using systems biology approaches. These approaches may further improve the health and longevity of pets.

Phenotypic Characterization Analysis of Human Hepatocarcinoma by Urine Metabolomics Approach

Hepatocarcinoma (HCC) is one of the deadliest cancers in the world and represents a significant disease burden. Better biomarkers are needed for early detection of HCC. Metabolomics was applied to urine samples obtained from HCC patients to discover noninvasive and reliable biomarkers for rapid diagnosis of HCC. Metabolic profiling was performed by LC-Q-TOF-MS in conjunction with multivariate data analysis, machine learning approaches, ingenuity pathway analysis and receiver-operating characteristic curves were used to select the metabolites which were used for the noninvasive diagnosis of HCC. Fifteen differential metabolites contributing to the complete separation of HCC patients from matched healthy controls were identified involving several key metabolic pathways. More importantly, five marker metabolites were effective for the diagnosis of human HCC, achieved a sensitivity of 96.5% and specificity of 83% respectively, could significantly increase the diagnostic performance of the metabolic biomarkers. Overall, these results illustrate the power of the metabolomics technology which has the potential as a non-invasive strategies and promising screening tool to evaluate the potential of the metabolites in the early diagnosis of HCC patients at high risk and provides new insight into pathophysiologic mechanisms.

An integrated chinmedomics strategy for discovery of effective constituents from traditional herbal medicine

Traditional natural product discovery affords no information about compound structure or pharmacological activities until late in the discovery process, and leads to low probabilities of finding compounds with unique biological properties. By integrating serum pharmacochemistry-based screening with high-resolution metabolomics analysis, we have developed a new platform, termed chinmedomics which is capable of directly discovering the bioactive constituents. In this work, the focus is on ShenQiWan (SQW) treatment of ShenYangXu (SYX, kidney-yang deficiency syndrome) as a case study, as determined by chinmedomics. With serum pharmacochemistry, a total of 34 peaks were tentatively characterised in vivo, 24 of which were parent components and 10 metabolites were detected. The metabolic profiling and potential biomarkers of SYX were also investigated and 23 differential metabolites were found. 20 highly correlated components were screened by the plotting of correlation between marker metabolites and serum constituents and considered as the main active components of SQW. These compounds are imported into a database to predict the action targets: 14 importantly potential targets were found and related to aldosterone-regulated sodium reabsorption and adrenergic signaling pathways. Our study showed that integrated chinmedomics is a powerful strategy for discovery and screening of effective constituents from herbal medicines.

Discovery of serum metabolites for diagnosis of progression of mild cognitive impairment to Alzheimer's disease using an optimized metabolomics method

A nontargeted metabolomics approach was developed to examine metabolic differences in serum samples from the mild cognitive impairment and Alzheimer's disease subjects.

Urinary UPLC-MS metabolomics dissecting the underlying mechanisms of Huaxian capsule protects against sepsis

Urinary UPLC-MS metabolomics could dissect the underlying mechanisms of HXC protects against SS.