Qingyi granules ameliorate severe acute pancreatitis in rats by modulating the gut microbiota and serum metabolic aberrations.

CONTEXT: Qingyi granules can be used to effectively treat patients with severe acute pancreatitis (SAP). OBJECTIVE: To elucidate the role of gut microbiota-mediated metabolism in the therapeutic effects of Qingyi granules. MATERIALS AND METHODS: Sprague-Dawley rats were grouped into the sham operation, SAP model, Qingyi granule intervention (Q, 1.8 g/kg) and emodin intervention (E, 50 mg/kg) groups and observed for 24 h. H&E staining and ELISA were used for histopathological analysis and serum enzyme and cytokine assays. 16S rDNA sequencing and UHPLC-HRMS were used for gut microbiota analysis and untargeted metabolomics. RESULTS: In SAP rats, Qingyi granules decreased the pancreatic pathological score (Q, 7.4 ± 1.14; SAP, 11.6 ± 1.14, p < 0.01); serum amylase (Q, 121.2 ± 6.7; SAP, 144.3 ± 8.86, p < 0.05), lipase (Q, 566 ± 20.34; SAP, 656.7 ± 29.32, p < 0.01), and diamineoxidase (Q, 492.8 ± 26.08; SAP, 566.1 ± 26.83, p < 0.05) activities; and IL-1ß (Q, 29.48 ± 0.88; SAP, 36.17 ± 1.88, p < 0.01), IL-6 (Q, 112.2 ± 3.57; SAP, 128.9 ± 9.09, p < 0.05) and TNF-α (Q, 215.3 ± 8.67; SAP, 266.4 ± 28.03, p < 0.05) levels. SAP induced Helicobacter and Lactobacillus overgrowth and suppressed Romboutsia and Allobaculum growth and caused aberrations in bacterial metabolites, which were partly reversed by Qingyi granules. DISCUSSION AND CONCLUSIONS: Qingyi granules can modulate the gut microbiota and metabolic abnormalities to ameliorate SAP. Multi-omics approaches allow systematic study of the pharmacological mechanisms of compound prescriptions for critical illnesses.

A Large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the third most lethal cancer worldwide. The lack of effective biomarkers for the early detection of HCC results in unsatisfactory curative treatments. Here, metabolite biomarkers were identified and validated for HCC diagnosis. A total of 1,448 subjects, including healthy controls and patients with chronic hepatitis B virus infection, liver cirrhosis, and HCC, were recruited from multiple centers in China. Liquid chromatography-mass spectrometry-based metabolomics methods were used to characterize the subjects' serum metabolic profiles and to screen and validate the HCC biomarkers. A serum metabolite biomarker panel including phenylalanyl-tryptophan and glycocholate was defined. This panel had a higher diagnostic performance than did α-fetoprotein (AFP) in differentiating HCC from a high-risk population of cirrhosis, such as an area under the receiver-operating characteristic curve of 0.930, 0.892, and 0.807 for the panel versus 0.657, 0.725, and 0.650 for AFP in the discovery set, test set, and cohort 1 of the validation set, respectively. In the nested case-control study, this panel had high sensitivity (range 80.0%-70.3%) to detect preclinical HCC, and its combination with AFP provided better risk prediction of preclinical HCC before clinical diagnosis. Besides, this panel showed a larger area under the receiver-operating characteristic curve than did AFP (0.866 versus 0.682) to distinguish small HCC, and 80.6% of the AFP false-negative patients with HCC were correctly diagnosed using this panel in the test set, which was corroborated by the validation set. The specificity and biological relevance of the identified biomarkers were further evaluated using sera from another two cancers and HCC tissue specimens, respectively. Conclusion: The discovered and validated serum metabolite biomarker panel exhibits good diagnostic performance for the early detection of HCC from at-risk populations. (Hepatology 2018;67:662-675).

Comprehensive Profiling by Non-targeted Stable Isotope Tracing Capillary Electrophoresis-Mass Spectrometry: A New Tool Complementing Metabolomic Analyses of Polar Metabolites.

Mass spectrometry (MS) driven metabolomics is a frequently used tool in various areas of life sciences; however, the analysis of polar metabolites is less commonly included. In general, metabolomic analyses lead to the detection of the total amount of all covered metabolites. This is currently a major limitation with respect to metabolites showing high turnover rates, but no changes in their concentration. Such metabolites and pathways could be crucial metabolic nodes (e.g., potential drug targets in cancer metabolism). A stable-isotope tracing capillary electrophoresis-mass spectrometry (CE-MS) metabolomic approach was developed to cover both polar metabolites and isotopologues in a non-targeted way. An in-house developed software enables high throughput processing of complex multidimensional data. The practicability is demonstrated analyzing [U-13 C]-glucose exposed prostate cancer and non-cancer cells. This CE-MS-driven analytical strategy complements polar metabolite profiles through isotopologue labeling patterns, thereby improving not only the metabolomic coverage, but also the understanding of metabolism.

The role of DKK1 in Alzheimer's disease: A potential intervention point of brain damage prevention?

Dickkopf-1 (DKK1), a secretory glycoprotein discovered for 'inducing generation of head', is an endogenous inhibitor of the canonical Wnt/ß-catenin signaling pathway. It was found to be involved in many pathophysiological processes in vivo. Abnormal expression of DKK1 will alter expressions of related proteins and genes not only in canonical Wnt/ß-catenin signaling pathway but also in other signaling pathways. Previous studies of DKK1 focused on its function in tumors. In recent years, a large number of studies have shown that it plays an important role in embryonic development, neural regeneration, synaptogenesis and so on. Therefore, its role in neuropsychiatric disorders, such as neurodysplasia, cognitive impairment and emotional disorder, has attracted increasing attention. At present, the role of DKK1 in Alzheimer's disease (AD) is one of the research hot topics. This article reviewed the research progress of its role in AD in order to provide new ideas and directions for further studies on the pathogenesis and treatment of AD.

A novel analysis method for biomarker identification based on horizontal relationship: identifying potential biomarkers from large-scale hepatocellular carcinoma metabolomics data.

Omics techniques develop quickly and have made a great contribution to disease study. Omics data are usually complex. How to analyze the data and mine important information has been a key part in omics research. To study the nature of disease mechanisms systematically, we propose a new data analysis method to define the network biomarkers based on horizontal comparison (DNB-HC). DNB-HC performs molecule horizontal relationships to characterize the physiological status and differential network analysis to screen the biomarkers. We applied DNB-HC to analyze a large-scale metabolomics data, which contained 550 samples from a nested case-control hepatocellular carcinoma (HCC) study. A network biomarker was defined, and its areas under curves (AUC) in the receiver-operating characteristic (ROC) analysis for HCC discrimination were larger than those defined by six efficient feature selection methods in most cases. The effectiveness was further corroborated by another nested HCC dataset. Besides, the performance of the defined biomarkers was better than that of α-fetoprotein (AFP), a commonly used clinical biomarker for distinguishing HCC from high-risk population of liver cirrhosis in other two independent metabolomics validation sets. All and 90.3% of the AFP false-negative patients with HCC were correctly diagnosed in these two sets, respectively. The experimental results illustrate that DNB-HC can mine more important information reflecting the nature of the research problems by studying the feature horizontal relationship systematically and identifying effective disease biomarkers in clinical practice. Graphical abstract.

Lactobacillus rhamnosus GG microcapsules inhibit Escherichia coli biofilm formation in coculture.

OBJECTIVES: Microbial biofilms have become one of the most significant causes of nosocomial infections. The aim of this study was to examine the potential quorum sensing inhibitor activities of Lactobacillus rhamnosus GG microcapsules. RESULTS: Lactobacillus rhamnosus GG microcapsules effectively inhibited initial biofilm formation at a concentration of 2.5 × 108 CFU/mL. Furthermore, the inhibition rate was increased to 79% in the Lactobacillus rhamnosus GG microcapsules group, resulting in a reduction in the biofilm maturation stage. In addition, real-time PCR analysis revealed that the LGG microcapsules can act as effective inhibitors of transcriptional activators of the quorum sensing circuit in E.coli, luxS, lsrK, and lsrR. CONCLUSIONS: Lactobacillus rhamnosus GG microcapsules can effectively inhibit biofilm formation and disturb mature biofilms.

Metabolomics and transcriptomics profiles reveal the dysregulation of the tricarboxylic acid cycle and related mechanisms in prostate cancer.

Genetic alterations drive metabolic reprograming to meet increased biosynthetic precursor and energy demands for cancer cell proliferation and survival in unfavorable environments. A systematic study of gene-metabolite regulatory networks and metabolic dysregulation should reveal the molecular mechanisms underlying prostate cancer (PCa) pathogenesis. Herein, we performed gas chromatography-mass spectrometry (GC-MS)-based metabolomics and RNA-seq analyses in prostate tumors and matched adjacent normal tissues (ANTs) to elucidate the molecular alterations and potential underlying regulatory mechanisms in PCa. Significant accumulation of metabolic intermediates and enrichment of genes in the tricarboxylic acid (TCA) cycle were observed in tumor tissues, indicating TCA cycle hyperactivation in PCa tissues. In addition, the levels of fumarate and malate were highly correlated with the Gleason score, tumor stage and expression of genes encoding related enzymes and were significantly related to the expression of genes involved in branched chain amino acid degradation. Using an integrated omics approach, we further revealed the potential anaplerotic routes from pyruvate, glutamine catabolism and branched chain amino acid (BCAA) degradation contributing to replenishing metabolites for TCA cycle. Integrated omics techniques enable the performance of network-based analyses to gain a comprehensive and in-depth understanding of PCa pathophysiology and may facilitate the development of new and effective therapeutic strategies.

Ion-Pair Selection Method for Pseudotargeted Metabolomics Based on SWATH MS Acquisition and Its Application in Differential Metabolite Discovery of Type 2 Diabetes.

The pseudotargeted metabolomics method integrates advantages of nontargeted and targeted analysis because it can acquire data of metabolites in the multireaction monitoring (MRM) mode of mass spectrometry (MS) without needing standards. The key is the ion-pair information collection from samples to be analyzed. It is well-known that sequential windowed acquisition of all theoretical Fragment ion (SWATH) MS mode can acquire MS2 information to a maximum extent. To expediently acquire as many ion-pairs as possible with optimal collision energy (CE), an ion-pair selection approach based on SWATH MS acquisition with variable isolation windows was developed in this study. Initially, nontargeted acquisition of all metabolites information in plasma Standard Reference Material (SRM 1950) was performed by ultra high-performance liquid chromatography (UHPLC)-quadrupole time-of-flight (Q-TOF) MS platform with three CEs. With the help of software tool, the ion-pairs of unique metabolites were gained. Then they were validated in scheduled MRM coupled with UHPLC. After removing false positive, the ion-pairs with an optimal CE was integrated. A total of 1373 unique metabolite ion-pairs were obtained at positive ion mode. And repeatability of the established pseudotargeted approach was evaluated by intraday and interday precision. The results demonstrated the method was stable, reliable, and suitable for metabolomics study. As an application example, alterations of serum metabolites in Type 2 diabetes were investigated by using the established method. This work provides a pseudotargeted ion-pair selection method based on SWATH MS acquisition with the characters of increased metabolite coverage, suitable CE, and convenient processing.

Quality Control of Serum and Plasma by Quantification of (4E,14Z)-Sphingadienine-C18-1-Phosphate Uncovers Common Preanalytical Errors During Handling of Whole Blood.

BACKGROUND: Nonadherence to standard operating procedures (SOPs) during handling and processing of whole blood is one of the most frequent causes affecting the quality of serum and plasma. Yet, the quality of blood samples is of the utmost importance for reliable, conclusive research findings, valid diagnostics, and appropriate therapeutic decisions. METHODS: UHPLC-MS-driven nontargeted metabolomics was applied to identify biomarkers that reflected time to processing of blood samples, and a targeted UHPLC-MS analysis was used to quantify and validate these biomarkers. RESULTS: We found that (4E,14Z)-sphingadienine-C18-1-phosphate (S1P-d18:2) was suitable for the reliable assessment of the pronounced changes in the quality of serum and plasma caused by errors in the phase between collection and centrifugation of whole blood samples. We rigorously validated S1P-d18:2, which included the use of practicality tests on >1400 randomly selected serum and plasma samples that were originally collected during single- and multicenter trials and then stored in 11 biobanks in 3 countries. Neither life-threatening disease states nor strenuous metabolic challenges (i.e., high-intensity exercise) affected the concentration of S1P-d18:2. Cutoff values for sample assessment were defined (plasma, ≤0.085 µg/mL; serum, ≤0.154 µg/mL). CONCLUSIONS: Unbiased valid monitoring to check for adherence to SOP-dictated time for processing to plasma or serum and/or time to storage of whole blood at 4 °C is now feasible. This novel quality assessment step could enable scientists to uncover common preanalytical errors, allowing for identification of serum and plasma samples that should be excluded from certain investigations. It should also allow control of samples before long-term storage in biobanks.

High-throughput metabolic profiling based on small amount of hepatic cells.

Common metabolomics platforms require about 106 cells, which has a limited throughput due to the time-consuming steps of cell culture and preparation. There is a demand for metabolic profiling methods to improve analytical throughput and detection sensitivity based on small amount of cells. In this study, we proposed a high-throughput scheme, integrating 96-well plate cell cultivation, in-situ cell pretreatment, and sensitive dansylation labeling coupled with LC-MS analysis of metabolites inside HepG2 cells (of the order of magnitude of 103 cells in each well). A simple and rapid cell pretreatment was performed showing good extraction efficiency and good precision (the RSDs smaller than 5%) for polar metabolites. The recovery in metabolite extraction evaluated with three isotope-labeled amino acids was from 89.7 to 106.3% at low, medium, and high concentrations. The suitability of the method was illustrated by exploring influences of different fatty acids on HepG2 cells.

Metabolomics Study of Roux-en-Y Gastric Bypass Surgery (RYGB) to Treat Type 2 Diabetes Patients Based on Ultraperformance Liquid Chromatography-Mass Spectrometry.

Roux-en-Y gastric bypass (RYGB) is one of the most effective treatments for long-term weight loss and diabetes remission; however, the mechanisms underlying these changes are not clearly understood. In this study, the serum metabolic profiles of 23 remission and 12 nonremission patients with type 2 diabetes mellitus (T2DM) were measured at baseline, 6- and 12-months after RYGB. A metabolomics analysis was performed based on ultra-performance liquid chromatography-mass spectrometry. Clinical improvements in insulin sensitivity, energy metabolism, and inflammation were related to metabolic alterations of free fatty acids (FFAs), acylcarnitines, amino acids, bile acids, and lipids species. Differential metabolic profiles were observed between the two T2DM subgroups, and patients with severity fat accumulation and oxidation stress may be more suitable for RYGB. Baseline levels of tryptophan, bilirubin, and indoxyl sulfate measured prior to surgery as well as levels of FFA 16:0, FFA 18:3, FFA 17:2, and hippuric acid measured at 6 months after surgery best predicted the suitability and efficacy of RYGB for patients with T2DM. These metabolites represent potential biomarkers that may be clinically helpful in individualized treatment for T2DM patients by RYGB.

Application of LC-MS-based metabolomics method in differentiating septic survivors from non-survivors.

Septic shock is the most severe form of sepsis, which is still one of the leading causes of death in the intensive care unit (ICU). Even though early prognosis and diagnosis are known to be indispensable for reaching an optimistic outcome, pathogenic complexities and the lack of specific treatment make it difficult to predict the outcome individually. In the present study, serum samples from surviving and non-surviving septic shock patients were drawn before clinical intervention at admission. Metabolic profiles of all the samples were analyzed by liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. One thousand four hundred nineteen peaks in positive mode and 1878 peaks in negative mode were retained with their relative standard deviation (RSD) below 30 %, in which 187 metabolites were initially identified by retention time and database in the light of the exact molecular mass. Differences between samples from the survivors and the non-survivors were investigated using multivariate and univariate analysis. Finally, 43 significantly varied metabolites were found in the comparison between survivors and non-survivors. Concretely, metabolites in the tricarboxylic acid (TCA) cycle, amino acids, and several energy metabolism-related metabolites were up-regulated in the non-survivors, whereas those in the urea cycle and fatty acids were generally down-regulated. Metabolites such as lysine, alanine, and methionine did not present significant changes in the comparison. Six metabolites were further defined as primary discriminators differentiating the survivors from the non-survivors at the early stage of septic shock. Our findings reveal that LC-MS-based metabolomics is a useful tool for studying septic shock. Graphical abstract ᅟ.

Development of urinary pseudotargeted LC-MS-based metabolomics method and its application in hepatocellular carcinoma biomarker discovery.

Hepatocellular carcinoma (HCC) is one of the pestilent malignancies leading to cancer-related death. Discovering effective biomarkers for HCC diagnosis is an urgent demand. To identify potential metabolite biomarkers, we developed a urinary pseudotargeted method based on liquid chromatography-hybrid triple quadrupole linear ion trap mass spectrometry (LC-QTRAP MS). Compared with nontargeted method, the pseudotargeted method can achieve better data quality, which benefits differential metabolites discovery. The established method was applied to cirrhosis (CIR) and HCC investigation. It was found that urinary nucleosides, bile acids, citric acid, and several amino acids were significantly changed in liver disease groups compared with the controls, featuring the dysregulation of purine metabolism, energy metabolism, and amino metabolism in liver diseases. Furthermore, some metabolites such as cyclic adenosine monophosphate, glutamine, and short- and medium-chain acylcarnitines were the differential metabolites of HCC and CIR. On the basis of binary logistic regression, butyrylcarnitine (carnitine C4:0) and hydantoin-5-propionic acid were defined as combinational markers to distinguish HCC from CIR. The area under curve was 0.786 and 0.773 for discovery stage and validation stage samples, respectively. These data show that the established pseudotargeted method is a complementary one of targeted and nontargeted methods for metabolomics study.

Multiple reaction monitoring-ion pair finder: a systematic approach to transform nontargeted mode to pseudotargeted mode for metabolomics study based on liquid chromatography-mass spectrometry.

Pseudotargeted metabolic profiling is a novel strategy combining the advantages of both targeted and untargeted methods. The strategy obtains metabolites and their product ions from quadrupole time-of-flight (Q-TOF) MS by information-dependent acquisition (IDA) and then picks targeted ion pairs and measures them on a triple-quadrupole MS by multiple reaction monitoring (MRM). The picking of ion pairs from thousands of candidates is the most time-consuming step of the pseudotargeted strategy. Herein, a systematic and automated approach and software (MRM-Ion Pair Finder) were developed to acquire characteristic MRM ion pairs by precursor ions alignment, MS(2) spectrum extraction and reduction, characteristic product ion selection, and ion fusion. To test the reliability of the approach, a mixture of 15 metabolite standards was first analyzed; the representative ion pairs were correctly picked out. Then, pooled serum samples were further studied, and the results were confirmed by the manual selection. Finally, a comparison with a commercial peak alignment software was performed, and a good characteristic ion coverage of metabolites was obtained. As a proof of concept, the proposed approach was applied to a metabolomics study of liver cancer; 854 metabolite ion pairs were defined in the positive ion mode from serum. Our approach provides a high throughput method which is reliable to acquire MRM ion pairs for pseudotargeted metabolomics with improved metabolite coverage and facilitate more reliable biomarkers discoveries.

Effects of pre-analytical processes on blood samples used in metabolomics studies.

Every day, analytical and bio-analytical chemists make sustained efforts to improve the sensitivity, specificity, robustness, and reproducibility of their methods. Especially in targeted and non-targeted profiling approaches, including metabolomics analysis, these objectives are not easy to achieve; however, robust and reproducible measurements and low coefficients of variation (CV) are crucial for successful metabolomics approaches. Nevertheless, all efforts from the analysts are in vain if the sample quality is poor, i.e. if preanalytical errors are made by the partner during sample collection. Preanalytical risks and errors are more common than expected, even when standard operating procedures (SOP) are used. This risk is particularly high in clinical studies, and poor sample quality may heavily bias the CV of the final analytical results, leading to disappointing outcomes of the study and consequently, although unjustified, to critical questions about the analytical performance of the approach from the partner who provided the samples. This review focuses on the preanalytical phase of liquid chromatography-mass spectrometry-driven metabolomics analysis of body fluids. Several important preanalytical factors that may seriously affect the profile of the investigated metabolome in body fluids, including factors before sample collection, blood drawing, subsequent handling of the whole blood (transportation), processing of plasma and serum, and inadequate conditions for sample storage, will be discussed. In addition, a detailed description of latent effects on the stability of the blood metabolome and a suggestion for a practical procedure to circumvent risks in the preanalytical phase will be given.

Metabolomics study of hepatocellular carcinoma: discovery and validation of serum potential biomarkers by using capillary electrophoresis-mass spectrometry.

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies. The lack of effective screening methods for early diagnosis has been a longstanding bottleneck to improve the survival rate. In the present study, a capillary electrophoresis-time-of-flight mass spectrometry (CE-TOF/MS)-based metabolomics method was employed to discover novel biomarkers for HCC. A total of 183 human serum specimens (77 sera in discovery set and 106 sera in external validation set) were enrolled in this study, and a "serum biomarker model" including tryptophan, glutamine, and 2-hydroxybutyric acid was finally established based on the comprehensive screening and validation workflow. This model was evaluated as an effective tool in that area under the receiver operating characteristic curve reached 0.969 in the discovery set and 0.99 in the validation set for diagnosing HCC from non-HCC (health and cirrhosis). Furthermore, this model enabled the discrimination of small HCC from precancer cirrhosis with an AUC of 0.976, highlighting the potential of early diagnosis. The biomarker model is effective for those a-fetoprotein (AFP) false-negative and false-postive subjects, indicating the complementary function to conventional tumor marker AFP. This study demonstrates the promising potential of CE-MS-based metabolomics approach in finding biomarkers for disease diagnosis and providing special insights into tumor metabolism.

Study of induction chemotherapy efficacy in oral squamous cell carcinoma using pseudotargeted metabolomics.

The effect of induction chemotherapy on oral cancer is controversial owing to inconsistent results. However, the efficacy of induction chemotherapy is closely related to locoregional recurrence, distant metastasis, and overall survival after the treatment. A pseudotargeted metabolomics revealed that metabolites involved in glycolysis and amino acid metabolism were inversely regulated in patients with different chemotherapy responses, and most fatty acids, steroids, and antioxidant substances were up-regulated in all patients after the treatment. Among the metabolites, lactic acid, glucose, glutamic acid, aspartic acid, leucine, and glycerol were remarkably associated with induction chemotherapy efficacy. Subsequently, lactic acid, glutamic acid, and aspartic acid were defined as potential biomarkers of the suitability and efficacy of induction chemotherapy. Our results show that 100.0 and 84.37% of patients with different chemotherapy efficacy were correctly identified in the training and test sets, respectively. Moreover, patient suitability for treatment was correctly predicted for 100.0, 81.25, and 100.0% of patients in the training, test, and external validation sets, respectively. In conclusion, metabolites related to glycolysis, redox homeostasis, and anabolic progress were indicative of induction chemotherapy efficacy both pre- and post-chemotherapy and beneficial for outcome evaluation and prediction. These results illustrate the potentials of metabolomics in personalized induction chemotherapy.

Ion fusion of high-resolution LC-MS-based metabolomics data to discover more reliable biomarkers.

A systematic approach for the fusion of associated ions from a common molecule was developed to generate "one feature for one peak" metabolomics data. This approach guarantees that each molecule is equally selected as a potential biomarker and may largely enhance the chance to obtain reliable findings without employing redundant ion information. The ion fusion is based on low mass variation in contrast to the theoretical calculation measured by a high-resolution mass spectrometer, such as LTQ orbitrap, and a high correlation of ion pairs from the same molecule. The mass characteristics of isotopic distribution, neutral loss, and adduct ions were simultaneously applied to inspect each extracted ion in the range of a predefined retention time window. The correlation coefficient was computed with the corresponding intensities of each ion pair among all experimental samples. Serum metabolomics data for the investigation of hepatocellular carcinoma (HCC) and healthy controls were utilized as an example to demonstrate this strategy. In total, 609 and 1084 ion pairs were respectively found meeting one or more criteria for fusion, and therefore fused to 106 and 169 metabolite features of the datasets in the positive and negative modes, respectively. The important metabolite features were separately discovered and compared to distinguish the HCC from the healthy controls using the two datasets with and without ion fusion. The results show that the developed method can be an effective tool to process high-resolution mass spectrometry data in "omics" studies.

Nontargeted modification-specific metabolomics study based on liquid chromatography-high-resolution mass spectrometry.

Modifications of genes and proteins have been extensively studied in systems biology using comprehensive analytical strategies. Although metabolites are frequently modified, these modifications have not been studied using -omics approaches. Here a general strategy for the nontargeted profiling of modified metabolites, which we call "nontargeted modification-specific metabolomics", is reported. A key aspect of this strategy was the combination of in-source collision-induced dissociation liquid chromatography-mass spectrometry (LC-MS) and global nontargeted LC-MS-based metabolomics. Characteristic neutral loss fragments that are specific for acetylation, sulfation, glucuronidation, glucosidation, or ribose conjugation were reproducibly detected using human urine as a model specimen for method development. The practical application of this method was demonstrated by profiling urine samples from liver cirrhosis patients. Approximately 900 features were identified as modified endogenous metabolites and xenobiotics. Moreover, this strategy supports the identification of compounds not included in traditional metabolomics databases (HMDB, Metlin, and KEGG), which are currently referred to as "unknowns" in metabolomics projects. Nontargeted modification-specific metabolomics opens a new perspective in systems biology.