Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases.

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

Derivative spectrophotometry-assisted determination of tryptophan metabolites emerges host and intestinal flora dysregulations during sepsis.

Sepsis is a life-threatening condition characterized by organ dysfunction resulting from a dysregulated host response to infection. Dysregulated tryptophan (TRP) metabolites serve as significant indicators for endogenous immune turnovers and abnormal metabolism in the intestinal microbiota during sepsis. Therefore, a high coverage determination of TRP and its metabolites in sepsis is beneficial for the diagnosis and prognosis of sepsis, as well as for understanding the underlying mechanism of sepsis development. However, similar structures in TRP metabolites make it challenging for separation and metabolite identification. Here, high-performance liquid chromatography coupled with a diode array detector (HPLC-DAD) was developed to determine TRP metabolites in rat serum. The first-order derivative spectrophotometry of targeted metabolites in the serum was investigated and proved to be promising for chromatographic peak annotation across different columns and systems. The established method separating the targeted metabolites was optimized and validated to be sensitive and accurate. Application of the method revealed dysregulated TRP metabolites, associated with immune disorders and NAD + metabolism in both the host and gut flora in septic rats. Our findings indicate that the derivative spectrophotometry-assisted method enhances metabolite identifications for the chromatographic systems based on DAD detectors and holds promise for precision medicine in sepsis.

MetEx: A Targeted Extraction Strategy for Improving the Coverage and Accuracy of Metabolite Annotation in Liquid Chromatography-High-Resolution Mass Spectrometry Data.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is the most popular platform for untargeted metabolomics studies, but compound annotation is a challenge. In this work, we developed a new LC-HRMS data-targeted extraction method called MetEx for metabolite annotation. MetEx contains the retention time (tR), MS1, and MS2 information of 30 620 metabolites from freely available spectral databases, including MoNA and KEGG. The tR values of 95.4% of the compounds in our database were calculated by the GNN-RT model. The MS2 spectra of 39.4% compounds were also predicted using CFM-ID. MetEx was initially examined on a mixture of 634 standards, considering chemical coverage and accurate metabolite assignment, and later applied to human plasma (NIST SRM 1950), human urine, HepG2 cells, mouse liver tissue, and mouse feces. MetEx correctly assigned 252 out of 253 standards detected in our instruments. The platform also provided 8.0-44.2% more compounds in the biological samples compared to XCMS, MS-DIAL, and MZmine 2. MetEx is implemented and visualized in R and freely available at http://www.metaboex.cn/MetEx.

Metabolite Triplet in Serum Improves the Diagnostic Accuracy of Prediabetes and Diabetes Screening.

Large-scale population screenings are not feasible by applying laborious oral glucose tolerance tests, but using fasting blood glucose (FPG) and glycated hemoglobin (HbA1c), a considerable number of diagnoses are missed. A novel marker is urgently needed to improve the diagnostic accuracy of broad-scale diabetes screening in easy-to-collect blood samples. In this study, by applying a novel knowledge-based, multistage discovery and validation strategy, we scaled down from 108 diabetes-associated metabolites to a diagnostic metabolite triplet (Met-T), namely hexose, 2-hydroxybutyric/2-hydroxyisobutyric acid, and phenylalanine. Met-T showed in two independent cohorts, each comprising healthy controls, prediabetic, and diabetic individuals, distinctly higher diagnostic sensitivities for diabetes screening than FPG alone (>79.6 vs <68%). Missed diagnoses decreased from >32% using fasting plasma glucose down to <20.4%. Combining Met-T and fasting plasma glucose further improved the diagnostic accuracy. Additionally, a positive association of Met-T with future diabetes risk was found (odds ratio: 1.41; p = 1.03 × 10-6). The results reveal that missed prediabetes and diabetes diagnoses can be markedly reduced by applying Met-T alone or in combination with FPG and it opens perspectives for higher diagnostic accuracy in broad-scale diabetes-screening approaches using easy to collect sample materials.

Strategy for Nontargeted Metabolomic Annotation and Quantitation Using a High-Resolution Spectral-Stitching Nanoelectrospray Direct-Infusion Mass Spectrometry with Data-Independent Acquisition.

Direct-infusion nanoelectrospray ionization high-resolution mass spectrometry (DI-nESI-HRMS) is an alternative approach to chromatography-MS-based techniques for nontargeted metabolomics, offering a high sample throughout. However, its annotation accuracy of analytes is still full of challenges. In this study, we proposed a strategy for the annotation and quantitation of nontargeted metabolomic data using a spectral-stitching DI-nESI-HRMS with data-independent acquisition. The metabolite annotation strategy included the isotopic distribution, MS/MS spectrum similarity, and precursor and product ion correlation as well as matching of the extracted metabolite features along with the targeted metabolite precursors. Two groups of mixed standard solutions containing 40 and 79 metabolites were, respectively, used to establish the metabolite annotation strategy and validate its reliability. The results showed that the detected standards could be well annotated at top three explanations and total qualitative percentages were 100% (40 of 40) for the standard solution and 94.9% (74 of 78) for the standards spiked into the serum matrix. The intensity of the precursor ions was used for quantitation except for isomers, which were quantified by the intensities of the characteristic product ions if available. Finally, the strategy was applied to study serum metabolomics in diabetes, and the results demonstrated that it is promising for a large-scale cohort metabolomic study.

Untargeted Defining Protein-Metabolites Interaction Based on Label-Free Kinetic Size Exclusion Chromatography-Mass Spectrometry.

The specific interactions between protein and metabolites (PMIs) are closely related to many cellular processes and play a vital role in signal transduction and regulating material and energy metabolism. However, most of the available analytical strategies for PMIs involve chemical modification of metabolites or immobilization of protein, which has restricted current PMIs study mainly to lipid-protein and hydrophobic metabolites. In this work, a label-free online kinetic size exclusion chromatography-mass spectrometry (KSEC-MS) method combined with untargeted metabolomics was developed to define PMIs in a complex system. The metabolite mixture and target protein were injected into the SEC column sequentially without preincubation, and the separation results of KSEC were monitored by global metabolite profiling with mass spectrometry. The potential ligands in the metabolite mixture can be discovered if their migration patterns were affected by the target protein and the variation was positively correlated with the concentration of target protein. To verify this approach, carbonic anhydrase was first selected as a test protein, and acetazolamide as its known inhibitor was successfully defined. Furthermore, human serum albumin (HSA) as the common transport carrier of metabolites was selected as a target protein to demonstrate the usefulness of this approach. Multiple endogenous ligands of HSA were simultaneously defined from the extracted metabolites of human serum; most of them are polar metabolites rather than nonpolar lipids. This approach can provide a novel way for mapping and identifying unknown PMIs in a complex system, especially for polar metabolites-protein interactions.

Pseudotargeted Method Based on Parallel Column Two-Dimensional Liquid Chromatography-Mass Spectrometry for Broad Coverage of Metabolome and Lipidome.

Metabolite and lipid profilings usually need two liquid chromatography-mass spectrometry (LC-MS) methods because of a great polarity difference. A pseudotargeted metabolomics method as a novel emerging approach can integrate the advantages of nontargeted and targeted methods. Here, we aim to establish a comprehensive method for metabolome and lipidome by using a parallel column-based two-dimensional LC (PC-2DLC)-MS and pseudotargeted approach. To simultaneously extract as many polar metabolites and nonpolar lipids as possible, we systematically optimized the sample pretreatment process, and isopropanol/methanol (3:1, v/v) and isopropanol/water (7:3, v/v) were selected as the extraction and reconstitution solvents, respectively. The detected triglycerides significantly increased after the sample pretreatment optimization. Then PC-2DLC coupled with Triple TOF MS was applied to analyze a mixed sample from serum, urine, and liver tissue matrixes. The multiple reaction monitoring (MRM) transitions of the metabolome and lipidome were defined according to the "MRM-Ion Pair Finder" software and lipidomics MRM-transition database, respectively. After verification by QTRAP MS in the scheduled MRM mode, 1609 potential metabolites and lipids corresponding to 1294 MRM transitions, and 847 potential metabolites and lipids corresponding to 687 MRM transitions were detected in positive and negative ion modes, respectively. They range at about 30 orders of magnitude in octanol/water partition coefficient. The pseudotargeted 2DLC-MS method was validated to have good analytical characteristics. As a proof of applicability, sera from type 2 diabetic patients were investigated by the established method. The results indicated that the pseudotargeted 2DLC-MS method is reliable and repeatable and can be used in a metabolomics study.

Carboxymethylated polyethylenimine modified magnetic nanoparticles specifically for purification of His-tagged protein.

Employing immobilized metal-ion affinity chromatography and magnetic separation could ideally provide a useful analytical strategy for purifying His-tagged protein. In the current study, a facile route was designed to prepare CMPEI-Ni2+ @SiO2 @Fe3 O4 (CMPEI=carboxymethylated polyethyleneimine) magnetic nanoparticles composed of a strong magnetic core of Fe3 O4 and a Ni2+ -immobilized carboxymethylated polyethyleneimine coated outside shell, which was formed by electrostatic interactions between polyanionic electrolyte of carboxymethylated polyethyleneimine and positively charged surface of 3-(trimethoxysilyl)propylamin modified SiO2 @Fe3 O4 . The resulting CMPEI-Ni2+ @SiO2 @Fe3 O4 composite nanoparticles displayed well-uniform structure and high magnetic responsiveness. Hexa His-tagged peptides and purified His-tagged recombinant retinoid X receptor alpha were chosen as the model samples to evaluate the adsorption, capacity, and reusability of the composite nanoparticles. The results demonstrated the CMPEI-Ni2+ @SiO2 @Fe3 O4 nanoparticles possessed rapid adsorption, large capacity, and good recyclability. The obtained nanoparticles were further used to purify His-tagged protein in practical environment. It was found that the nanoparticles could selectively capture His-tagged recombinant retinoid X receptor protein from complex cell lysate. Owing to its easy synthesis, large binding capacity, and good reusability, the prepared CMPEI-Ni2+ @SiO2 @Fe3 O4 magnetic nanoparticles have great potential for application in biotechnological fields.

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.

Comprehensive metabolite quantitative assay based on alternate metabolomics and lipidomics analyses.

Metabolomics-based precision medicine is facing several obstacles including cross-platform data comparison issue and the lack of metabolome benchmark values of healthy population, one of main reasons is the shortage of comprehensive metabolome quantitation methods. Here, we developed an alternate reversed-phase liquid chromatography-mass spectrometry (RPLC-MS) method to quantitatively determine metabolites and lipids. Assisted by a wide set of reference standards and real samples, up to 397 multiple reaction monitoring (MRM) transitions (239 for positive and 158 for negative ion modes) and 1080 MRM transitions (607 for positive and 473 for negative ion modes) were defined respectively in the metabolomic and lipidomic analyses with more than 1000 metabolites and lipids being quantified. Among them, 144 analytes including amines, amino acids, benzenoids, peptides, nucleobases and related, bile acids, carboxylic acids, fatty acids, hormones, indoles and others were absolutely quantified, while carnitines, lyso-phosphatidylcholines, lyso-phosphatidylethanolamines, free fatty acids, sphingomyelins, phosphatidylcholines (PCs), alkyl and alkenyl substituted PCs, phosphatidylethanolamines (PEs), alkyl and alkenyl substituted PEs and triacylglycerols were semiquantified. The developed method was validated to have good analytical characteristics. Analytical results of standard reference material 1950 human plasma had a good agreement with literature data. As a proof of application, this method was used to study serum metabolic pattern changes of patients with hyperuricemia and nonalcoholic fatty liver. This alternate RPLC-MS method for quantitative metabolites and lipids analysis can further be used to provide technology and large-scale data support for precision medicine and life sciences.

A high throughput lipidomics method and its application in atrial fibrillation based on 96-well plate pretreatment and liquid chromatography-mass spectrometry.

Successful applications of lipidomics in clinic need study large-scale samples, and the bottlenecks are in throughput and robustness of the lipid analytical method. Here, we report an untargeted lipidomics method by combining high throughput pretreatment in the 96-well plate with ultra-high performance liquid chromatography coupled to quadrupole time-of-flight tandem mass spectrometry. The developed method was validated to have satisfactory analytical characteristics in terms of linearity, repeatability and extraction recovery. It can be used to handle 96 samples simultaneously in 25 min and detect 441 lipids in plasma sample. Storage stability investigation on lipid extracts provided an operable procedure for large-scale sample analysis and demonstrated most lipids were stable in autosampler at 10 °C within 36 h and at -80 °C within 72 h after the pretreatment. To prove the usefulness, the method was employed to investigate abnormal plasma lipidome related to atrial fibrillation. A biomarker panel with the area under the curve (AUC) values of 0.831 and 0.745 was achieved in the discovery and external validation sets, respectively. These results showed that the developed method is applicable for large-scale biological sample handling and lipid analysis of plasma.

Metabolome-Genome-Wide Association Study (mGWAS) Reveals Novel Metabolites Associated with Future Type 2 Diabetes Risk and Susceptibility Loci in a Case-Control Study in a Chinese Prospective Cohort.

In a Chinese prospective cohort, 500 patients with new-onset type 2 diabetes (T2D) within 4.61 years and 500 matched healthy participants are selected as case and control groups, and randomized into discovery and validation sets to discover the metabolite changes before T2D onset and the related diabetogenic loci. A serum metabolomics analysis reveals that 81 metabolites changed significantly before T2D onset. Based on binary logistic regression, eight metabolites are defined as a biomarker panel for T2D prediction. Pipecolinic acid, carnitine C14:0, epinephrine and phosphatidylethanolamine 34:2 are first found associated with future T2D. The addition of the biomarker panel to the clinical markers (BMI, triglycerides, and fasting glucose) significantly improves the predictive ability in the discovery and validation sets, respectively. By associating metabolomics with genomics, a significant correlation (p < 5.0 × 10-8) between eicosatetraenoic acid and the FADS1 (rs174559) gene is observed, and suggestive correlations (p < 5.0 × 10-6) between pipecolinic acid and CHRM3 (rs535514), and leucine/isoleucine and WWOX (rs72487966) are discovered. Elevated leucine/isoleucine levels increased the risk of T2D. In conclusion, multiple metabolic dysregulations are observed to occur before T2D onset, and the new biomarker panel can help to predict T2D risk.

Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography-mass spectrometry.

Untargeted methods are typically used in the detection and discovery of small organic compounds in metabolomics research, and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is one of the most commonly used platforms for untargeted metabolomics. Although they are non-biased and have high coverage, untargeted approaches suffer from unsatisfying repeatability and a requirement for complex data processing. Targeted metabolomics based on triple-quadrupole mass spectrometry (TQMS) could be a complementary tool because of its high sensitivity, high specificity and excellent quantification ability. However, it is usually applicable to known compounds: compounds whose identities are known and/or are expected to be present in the analyzed samples. Pseudotargeted metabolomics merges the advantages of untargeted and targeted metabolomics and can act as an alternative to the untargeted method. Here, we describe a detailed protocol of pseudotargeted metabolomics using UHPLC-TQMS. An in-depth, untargeted metabolomics experiment involving multiple UHPLC-HRMS runs with MS at different collision energies (both positive and negative) is performed using a mixture obtained using small amounts of the analyzed samples. XCMS, CAMERA and Multiple Reaction Monitoring (MRM)-Ion Pair Finder are used to find and annotate peaks and choose transitions that will be used to analyze the real samples. A set of internal standards is used to correct for variations in retention time. High coverage and high-performance quantitative analysis can be realized. The entire protocol takes ~5 d to complete and enables the simultaneously semiquantitative analysis of 800-1,300 metabolites.

Alternate reversed-phase and hydrophilic interaction liquid chromatography coupled with mass spectrometry for broad coverage in metabolomics analysis.

Because of the greatly different physicochemical properties of metabolites, comprehensive metabolite profiling analysis has always been a challenging task. Reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) have been used to the analysis of nonpolar metabolites and polar metabolites, respectively. In this work, an alternate HILIC/RPLC-mass spectrometry (MS) approach was developed for the comprehensive and high-throughput analysis of polar and nonpolar metabolites. HILIC and RPLC are respectively performed on two ultra-high performance LC (UHPLC) systems, and coupled to one mass spectrometer to acquire the data. When HILIC gradient elution is running RPLC is in a washing and equilibration state, and vice versa. As a result, the total analysis time was reduced by about one third to 25.4 min. Two hundred and eight representative standards including at least twelve types of commonly met metabolites, SRM 1950 plasma, serum, urine and liver tissue samples were used to test the established alternate HILIC/RPLC-MS method. The results demonstrated that the method possessed high metabolite coverage. The developed method was validated to have good linearity and repeatability. As an example of application, 61 significantly changed metabolites in the colon cancer tissues were defined by this established method.

Surface-bonded amide-functionalized imidazolium ionic liquid as stationary phase for hydrophilic interaction liquid chromatography.

The amide group modified silica materials are popular stationary phases for hydrophilic interaction liquid chromatography (HILIC). Meanwhile, surface-confined imidazolium ionic liquids (ILs) have been proved to be useful HILIC stationary phases and possess many unique properties. In this study, the synthesis of an amide-functionalized imidazolium IL was conducted which was then bonded onto silica surface to obtain a novel imidazolium-embedded amide stationary phase for HILIC. The combination of the amide group and imidazolium IL moiety might bring some advantages in selectivity or retention and therefore extended its applications. After characterizing the prepared IL and the resulting modified silica materials, the chromatographic performance and separation selectivity of the packed column were evaluated and compared with a commercial amide column. Then, the retention behavior was investigated through observing the retention factors at different chromatographic conditions using a wide range of compounds. Exceptionally, the prepared amide IL column exhibited superior separation performance towards complex samples such as flavonoids mixture, soybean flavonoids and human urine. All the results indicated that the novel amide IL column possessed an anion-exchange/HILIC mixed-mode retention mechanism and could be useful in the sample analysis as a promising candidate for HILIC stationary phase.