Multiplexed Antibody Glycosylation Profiling Using Dual Enzyme Digestion and Liquid Chromatography-Triple Quadrupole Mass Spectrometry Method.

Antibody glycosylation plays a crucial role in the humoral immune response by regulating effector functions and influencing the binding affinity to immune cell receptors. Previous studies have focused mainly on the immunoglobulin G (IgG) isotype owing to the analytical challenges associated with other isotypes. Thus, the development of a sensitive and accurate analytical platform is necessary to characterize antibody glycosylation across multiple isotypes. In this study, we have developed an analytical workflow using antibody-light-chain affinity beads to purify IgG, IgA, and IgM from 16 µL of human plasma. Dual enzymes, trypsin and Glu-C, were used during on-bead digestion to obtain enzymatic glycopeptides and protein-specific surrogate peptides. Ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry was used in order to determine the sensitivity and specificity. Our platform targets 95 glycopeptides across the IgG, IgA, and IgM isotypes, as well as eight surrogate peptides representing total IgG, four IgG classes, two IgA classes, and IgM. Four stable isotope-labeled internal standards were added after antibody purification to calibrate the preparation and instrumental bias during analysis. Calibration curves constructed using serially diluted plasma samples showed good curve fitting (R2 > 0.959). The intrabatch and interbatch precision for all the targets had relative standard deviation of less than 29.6%. This method was applied to 19 human plasma samples, and the glycosylation percentages were calculated, which were comparable to those reported in the literature. The developed method is sensitive and accurate for Ig glycosylation profiling. It can be used in clinical investigations, particularly for detailed humoral immune profiling.

Phosphorylation of enteroviral 2Apro at Ser/Thr125 benefits its proteolytic activity and viral pathogenesis.

Enteroviral 2A proteinase (2Apro ), a well-established and important viral functional protein, plays a key role in shutting down cellular cap-dependent translation, mainly via its proteolytic activity, and creating optimal conditions for Enterovirus survival. Accumulated data show that viruses take advantage of various signaling cascades for their life cycle; studies performed by us and others have demonstrated that the extracellular signal-regulated kinase (ERK) pathway is essential for enterovirus A71 (EV-A71) and other viruses replication. We recently showed that ERK1/2 is required for the proteolytic activity of viral 2Apro ; however, the mechanism underlying the regulation of 2Apro remains unknown. Here, we demonstrated that the 125th residue Ser125 of EV-A71 2Apro or Thr125 of coxsackievirus B3 2Apro , which is highly conserved in the Enterovirus, was phosphorylated by ERK1/2. Importantly, 2Apro with phosphor-Ser/Thr125 had much stronger proteolytic activity toward eukaryotic initiation factor 4GI and rendered the virus more efficient for multiplication and pathogenesis in hSCARB2 knock-in mice than that in nonphospho-Ser/Thr125A (S/T125A) mutants. Notably, phosphorylation-mimic mutations caused deleterious changes in 2Apro catalytic function (S/T125D/E) and in viral propagation (S125D). Crystal structure simulation analysis showed that Ser125 phosphorylation in EV-A71 2Apro enabled catalytic Cys to adopt an optimal conformation in the catalytic triad His-Asp-Cys, which enhances 2Apro proteolysis. Therefore, we are the first to report Ser/Thr125 phosphorylation of 2Apro increases enteroviral adaptation to the host to ensure enteroviral multiplication, causing pathogenicity. Additionally, weakened viruses containing a S/T125A mutation could be a general strategy to develop attenuated Enterovirus vaccines.

PanGPCR: predictions for multiple targets, repurposing and side effects.

SUMMARY: Drug discovery targeting G protein-coupled receptors (GPCRs), the largest known class of therapeutic targets, is challenging. To facilitate the rapid discovery and development of GPCR drugs, we built a system, PanGPCR, to predict multiple potential GPCR targets and their expression locations in the tissues, side effects and possible repurposing of GPCR drugs. With PanGPCR, the compound of interest is docked to a library of 36 experimentally determined crystal structures comprising of 46 docking sites for human GPCRs, and a ranked list is generated from the docking studies to assess all GPCRs and their binding affinities. Users can determine a given compound's GPCR targets and its repurposing potential accordingly. Moreover, potential side effects collected from the SIDER (Side-Effect Resource) database and mapped to 45 tissues and organs are provided by linking predicted off-targets and their expressed sequence tag profiles. With PanGPCR, multiple targets, repurposing potential and side effects can be determined by simply uploading a small ligand. AVAILABILITY AND IMPLEMENTATION: PanGPCR is freely accessible at https://gpcrpanel.cmdm.tw/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prolonged exposure to traffic-related particulate matter and gaseous pollutants implicate distinct molecular mechanisms of lung injury in rats.

BACKGROUND: Exposure to air pollution exerts direct effects on respiratory organs; however, molecular alterations underlying air pollution-induced pulmonary injury remain unclear. In this study, we investigated the effect of air pollution on the lung tissues of Sprague-Dawley rats with whole-body exposure to traffic-related PM1 (particulate matter < 1 µm in aerodynamic diameter) pollutants and compared it with that in rats exposed to high-efficiency particulate air-filtered gaseous pollutants and clean air controls for 3 and 6 months. Lung function and histological examinations were performed along with quantitative proteomics analysis and functional validation. RESULTS: Rats in the 6-month PM1-exposed group exhibited a significant decline in lung function, as determined by decreased FEF25-75% and FEV20/FVC; however, histological analysis revealed earlier lung damage, as evidenced by increased congestion and macrophage infiltration in 3-month PM1-exposed rat lungs. The lung tissue proteomics analysis identified 2673 proteins that highlighted the differential dysregulation of proteins involved in oxidative stress, cellular metabolism, calcium signalling, inflammatory responses, and actin dynamics under exposures to PM1 and gaseous pollutants. The presence of PM1 specifically enhanced oxidative stress and inflammatory reactions under subchronic exposure to traffic-related PM1 and suppressed glucose metabolism and actin cytoskeleton signalling. These factors might lead to repair failure and thus to lung function decline after chronic exposure to traffic-related PM1. A detailed pathogenic mechanism was proposed to depict temporal and dynamic molecular regulations associated with PM1- and gaseous pollutants-induced lung injury. CONCLUSION: This study explored several potential molecular features associated with early lung damage in response to traffic-related air pollution, which might be used to screen individuals more susceptible to air pollution.

Affinity Purification Coupled to Stable Isotope Dilution LC-MS/MS Analysis to Discover IgG4 Glycosylation Profiles for Autoimmune Pancreatitis.

Type 1 autoimmune pancreatitis (AIP) is categorized as an IgG4-related disease (IgG4-RD), where a high concentration of plasma IgG4 is one of the common biomarkers among patients. IgG Fc-glycosylation has been reported to be potential biosignatures for diseases. However, human IgG3 and IgG4 Fc-glycopeptides from populations in Asia were found to be isobaric ions when using LC-MS/MS as an analytical tool. In this study, an analytical workflow that coupled affinity purification and stable isotope dilution LC-MS/MS was developed to dissect IgG4 glycosylation profiles for autoimmune pancreatitis. Comparing the IgG4 and glycosylation profiles among healthy controls, patients with pancreatic ductal adenocarcinoma (PDAC), and AIP, the IgG4 glycosylations from the AIP group were found to have more digalactosylation (compared to PDAC) and less monogalactosylation (compared to HC). In addition, higher fucosylation and sialylation profiles were also discovered for the AIP group. The workflow is efficient and selective for IgG4 glycopeptides, and can be used for clinical biosignature discovery.

Improved Dried Blood Spot-Based Metabolomics Analysis by a Postcolumn Infused-Internal Standard Assisted Liquid Chromatography-Electrospray Ionization Mass Spectrometry Method.

Dried blood spots (DBSs) have gained increasing attention recently with their growing importance in precision medicine. DBS-based metabolomics analysis provides a powerful tool for investigating new biomarkers. Until now, very few studies have discussed measures for improving analytical accuracy with the consideration of the special characteristics of DBSs. The present study proposed a postcolumn infused-internal standard (PCI-IS) assisted strategy to improve data quality for DBS-based metabolomics studies. An efficient sample preparation protocol with 80% acetonitrile as the extraction solvent was first established to improve the metabolite recovery. The PCI-IS assisted liquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS) method was used to simultaneously estimate the blood volume and correct the signal change caused by ion source contamination and the matrix effect to evaluate the spot volume effect and hematocrit (Hct) variation effect on target metabolites. Phenylalanine-d8 was selected as the single PCI-IS to correct the matrix effect. For calibration of errors caused by the blood volume difference, 75% of the test metabolites showed good correlation (R2 ≥ 0.9) between the spot volume and the signal intensity after PCI-IS correction compared to less than 50% metabolites with good correlation before calibration. The spot volume was further calibrated by the same PCI-IS. Investigation of the Hct variation effect on target metabolites revealed that it affected the concentrations of metabolites in the DBS samples depending on their abundance in the red blood cell (RBC) or plasma; it is essential to preinvestigate the distribution of metabolites in blood to minimize the comparison bias in metabolomics studies. Finally, the PCI-IS assisted method was applied to study acetaminophen-induced liver toxicity. The results indicated that the proposed PCI-IS strategy could effectively remove analytical errors and improve the data quality, which would make the DBS-based metabolomics more feasible in real-world applications.

High accuracy differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma by immunoglobulin G glycosylation.

BACKGROUND: Misdiagnosis of autoimmune pancreatitis (AIP) as pancreatic cancer (PDAC) or vice versa can cause dismal patents' outcomes. Changes in IgG glycosylation are associated with cancers and autoimmune diseases. This study investigated the IgG glycosylation profiles as diagnostic and prognostic biomarkers in PDAC and AIP. METHODS: Serum IgG-glycosylation profiles from 86 AIP patients, 115 PDAC patients, and 57 controls were analyzed using liquid chromatography-electrospray ionization mass spectrometry. Classification and regression tree (CART) analysis was applied to build a decision tree for discriminating PDAC from AIP. The result was validated in an independent cohort. RESULTS: Compared with AIP patients and controls, PDAC patients had significantly higher agalactosylation, lower fucosylation, and sialylation of IgG1, a higher agalactosylation ratio of IgG1 and a higher agalactosylation ratio of IgG2. AIP patients had significantly higher fucosylation of IgG1 and a higher sialylation ratio of IgG subclasses 1, 2 and 4. Using the CART analysis of agalactosylation and sialylation ratios in the IgG to discriminate AIP from PDAC, the diagnostic accuracy of the glycan markers was 93.8% with 94.6% sensitivity and 92.9% specificity. There were no statistically significant difference of IgG-glycosylation profiles between diffuse type and focal type AIP. CONCLUSIONS: AIP and PDAC patients have distinct IgG-glycosylation profilings. IgG-glycosylation could different PDAC from AIP with high accuracy.

Metabolome analysis for investigating host-gut microbiota interactions.

Dysbiosis of the gut microbiome is associated with host health conditions. Many diseases have shown to have correlations with imbalanced microbiota, including obesity, inflammatory bowel disease, cancer, and even neurodegeneration disorders. Metabolomics studies targeting small molecule metabolites that impact the host metabolome and their biochemical functions have shown promise for studying host-gut microbiota interactions. Metabolome analysis determines the metabolites being discussed for their biological implications in host-gut microbiota interactions. To facilitate understanding the critical aspects of metabolome analysis, this article reviewed (1) the sample types used in host-gut microbiome studies; (2) mass spectrometry (MS)-based analytical methods and (3) useful tools for MS-based data processing/analysis. In addition to the most frequently used sample type, feces, we also discussed others biosamples, such as urine, plasma/serum, saliva, cerebrospinal fluid, exhaled breaths, and tissues, to better understand gut metabolite systemic effects on the whole organism. Gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), three powerful tools that can be utilized to study host-gut microbiota interactions, are included with examples of their applications. After obtaining big data from MS-based instruments, noise removal, peak detection, missing value imputation, and data analysis are all important steps for acquiring valid results in host-gut microbiome research. The information provided in this review will help new researchers aiming to join this field by providing a global view of the analytical aspects involved in gut microbiota-related metabolomics studies.

Web Server for Peak Detection, Baseline Correction, and Alignment in Two-Dimensional Gas Chromatography Mass Spectrometry-Based Metabolomics Data.

Two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC/TOF-MS) is superior for chromatographic separation and provides great sensitivity for complex biological fluid analysis in metabolomics. However, GC×GC/TOF-MS data processing is currently limited to vendor software and typically requires several preprocessing steps. In this work, we implement a web-based platform, which we call GC2MS, to facilitate the application of recent advances in GC×GC/TOF-MS, especially for metabolomics studies. The core processing workflow of GC2MS consists of blob/peak detection, baseline correction, and blob alignment. GC2MS treats GC×GC/TOF-MS data as pictures and clusters the pixels as blobs according to the brightness of each pixel to generate a blob table. GC2MS then aligns the blobs of two GC×GC/TOF-MS data sets according to their distance and similarity. The blob distance and similarity are the Euclidean distance of the first and second retention times of two blobs and the Pearson's correlation coefficient of the two mass spectra, respectively. GC2MS also directly corrects the raw data baseline. The analytical performance of GC2MS was evaluated using GC×GC/TOF-MS data sets of Angelica sinensis compounds acquired under different experimental conditions and of human plasma samples. The results show that GC2MS is an easy-to-use tool for detecting peaks and correcting baselines, and GC2MS is able to align GC×GC/TOF-MS data sets acquired under different experimental conditions. GC2MS is freely accessible at http://gc2ms.web.cmdm.tw .

Ion trace detection algorithm to extract pure ion chromatograms to improve untargeted peak detection quality for liquid chromatography/time-of-flight mass spectrometry-based metabolomics data.

Able to detect known and unknown metabolites, untargeted metabolomics has shown great potential in identifying novel biomarkers. However, elucidating all possible liquid chromatography/time-of-flight mass spectrometry (LC/TOF-MS) ion signals in a complex biological sample remains challenging since many ions are not the products of metabolites. Methods of reducing ions not related to metabolites or simply directly detecting metabolite related (pure) ions are important. In this work, we describe PITracer, a novel algorithm that accurately detects the pure ions of a LC/TOF-MS profile to extract pure ion chromatograms and detect chromatographic peaks. PITracer estimates the relative mass difference tolerance of ions and calibrates the mass over charge (m/z) values for peak detection algorithms with an additional option to further mass correction with respect to a user-specified metabolite. PITracer was evaluated using two data sets containing 373 human metabolite standards, including 5 saturated standards considered to be split peaks resultant from huge m/z fluctuation, and 12 urine samples spiked with 50 forensic drugs of varying concentrations. Analysis of these data sets show that PITracer correctly outperformed existing state-of-art algorithm and extracted the pure ion chromatograms of the 5 saturated standards without generating split peaks and detected the forensic drugs with high recall, precision, and F-score and small mass error.

Machine learning algorithms to predict colistin-induced nephrotoxicity from electronic health records in patients with multidrug-resistant Gram-negative infection.

OBJECTIVES: Colistin-induced nephrotoxicity prolongs hospitalisation and increases mortality. The study aimed to construct machine learning models to predict colistin-induced nephrotoxicity in patients with multidrug-resistant Gram-negative infection. METHODS: Patients receiving colistin from three hospitals in the Clinical Research Database were included. Data were divided into a derivation cohort (2011-2017) and a temporal validation cohort (2018-2020). Fifteen machine learning models were established by categorical boosting, light gradient boosting machine and random forest. Classifier performances were compared by the sensitivity, F1 score, Matthews correlation coefficient (MCC), area under the receiver operating characteristic (AUROC) curve, and area under the precision-recall curve (AUPRC). SHapley Additive exPlanations plots were drawn to understand feature importance and interactions. RESULTS: The study included 1392 patients, with 360 (36.4%) and 165 (40.9%) experiencing nephrotoxicity in the derivation and temporal validation cohorts, respectively. The categorical boosting with oversampling achieved the highest performance with a sensitivity of 0.860, an F1 score of 0.740, an MCC of 0.533, an AUROC curve of 0.823, and an AUPRC of 0.737. The feature importance demonstrated that the days of colistin use, cumulative dose, daily dose, latest C-reactive protein, and baseline haemoglobin were the most important risk factors, especially for vulnerable patients. A cutoff colistin dose of 4.0 mg/kg body weight/d was identified for patients at higher risk of nephrotoxicity. CONCLUSIONS: Machine learning techniques can be an early identification tool to predict colistin-induced nephrotoxicity. The observed interactions suggest a modification in dose adjustment guidelines. Future geographic and prospective validation studies are warranted to strengthen the real-world applicability.

Batch Normalizer: a fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight mass spectrometry-based metabolomics data and comparison with current calibration methods.

Metabolomics is a powerful tool for understanding phenotypes and discovering biomarkers. Combinations of multiple batches or data sets in large cross-sectional epidemiology studies are frequently utilized in metabolomics, but various systematic biases can introduce both batch and injection order effects and often require proper calibrations prior to chemometric analyses. We present a novel algorithm, Batch Normalizer, to calibrate large scale metabolomic data. Batch Normalizer utilizes a regression model with consideration of the total abundance of each sample to improve its calibration performance, and it is able to remove both batch effect and injection order effects. This calibration method was tested using liquid chromatography/time-of-flight mass spectrometry (LC/TOF-MS) chromatograms of 228 plasma samples and 23 pooled quality control (QC) samples. We evaluated the performance of Batch Normalizer by examining the distribution of relative standard deviation (RSD) for all peaks detected in the pooled QC samples, the average Pearson correlation coefficients for all peaks between any two of QC samples, and the distribution of QC samples in the scores plot of a principal component analysis (PCA). After calibration by Batch Normalizer, the number of peaks in QC samples with RSD less than 15% increased from 11 to 914, all of the QC samples were closely clustered in PCA scores plot, and the average Pearson correlation coefficients for all peaks of QC samples increased from 0.938 to 0.976. This method was compared to 7 commonly used calibration methods. We discovered that using Batch Normalizer to calibrate LC/TOF-MS data produces the best calibration results.

Distribution-based classification method for baseline correction of metabolomic 1D proton nuclear magnetic resonance spectra.

Baseline distortion in 1D (1)H NMR data complicates the quantification of individual components of biofluids in metabolomic experiments. Current 1D (1)H NMR baseline correction methods usually require manual parameter and filter tuning by experienced users to obtain desirable results from complex metabolomic spectra, thus becoming prone to correction variation and biased quantification. We present a novel alternative method, BaselineCorrector, for automatically estimating the baselines of 1D (1)H NMR metabolomic data. By collecting the standard deviations of spectral intensities, using a moving window to slide through a spectrum, BaselineCorrector can model the distribution of noise standard deviation as a derived chi-squared distribution in each window and then determine optimal parameters for least-error classification of signal and noise. Due to the universal property of noise distributions, BaselineCorrector can robustly recognize the baseline segments in various spectra. In addition to the commonly used 1D NOESY and CPMG pulse sequences, BaselineCorrector also provides an algorithm for correcting diffusion-edited NMR spectra. Using its classification model, BaselineCorrector is able to preserve low signal peaks and correctly handle wide, overlapping peaks in complex metabolomic spectra.

Metabolomic characterization of rhubarb species by capillary electrophoresis and ultra-high-pressure liquid chromatography.

This study developed CE and ultra-high-pressure LC (UHPLC) methods coupled with UV detectors to characterize the metabolomic profiles of different rhubarb species. The optimal CE conditions used a BGE with 15 mM sodium tetraborate, 15 mM sodium dihydrogen phosphate monohydrate, 30 mM sodium deoxycholate, and 30% ACN v/v at pH 8.3. The optimal UHPLC conditions used a mobile phase composed of 0.05% phosphate buffer and ACN with gradient elution. The gradient profile increased linearly from 10 to 21% ACN within the first 25 min, then increased to 33% ACN for the next 10 min. It took another 5 min to reach the 65% ACN, then for the next 5 min, it stayed unchanged. Sixteen samples of Rheum officinale and Rheum tanguticum collected from various locations were analyzed by CE and UHPLC methods. The metabolite profiles of CE were aligned and baseline corrected before chemometric analysis. Metabolomic signatures of rhubarb species from CE and UHPLC were clustered using principle component analysis and distance-based redundancy analysis; the clusters were not only able to discriminate different species but also different cultivation regions. Similarity measurements were performed by calculating the correlation coefficient of each sample with the authentic samples. Hybrid rhizome was clearly identified through similarity measurement of UHPLC metabolite profile and later confirmed by gene sequencing. The present study demonstrated that CE and UHPLC are efficient and effective tools to identify and authenticate herbs even coupled with simple detectors.

Plasma metabolomic profiles predict near-term death among individuals with lower extremity peripheral arterial disease.

BACKGROUND: Individuals with peripheral arterial disease (PAD) have a nearly two-fold increased risk of all-cause and cardiovascular disease mortality compared to those without PAD. This pilot study determined whether metabolomic profiling can accurately identify patients with PAD who are at increased risk of near-term mortality. METHODS: We completed a case-control study using (1)H NMR metabolomic profiling of plasma from 20 decedents with PAD, without critical limb ischemia, who had blood drawn within 8 months prior to death (index blood draw) and within 10 to 28 months prior to death (preindex blood draw). Twenty-one PAD participants who survived more than 30 months after their index blood draw served as a control population. RESULTS: Results showed distinct metabolomic patterns between preindex decedent, index decedent, and survivor samples. The major chemical signals contributing to the differential pattern (between survivors and decedents) arose from the fatty acyl chain protons of lipoproteins and the choline head group protons of phospholipids. Using the top 40 chemical signals for which the intensity was most distinct between survivor and preindex decedent samples, classification models predicted near-term all-cause death with overall accuracy of 78% (32/41), a sensitivity of 85% (17/20), and a specificity of 71% (15/21). When comparing survivor with index decedent samples, the overall classification accuracy was optimal at 83% (34/41) with a sensitivity of 80% (16/20) and a specificity of 86% (18/21), using as few as the top 10 to 20 chemical signals. CONCLUSIONS: Our results suggest that metabolomic profiling of plasma may be useful for identifying PAD patients at increased risk for near-term death. Larger studies using more sensitive metabolomic techniques are needed to identify specific metabolic pathways associated with increased risk of near-term all-cause mortality among PAD patients.

MetaMOPE: a web service for mobile phase determination and fast chromatography peaks evaluation for metabolomics.

Motivation: Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used in metabolomics studies, while HILIC LC-MS is particularly suited for polar metabolites. Determining an optimized mobile phase and developing a proper liquid chromatography method tend to be laborious, time-consuming and empirical. Results: We developed a containerized web tool providing a workflow to quickly determine the optimized mobile phase by batch-evaluating chromatography peaks for metabolomics LC-MS studies. A mass chromatographic quality value, an asymmetric factor, and the local maximum intensity of the extracted ion chromatogram were calculated to determine the number of peaks and peak retention time. The optimal mobile phase can be quickly determined by selecting the mobile phase that produces the largest number of resolved peaks. Moreover, the workflow enables one to automatically process the repeats by evaluating chromatography peaks and determining the retention time of large standards. This workflow was validated with 20 chemical standards and successfully constructed a reference library of 571 metabolites for the HILIC LC-MS platform. Availability and implementation: MetaMOPE is freely available at https://metamope.cmdm.tw. Source code and installation instructions are available on GitHub: https://github.com/CMDM-Lab/MetaMOPE. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

An automated workflow on data processing (AutoDP) for semiquantitative analysis of urine organic acids with GC-MS to facilitate diagnosis of inborn errors of metabolism.

Determination of urine organic acids (UOAs) is essential to understand the disease progress of inborn errors of metabolism (IEM) and often relies on GC-MS analysis. However, the efficiency of analytical reports is sometimes restricted by data processing due to labor-intensive work if no proper tool is employed. Herein, we present a simple and rapid workflow with an R-based script for automated data processing (AutoDP) of GC-MS raw files to quantitatively analyze essential UOAs. AutoDP features automatic quality checks, compound identification and confirmation with specific fragment ions, retention time correction from analytical batches, and visualization of abnormal UOAs with age-matched references on chromatograms. Compared with manual processing, AutoDP greatly reduces analytical time and increases the number of identifications. Speeding up data processing is expected to shorten the waiting time for clinical diagnosis, which could greatly benefit clinicians and patients with IEM. In addition, with quantitative results obtained from AutoDP, it would be more feasible to perform retrospective analysis of specific UOAs in IEM and could provide new perspectives for studying IEM.

Metabolomic characterization of laborers exposed to welding fumes.

The complex composition of welding fumes, multiplicity of molecular targets, diverse cellular effects, and lifestyles associated with laborers vastly complicate the assessment of welding fume exposure. The urinary metabolomic profiles of 35 male welders and 16 male office workers at a Taiwanese shipyard were characterized via (1)H NMR spectroscopy and pattern recognition methods. Blood samples for the same 51 individuals were also collected, and the expression levels of the cytokines and other inflammatory markers were examined. This study dichotomized the welding exposure variable into high (welders) versus low (office workers) exposures to examine the differences of continuous outcome markers-metabolites and inflammatory markers-between the two groups. Fume particle assessments showed that welders were exposed to different concentrations of chromium, nickel, and manganese particles. Multivariate statistical analysis of urinary metabolomic patterns showed higher levels of glycine, taurine, betaine/TMAO, serine, S-sulfocysteine, hippurate, gluconate, creatinine, and acetone and lower levels of creatine among welders, while only TNF-α was significantly associated with welding fume exposure among all cytokines and other inflammatory markers measured. Of the identified metabolites, the higher levels of glycine, taurine, and betaine among welders were suspected to play some roles in modulating inflammatory and oxidative tissue injury processes. In this metabolomics experiment, we also discovered that the association of the identified metabolites with welding exposure was confounded by smoking, but not with drinking, which is a finding consistent with known modified response of inflammatory markers among smokers. Our results correspond with prior studies that utilized nonmetabolomic analytical techniques and suggest that the metabolomic profiling is an efficient method to characterize the overall effect of welding fume exposure and other confounders.

Thermodynamics analysis and experiments on Ti-bearing blast furnace slag leaching enhanced by sulfuric acid roasting.

The potential-pH diagrams of the main components of Ti-bearing blast furnace slag (air-cooled slag) at 298.15 K (25 °C) and an ion activity of 1.00 were drawn by thermodynamic calculation. Thermodynamic analysis showed that the main metal components, when the Ti-bearing blast furnace slag is roasted with concentrated sulfuric acid, could be converted to sulfate. From these analyses, it can be seen that under strong acid conditions, the major metal components could react to form sulfate, and the effective separation of Ti, Mg, and Al can be achieved from both Ca and Si. Further experiments were performed with a 5.0% dilute sulfuric acid solution used to leach a Ti-bearing blast furnace slag sample that had been calcined with concentrated sulfuric acid, at a liquid-solid ratio of 10, a reaction time of 60 min, and a reaction temperature of 338.15 K (65 °C). This led to a leaching ratio of Ti above 85.0%, leaching ratios of Mg and Al higher than 95.0%, and leaching ratios of Fe and Ca of 45.7% and 24.7%, respectively. All these values were higher than the leaching ratios of Ti-bearing blast furnace slag.

Development of an analytical method to detect simultaneously 219 new psychoactive substances and 65 other substances in urine specimens using LC-QqQ MS/MS with CriticalPairFinder and TransitionFinder.

Emerging new psychoactive substances (NPS) poses a great risk to public health. Analyzing these large numbers of NPS and other associated substances often relies on liquid chromatography coupled to triple quadrupole mass spectrometry (LC-QqQ-MS) with multiple-reaction monitoring (MRM) mode. However, the differentiation of critical pairs, coeluted isobaric and/or isomeric species, is one of the challenges for this analytical platform. MRM transitions with poor selectivity can jeopardize accurate quantification and lead to biased interpretation. Herein, we refined a novel workflow for developing an MRM-based method with in-house CriticalPairFinder and TransitionFinder tools for the effective identification of unique and selective MRM transitions. Transitions selected by TransitionFinder showed much better accuracies than those selected only by fragment abundance in some mixtures of critical pairs. Using the proposed analytical strategy, a method that can simultaneously determine 219 NPS and 65 other substances across a variety of NPS classes in urine samples was developed, validated and applied to analyze clinical urine samples. This automated workflow is anticipated to facilitate method development for analyzing complex analytes while considering selectivity.