Untargeted metabolomics analysis assisted by signal selection for comprehensively identifying metabolites of new psychoactive substances: 4-MeO-α-PVP as an example.

New psychoactive substances (NPS) have been rapidly emerged as legal alternatives to controlled drugs, which raised severe public health issue. The detection and monitoring of its intake by complete metabolic profiling is an urgent and vital task. Untargeted metabolomics approach has been applied for several NPS metabolites studies. Although the number of such works is relatively limited but with a rapidly growing need. The present study aimed to propose a procedure that includes liquid chromatography high-resolution mass spectrometry (LC-HRMS) analysis and a signal selection software, MetaboFinder, programed as a web tool. The comprehensive metabolites profile of one kind of NPS, 4-methoxy-α-pyrrolidinovalerophenone (4-MeO-α-PVP), was studied by using this workflow. In this study, two different concentrations of 4-MeO-α-PVP along with as blank sample were incubated with human liver S9 fraction for the conversion to their metabolites and followed by LC-MS analysis. After retention time alignment and feature identification, 4640 features were obtained and submitted to statistical analysis for signal selection by using MetaboFinder. A total of 50 features were considered as 4-MeO-α-PVP metabolite candidates showing significant changes (p < 0.00001 and fold change >2) between the two investigated groups. Targeted LC-MS/MS analysis was conducted focusing on these significantly expressed features. Assisted by chemical formula determination according to high mass accuracy and in silico MS2 fragmentation prediction, 19 chemical structure identifications were achieved. Among which, 8 metabolites have been reported derived from 4-MeO-α-PVP in a previous literature while 11 novel 4-MeO-α-PVP metabolites were identified by using our strategy. Further in vivo animal experiment confirmed that 18 compounds were 4-MeO-α-PVP metabolites, which demonstrated the feasibility of our strategy for screening the 4-MeO-α-PVP metabolites. We anticipate that this procedure may support and facilitate traditional metabolism studies and potentially being applied for routine NPS metabolites screening.

Discovery of Spoilage Markers for Chicken Eggs Using Liquid Chromatography-High Resolution Mass Spectrometry-Based Untargeted and Targeted Foodomics.

The current approaches remain insufficient for measuring chicken egg spoilage or present analytical limitations. This study aimed to complement the existing analyses and identify novel markers using liquid chromatography-high resolution mass spectrometry-based foodomics strategies. In the discovery set, comparative untargeted metabolomics was utilized to identify marker candidates in microbially inoculated chicken eggs. Markers were annotated by spectral matching with authentic standards, experimental libraries, or in silico fragmentation. In the validation set, targeted metabolomics was employed to verify the markers in stored chicken eggs from five farms. Statistical differences at a p-value < 0.001 revealed increases in lactic and 3-hydroxybutyric acids and decreases in phosphocholine, LPE(O-18:1), LPC(16:0), and LPC(18:0) in stored eggs. Receiver operating characteristic curve analysis of the six combined markers yielded an AUC of 0.956 and a sensitivity and specificity of ∼90%. Four phospholipids were highlighted as a novel class of spoilage markers. Our findings may contribute to further industrial implementation, benefiting the quality assurance and food safety of poultry egg production.

Assessment of Serological Early Biomarker Candidates for Lung Adenocarcinoma by using Multiple Reaction Monitoring-Mass Spectrometry.

PURPOSE: Plasma markers that enable diagnosis in the early stage of lung cancer is not discovered. A liquid chromatography multiple reaction monitoring-mass spectrometry (LC-MRM-MS) assay for identifying potential early marker proteins for lung adenocarcinoma is developed. EXPERIMENTAL DESIGN: LC-MRM-MS assay is used for measuring the level of 35 candidate peptides in plasma from 102 lung adenocarcinoma patients (including n = 50, 16, 24, and 12 in stage I, II, III, and IV, respectively.) and 84 healthy controls. Stable isotope labeled standard peptides are synthesized to accurately measure the amount of these proteins. RESULTS: Seven proteins are able to distinguish stage I patients from controls. These proteins are combined in to a protein marker panel which improve the sensitivity to discriminate stage I patients from controls with cross-validated area under the curve = 0.76. Besides, it is found that low expression of eukaryotic initiation factor 4A-I and high expression of lumican show significantly poor prognosis in overall survival (p = 0.012 and 0.0074, respectively), which may be used as prognostic biomarkers for lung cancer. CONCLUSIONS AND CLINICAL RELEVANCE: Proteins highlighted here may be used for early detection of lung adenocarcinoma or therapeutics development after validation in a larger cohort.

Assessing the Similarity between Random Copolymer Drug Glatiramer Acetate by Using LC-MS Data Coupling with Hypothesis Testing.

The full characterization of nonbiological complex drugs (NBCDs) is not possible, but analytical approaches are of urgent need to evaluate the similarity between different lots and compare with their follow-up versions. Here, we propose a hypothesis testing-based approach to assess the similarity/difference between random amino acid copolymer drugs using liquid chromatography mass spectrometry (LC-MS) analysis. Two glatiramer acetate (GA) drugs, commercially available Copaxone and in-house synthesized SPT, and a negative control were digested by Lys-C and followed by HILIC-MS analysis. After retention time alignment and feature identification, 1627 features matched to m/z values in an elemental composition database were considered as derived from active drug ingredients. A hypothesis testing approach, the sum of squared deviations test, was developed to process high-dimensional data derived from LC-MS spectra. The feasibility of this approach was first demonstrated by testing 5 versus 5 lots of Copaxone and Copaxone versus SPT, which suggested a significant similarity by obtaining the estimated 95th percentile of the distribution of the estimator (ρ̂(95%)) at 0.0056 (p-value = 0.0026) and 0.0026 (p-value < 0.0001), respectively. In contrast, the ρ̂ was 0.036 (p-value = 1.00), while comparing Copaxone and the negative control, implying a lack of similarity. We further synthesized nine stable isotope-labeled peptides to validate the proposed amino acid sequences in the database, demonstrating the correctness in sequence identification. The quantitation variations in our analytical procedures were determined to be 6.8-7.7%. This approach was found to have a great potential for evaluating the similarity between generic NBCDs and listed reference drugs, as well as to monitor the lot-to-lot variation.

Electrochemical Reflective Absorption Microscopy for Probing the Local Diffusion Behavior in the Electrochemical Interface.

Electrochemical interfaces determine the performance of electrochemical devices, including energy-related systems. An in-depth understanding of the heterogeneous interfaces requires in situ techniques with high sensitivity and high temporal and spatial resolution. We develop here an electrochemical reflective absorption microscope (EC-RAM) by using the absorption signals of reacting species with a reasonably good spatial resolution and high sensitivity. We systematically study the response of absorbance ( A) and its derivative, i.e. d A/d t, at different positions of the electrode surface and at electrodes with different sizes (50 µm, 500 µm, and 2 mm) both experimentally and theoretically. We find that the derivative cyclic voltabsorptometry (DCVA) frequently used to obtain the local current response in conventional electrochemical optical microscopy techniques is only applicable to reactions of surface species or solution species under linear diffusion control. For processes when the radial diffusion cannot be ignored, as in the case of a microelectrode or the edge of a large electrode, the DCVA curves show distinct diffusion behaviors for the electroactive species in different regions of the electrode, which cannot be directly related to the CV curves. When the radial diffusion dominates the reaction, CVA curves follow the same shape as the CV curves. The developed EC-RAM technique can be applied to extract in situ the local response of an electrochemical system during the dynamic reaction processes.