Characterizing the D-Amino Acid Position in Peptide Epimers by Using Higher-Energy Collisional Dissociation Tandem Mass Spectrometry: A Case Study of Liraglutide.

D-amino acid-containing peptides (DAACPs) occur in biological and artificial environments. Since the importance of DAACPs has been recognized, various mass spectrometry-based analytical approaches have been developed. However, the capability of higher-energy collisional dissociation (HCD) fragmentation to characterize DAACP sites has not been evaluated. In this study, we compared the normalized spectra intensity under different conditions of HCD and used liraglutide along with its DAACPs as examples. Our results indicated that the difference in the intensity of y ions between DAACPs and all-L liraglutide could not only distinguish them but also localize the sites of D-amino acids in the DAACPs. Our data demonstrate the potential of using HCD for the site characterization of DAACPs, which may have great impact in biological studies and peptide drug development.

Characterization of Hair Metabolome in 5xFAD Mice and Patients with Alzheimer's Disease Using Mass Spectrometry-Based Metabolomics.

Hair emerged as a biospecimen for long-term investigation of endogenous metabolic perturbations, reflecting the chemical composition circulating in the blood over the past months. Despite its potential, the use of human hair for metabolomics in Alzheimer's disease (AD) research remains limited. Here, we performed both untargeted and targeted metabolomic approaches to profile the key metabolic pathways in the hair of 5xFAD mice, a widely used AD mouse model. Furthermore, we applied the discovered metabolites to human subjects. Hair samples were collected from 6-month-old 5xFAD mice, a stage marked by widespread accumulation of amyloid plaques in the brain, followed by sample preparation and high-resolution mass spectrometry analysis. Forty-five discriminatory metabolites were discovered in the hair of 6-month-old 5xFAD mice compared to wild-type control mice. Enrichment analysis revealed three key metabolic pathways: arachidonic acid metabolism, sphingolipid metabolism, and alanine, aspartate, and glutamate metabolism. Among these pathways, six metabolites demonstrated significant differences in the hair of 2-month-old 5xFAD mice, a stage prior to the onset of amyloid plaque deposition. These findings suggest their potential involvement in the early stages of AD pathogenesis. When evaluating 45 discriminatory metabolites for distinguishing patients with AD from nondemented controls, a combination of l-valine and arachidonic acid significantly differentiated these two groups, achieving a 0.88 area under the curve. Taken together, these findings highlight the potential of hair metabolomics in identifying disease-specific metabolic alterations and developing biomarkers for improving disease detection and monitoring.

Utilizing fish wastewater in aquaponic systems to enhance anti-inflammatory and antioxidant bioactive compounds in Sarcodia suae.

Aquaculture wastewater, rich in organic nutrients, is an essential environmental factor. When applied to seaweed cultivation systems, this wastewater holds the potential to notably increase the growth rate and carbon capture of Sarcodia suae. Sarcodia suae has the potential to be a healthy food due to its various biological activities; however, its chemical composition has yet to be completely defined. In this study, we applied a UHPLC-HRMS-based foodomics strategy to determine and classify possible bioactive metabolites in S. suae. From pooled seaweed samples (S. suae cultured in filtered running, FR, aquaponic recirculation, AR systems), we identified 179 and 146 compounds in POS and NEG modes, respectively. These compounds were then classified based on their structures using the Classyfire classification. Results show that S. suae in AR exhibited higher growth performance, and ten upregulated metabolites were determined. We also validated the anti-inflammatory and antioxidative bioactivities of some selected compounds. Our study provided important insights into the potential use of fish wastewater in aquaponic systems to profile and produce bioactive compounds in S. suae comprehensively. This has significant implications for the development of sustainable food and the promotion of environmental health.

Identification of Xenobiotic Biotransformation Products Using Mass Spectrometry-Based Metabolomics Integrated with a Structural Elucidation Strategy by Assembling Fragment Signatures.

The identification of xenobiotic biotransformation products is crucial for delineating toxicity and carcinogenicity that might be caused by xenobiotic exposures and for establishing monitoring systems for public health. However, the lack of available reference standards and spectral data leads to the generation of multiple candidate structures during identification and reduces the confidence in identification. Here, a UHPLC-HRMS-based metabolomics strategy integrated with a metabolite structure elucidation approach, namely, FragAssembler, was proposed to reduce the number of false-positive structure candidates. biotransformation product candidates were filtered by mass defect filtering (MDF) and multiple-group comparison. FragAssembler assembled fragment signatures from the MS/MS spectra and generated the modified moieties corresponding to the identified biotransformation products. The feasibility of this approach was demonstrated by the three biotransformation products of di(2-ethylhexyl)phthalate (DEHP). Comprehensive identification was carried out, and 24 and 13 biotransformation products of two xenobiotics, DEHP and 4'-Methoxy-α-pyrrolidinopentiophenone (4-MeO-α-PVP), were annotated, respectively. The number of 4-MeO-α-PVP biotransformation product candidates in the FragAssembler calculation results was approximately 2.1 times lower than that generated by BioTransformer 3.0. Our study indicates that the proposed approach has great potential for efficiently and reliably identifying xenobiotic biotransformation products, which is attributed to the fact that FragAssembler eliminates false-positive reactions and chemical structures and distinguishes modified moieties on isomeric biotransformation products. The FragAssembler software and associated tutorial are freely available at https://cosbi.ee.ncku.edu.tw/FragAssembler/ and the source code can be found at https://github.com/YuanChihChen/FragAssembler.

Health risk assessment of exposure to various vapors and fumes in a factory of automobile manufacturing.

This study aimed to comprehensively evaluate the health risk of exposure to various vapors and fumes in a factory of automobile manufacturing. This study was performed in 2021 on 115 workers. Vapors and fumes were gathered by the adsorbent tubes of activated charcoal and mixed cellulose esters (MCE) membrane filter, respectively. The flow rate for vapors and fumes were between 0.05 and 0.20 L per min and 1 to 4 L per min, respectively. After preparing, samples were analyzed. To assess the non-cancer and cancer risk of the pollutants, the method proposed environmental protection agency (EPA) was applied. The total concentration of copper (1.031 ppm), manganese (0.114), and 2-butoxyethanol (91.767 ppm) were found to be higher than The threshold limit values (TLVs). The values of non-cancer risk (HQ) due to exposure to vapors of benzene (6.583), toluene (1.396), ethyl benzene (1.212), xylene (31.148), 2-butoxyethanol (89.302), 2-propanol (4.695), 1,2,3-trimethylbenzene (1.923), copper (2.336), manganese (715.82), aluminum (3.772), and chromium (107.066) were higher than the acceptable limit. Moreover, the estimated LCR for benzene (2.15 × 10-4), ethyl benzene (3.97 × 10-4), vinyl chloride (1.25 × 10-4), and chromium (2.11 × 10-2) were higher than the threshold risk level set by EPA. It is emphasized that preventive measures are performed.

Investigation of New Psychoactive Substances (NPS), Other Illicit Drugs, and Drug-Related Compounds in a Taiwanese Wastewater Sample Using High-Resolution Mass-Spectrometry-Based Targeted and Suspect Screening.

The proliferation of new psychoactive substances (NPSs) in recent years has posed a significant challenge to public health. Traditional monitoring methods have proven insufficient in tracking these constantly evolving substances, leading to the development of alternative approaches such as wastewater-based epidemiology (WBE). The present study aims to utilize high-resolution mass spectrometry (HRMS)-based targeted and suspect screening to profile NPS, other illicit drugs, and drug-related compounds in a Taiwanese wastewater sample. For the targeted analysis, 8 out 18 standards of illicit drugs have been identified. The suspect screening approach based on approximately 3600 substances in the SWGDRUG library can further identify 92 compounds, including opiate analgesics, synthetic cathinones, phenylalkylamines derivatives, phenethylamine derivatives, tryptamine derivatives, steroids, and ephedrine-related compounds. Additionally, the presence of 5-methoxy-2-aminoindane (MEAI) in the wastewater indicates that drug dealers have recently sold this potential NPS to evade drug regulations. This study firstly reports the HRMS-based comprehensive profile of NPS, other illicit drugs, and drug-related compounds in Taiwan, which could be applied as biomarkers for estimating the consumption of drugs.

Development of a lateral flow immunoassays-based method for the screening of ractopamine in foods and evaluation of the optimal strategy in combination of screening and confirmatory tests.

Ractopamine has been authorized as a feed additive and permitted in animal husbandry. With the establishment of the regulation to limit the concentration of ractopamine, a rapid screening method for ractopamine is urgently needed. Additionally, how to combine the screening and confirmatory tests of ractopamine is also critical to maximizing the efficiency of testing. Here, we developed a lateral flow immunoassays-based method for the screening of ractopamine in foods and proposed a cost-benefit analysis approach to optimize cost allocation between screening and confirmatory tests. After verifying the analytical and clinical performances of the screening method, a mathematical model was established to calculate the screening and confirmatory test results with various parameter settings, such as cost allocation, false-negative tolerance, and total budget size. The developed immunoassay-based screening test could successfully distinguish gravy samples with ractopamine levels over and lower than maximum residue limits (MRL). The area under curve (AUC) value of receiver operating characteristic (ROC) curve is 0.99. For the cost-benefit analysis, mathematical simulation indicated that when the samples are allocated to screening and confirmatory tests at the optimized cost allocation, the number of confirmed positive samples can increase by 26 times compared to the scenarios entirely relying on confirmatory testing. While conventional wisdom considers that screening should be carried out at low false-negative rates, such as 0.1%, our results indicated that the cutoff value of a screening test with a 20% false-negative rate at MRL could capture the maximum number of confirmed positive samples at a limited budget. Our work indicated that the participation of the screening method in ractopamine analysis and optimized cost allocation between screening and confirmatory tests could enhance the efficiency in detecting the positive samples, which provides a rational basis for decision-making in food safety enforcement for public health.

Detection of emerging patterns of drug misuse in sports via wastewater monitoring: A mini-review and potential strategies.

Biological testing is a key component of the current anti-doping programme implemented by the authorities to detect doping in sports. Strategies such as longitudinal individualised data analysis and sport-specific analysis have been developed to increase the comprehensiveness of the testing. However, the trends of drug misuse in sports might not be effectively captured through today's testing plan. Wastewater testing, assembling individual-level data of a designated group to produce population-level results in one single aggregated sample, can be employed to as a complementary strategy offering added value for doping control. This paper presents an updated summary of the status of anti-doping testing and analytical methodologies for wastewater. The available literature on wastewater-based analyses of drugs prohibited in sports is reviewed. Publications surrounding sporting activities or competitions and others relevant to sports doping are selected. We debate between potential strategies and major limitations of using wastewater monitoring in anti-doping. Knowledge gaps and research directions, specifically on metabolites, stability, sensitivity, and ethical and legal considerations, are discussed. Choosing different wastewater sampling sites allows target sub-population that involved competing athletes and potentially reveal sport-specific or athlete-level-specific behaviour. Sampling from on-board toilets or athlete villages could target international-level athletes, sampling from the dormitories of national training centres allows monitoring of national-level athletes on a daily basis, and sampling from sports stadiums provides a full picture of drug use in the general population during an event. Confounding occurs as (i) the presence of non-athlete composition and the difficulty of analyses to be completely selective to the athlete population; and (ii) the identification of compounds prescribed legitimately with Therapeutic Use Exemptions, only banned in-competition, and naturally occurring. The practicalities of the approach are contextualised in monitoring the non-threshold substances such as anabolic agents, selective androgen receptor modulators, metabolic modulators, and hypoxia-inducible factor activators.

Monitoring long-term chemical exposome by characterizing the hair metabolome using a high-resolution mass spectrometry-based suspect screening approach.

Hair has recently emerged as a biospecimen for characterizing the long-term chemical exposome in biomonitoring investigations spanning several months, as chemical compounds circulating in the bloodstream accumulate in hair. Although there has been interest in using human hair as a biospecimen for exposome studies, it has yet to be widely adopted compared to blood and urine. Here, we applied a high-resolution mass spectrometry (HRMS)-based suspect screening strategy to characterize the long-term chemical exposome in human hair. Hair samples were collected from 70 subjects and cut into 3 cm segments, which were then mixed to prepare pooled samples. The pooled hair samples underwent a sample preparation procedure, and the hair extracts were further analyzed using an HRMS-based suspect screening approach. An in-house chemical suspect list containing 1227 chemical entries from National Report on Human Exposure to Environmental Chemicals (Report) published by the U.S. CDC and the Exposome-Explorer 3.0 database developed by the WHO was subsequently used to screen and filter the suspect features against the HRMS dataset. Overall, we matched 587 suspect features in the HRMS dataset to 246 unique chemical formulas in the suspect list, and the structures of 167 chemicals were further identified through a fragmentation analysis. Among these, chemicals such as mono-2-ethylhexyl phthalate, methyl paraben, and 1-naphthol, which have been detected in the urine or blood for exposure assessment, were also identified in human hair. This suggests that hair reflects the accumulation of environmental compounds to which an individual is exposed. Exposure to exogenous chemicals may exert adverse effects on cognitive function, and we discovered 15 chemicals in human hair that may contribute to the pathogenesis of Alzheimer's disease. This finding suggests that human hair may be a promising biospecimen for monitoring long-term exposure to multiple environmental chemicals and perturbations in endogenous chemicals in biomonitoring investigations.

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.

Rat Hair Metabolomics Analysis Reveals Perturbations of Unsaturated Fatty Acid Biosynthesis, Phenylalanine, and Arachidonic Acid Metabolism Pathways Are Associated with Amyloid-ß-Induced Cognitive Deficits.

Hair is a noninvasive valuable biospecimen for the long-term assessment of endogenous metabolic disturbance. Whether the hair is suitable for identifying biomarkers of the Alzheimer's disease (AD) process remains unknown. We aim to investigate the metabolism changes in hair after ß-amyloid (Aß1-42) exposure in rats using ultra-high-performance liquid chromatography-high-resolution mass spectrometry-based untargeted and targeted methods. Thirty-five days after Aß1-42 induction, rats displayed significant cognitive deficits, and forty metabolites were changed, of which twenty belonged to three perturbed pathways: (1) phenylalanine metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis-L-phenylalanine, phenylpyruvate, ortho-hydroxyphenylacetic acid, and phenyllactic acid are up-regulated; (2) arachidonic acid (ARA) metabolism-leukotriene B4 (LTB4), arachidonyl carnitine, and 5(S)-HPETE are upregulation, but ARA, 14,15-DiHETrE, 5(S)-HETE, and PGB2 are opposite; and (3) unsaturated fatty acid biosynthesis- eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), FA 18:3 + 1O, and FA 18:3 + 2O are downregulated. Linoleic acid metabolism belonging to the biosynthesis of unsaturated fatty acid includes the upregulation of 8-hydroxy-9,10-epoxystearic acid, 13-oxoODE, and FA 18:2 + 4O, and downregulation of 9(S)-HPODE and dihomo-γ-linolenic acid. In addition, cortisone and dehydroepiandrosterone belonging to steroid hormone biosynthesis are upregulated. These three perturbed metabolic pathways also correlate with cognitive impairment after Aß1-42 stimulation. Furthermore, ARA, DHA, EPA, L-phenylalanine, and cortisone have been previously implicated in the cerebrospinal fluid of AD patients and show a similar changing trend in Aß1-42 rats' hair. These data suggest hair can be a useful biospecimen that well reflects the expression of non-polar molecules under Aß1-42 stimulation, and the five metabolites have the potential to serve as novel AD biomarkers.

Identifying Hair Biomarker Candidates for Alzheimer's Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies.

High-resolution mass spectrometry (HRMS)-based untargeted metabolomics strategies have emerged as an effective tool for discovering biomarkers of Alzheimer's disease (AD). There are various HRMS-based untargeted metabolomics strategies for biomarker discovery, including the data-dependent acquisition (DDA) method, the combination of full scan and target MS/MS, and the all ion fragmentation (AIF) method. Hair has emerged as a potential biospecimen for biomarker discovery in clinical research since it might reflect the circulating metabolic profiles over several months, while the analytical performances of the different data acquisition methods for hair biomarker discovery have been rarely investigated. Here, the analytical performances of three data acquisition methods in HRMS-based untargeted metabolomics for hair biomarker discovery were evaluated. The human hair samples from AD patients (N = 23) and cognitively normal individuals (N = 23) were used as an example. The most significant number of discriminatory features was acquired using the full scan (407), which is approximately 10-fold higher than that using the DDA strategy (41) and 11% higher than that using the AIF strategy (366). Only 66% of discriminatory chemicals discovered in the DDA strategy were discriminatory features in the full scan dataset. Moreover, compared to the deconvoluted MS/MS spectra with coeluted and background ions from the AIF method, the MS/MS spectrum obtained from the targeted MS/MS approach is cleaner and purer. Therefore, an untargeted metabolomics strategy combining the full scan with the targeted MS/MS method could obtain most discriminatory features along with a high quality MS/MS spectrum for discovering the AD biomarkers.

Discovering Hair Biomarkers of Alzheimer's Disease Using High Resolution Mass Spectrometry-Based Untargeted Metabolomics.

Hair may be a potential biospecimen to discover biomarkers for Alzheimer's disease (AD) since it reflects the integral metabolic profiles of body burden over several months. Here, we described the AD biomarker discovery in the hair using a high-resolution mass spectrometry (HRMS)-based untargeted metabolomics approach. A total of 24 patients with AD and 24 age- and sex-matched cognitively healthy controls were recruited. The hair samples were collected 0.1-cm away from the scalp and further cut into 3-cm segments. Hair metabolites were extracted by ultrasonication with methanol/phosphate-buffered saline 50/50 (v/v) for 4 h. A total of 25 discriminatory chemicals in hair between the patients with AD and controls were discovered and identified. The AUC value achieved 0.85 (95% CI: 0.72~0.97) in patients with very mild AD compared to healthy controls using a composite panel of the 9 biomarker candidates, indicating high potential for the initiation or promotion phase of AD dementia in the early stage. A metabolic panel combined with the nine metabolites may be used as biomarkers for the early detection of AD. The hair metabolome can be used to reveal metabolic perturbations for biomarker discovery. Investigating perturbations of the metabolites will offer insight into the pathogenesis of AD.

Connecting chemical exposome to human health using high-resolution mass spectrometry-based biomonitoring: Recent advances and future perspectives.

Compared with the rapid advances in genomics leading to broad understanding of human disease, the linkage between chemical exposome and diseases is still under investigation. High-resolution mass spectrometry (HRMS) is expected to accelerate the process via relatively accurate and precise biomonitoring of human exposome. This review covers recent advancements in biomonitoring of exposed environmental chemicals (chemical exposome) using HRMS described in the 124 articles that resulted from a systematic literature search on Medline and Web of Science databases. The analytical strategic aspects, including the selection of specimens, sample preparation, instrumentation, untargeted versus targeted analysis, and workflows for MS-based biomonitoring to explore the environmental chemical space of human exposome, are deliberated. Applications of HRMS in human exposome investigation are presented by biomonitoring (1) exposed chemical compounds and their biotransformation products; (2) DNA/protein adducts; and (3) endogenous compound perturbations. Challenges and future perspectives are also discussed.

Exploration of long-term exposure markers for phthalate esters in human hair using liquid chromatography-tandem mass spectrometry.

Phthalate esters are a group of synthetic industrial chemicals that are widely used in plastics. Urinary phthalate metabolites are short-term exposure markers frequently used to represent exposure levels in environmental epidemiology studies. Human hair is an alternative matrix for recording long-term exposure, but there are still analytical challenges that need to be addressed. In this study, an analytical method was established for simultaneously measuring nine major phthalate metabolites in human hair and successfully applied to measure phthalate metabolites in 30 hair samples collected from 30 individual human volunteers without known occupational exposure to phthalates. Two portions of 25 mg of hair samples were extracted by acidified methanol and water in 240 min of ultrasonication and then analyzed using a liquid chromatography-tandem mass spectrometry system. The limit of quantification ranged from 0.72 to 10.7 ng/g hair for nine phthalate metabolites. All nine phthalate metabolites were detected in more than 70% of the 30 individual human hair samples. The measured levels of hair phthalate metabolites were (in descending order): MEHP > MMP â‰« MEP > MBP (MnBP + MiBP) > MiNP > MEHHP ≈ MEOHP ≈ MECPP. The primary metabolite, MEHP (692 ± 582 ng/g), is the major DEHP metabolite in hair. This result is consistent with the findings in blood but not in urine, in which the secondary metabolites are the major DEHP metabolites. This method is easy to foresee with a clinical application and applies to human biomonitoring studies to assess long-term environmental phthalate exposure.

Post-Deconvolution MS/MS Spectra Extraction with Data-Independent Acquisition for Comprehensive Profiling of Urinary Glucuronide-Conjugated Metabolome.

Conjugation reactions are of critical significance in human metabolism. Identification of these conjugated metabolites is still challenging. Here, we propose a strategy, post-deconvolution MS/MS spectra extraction with data-independent acquisition (PDMS2E-DIA), to comprehensively profile the glucuronide-conjugated metabolome. PDMS2E-DIA enables the identification of conjugated and unconjugated metabolite pairs through neutral loss filtering combined with a significant change in abundance after the deconjugation reaction. Purified DIA MS/MS spectra were constructed by extracting MS/MS fragments shared between spectra derived from conjugated and unconjugated metabolites. The feasibility of this approach was first demonstrated by the identification of two glucuronide-conjugated metabolite standards spiked in urine samples. For human urine samples, 479 features were structurally annotated as potential glucuronide-conjugated metabolites, resulting in the identification of 211 metabolites. Fragment peaks derived from interferents were found to be removed by PDMS2E-DIA, which increased about 6 times the number of identified urine metabolites compared with those calculated from raw DIA deconvoluted MS/MS spectra. This approach was found to have great potential for identifying glucuronide-conjugated metabolites, as well as other kinds of chemical conjugations.

LCMD: Lung Cancer Metabolome Database.

Lung cancer, one of the most common causes of cancer-related death worldwide, has been associated with high treatment cost and imposed great burdens. The 5-year postoperative survival rate of lung cancer (13%) is lower than many other leading cancers indicating the urgent needs to dissect its pathogenic mechanisms and discover specific biomarkers. Although several proteins have been proposed to be potential candidates for the diagnosis of lung cancer, they present low accuracy in clinical settings. Metabolomics has thus emerged as a very promising tool for biomarker discovery. To date, many lung cancer-related metabolites have been highlighted in the literature but no database is available for scientists to retrieve this information. Herein, we construct and introduce the first Lung Cancer Metabolome Database (LCMD), a freely available online database depositing 2013 lung cancer-related metabolites identified from 65 mass spectrometry-based lung cancer metabolomics studies. Researchers are able to explore LCMD via two ways. Firstly, by applying various filters in the "Browse Metabolites" mode, users can access a list of lung cancer-related metabolites that satisfy the filter specifications. For each metabolite, users can acquire the value of the fold change (cancer/normal), statistical significance (p-value) of the fold change, and the comparative research designs of all the mass spectrometry-based lung cancer metabolomics studies that identify this metabolite. Secondly, by applying various filters in the "Browse Studies" mode, users can obtain a list of mass spectrometry-based lung cancer metabolomics studies that satisfy the filter specifications. For each study, users can view the type of studied specimen, mass spectrometry (MS) method, MS data processing software, and differential analysis method, as well as all the identified lung cancer-related metabolites. Furthermore, the overview of each study is clearly illustrated by a graphical summary. The LCMD (http://cosbi7.ee.ncku.edu.tw/LCMD/) is the first database that brings together the meaningful information of lung cancer-related metabolites. The development of the LCMD is envisioned to promote the biomarker discovery of lung cancer.

Proteomic Signature of Extracellular Vesicles for Lung Cancer Recognition.

The proteins of extracellular vesicles (EVs) that originate from tumors reflect the producer cells' proteomes and can be detected in biological fluids. Thus, EVs provide proteomic signatures that are of great interest for screening and predictive cancer diagnostics. By applying targeted mass spectrometry with stable isotope-labeled peptide standards, we assessed the levels of 28 EV-associated proteins, including the conventional exosome markers CD9, CD63, CD81, CD82, and HSPA8, in vesicles derived from the lung cancer cell lines NCI-H23 and A549. Furthermore, we evaluated the detectability of these proteins and their abundance in plasma samples from 34 lung cancer patients and 23 healthy volunteers. The abundance of TLN1, TUBA4A, HSPA8, ITGB3, TSG101, and PACSIN2 in the plasma of lung cancer patients was measured using targeted mass spectrometry and compared to that in plasma from healthy volunteers. The most diagnostically potent markers were TLN1 (AUC, 0.95), TUBA4A (AUC, 0.91), and HSPA8 (AUC, 0.88). The obtained EV proteomic signature allowed us to distinguish between the lung adenocarcinoma and squamous cell carcinoma histological types. The proteomic cargo of the extracellular vesicles represents a promising source of potential biomarkers.

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.

Extraction strategies for tackling complete hair metabolome using LC-HRMS-based analysis.

Over recent years, metabolomics has been featured as the state-of-the-art technology that successfully opens the paths to understanding biological mechanisms and facilitating biomarker discovery. However, the inherent dynamic and sensitive nature of the metabolome have been challenging the accuracy of capturing the timepoints of interest while using biofluids such as urine and blood. Hair has thus emerged as a valuable analytical specimen for the long-term and retrospective determinations. Unfortunately, notwithstanding the apparent interest on global hair metabolomics, very few studies have engaged in the optimisation of the extraction strategy. In this study, we systemically investigated the extraction procedures for hair metabolome using a single factor experimental design. Three pH values (acidic, neutral, and basic) in aqueous solution, six extraction solvents (methanol, acetonitrile, acetone, phosphate-buffered saline, deionised water, and dichloromethane), different compositions of selected solvent mixtures and their sequential extraction, and a series of extraction times (15, 45, 60, 120, 240, and 480 min) were evaluated. The ideal condition for hair extraction is ultrasonic-assisted extraction with methanol:phosphate-buffered saline 50:50 (v/v) under +55 °C for 240 min. This strategy may secure the true composition of the metabolome, maximise the signal abundance, and guarantee a high coverage of wide-range metabolites in a straightforward approach. The optimised extraction strategy was then coupled with structure annotation tools for hair metabolome profiling. After a single RPLC-HRMS run, hair metabolite identification was achieved as the annotations of 171 probable structures and 853 tentative structures as well as the assignments of 414 unequivocal molecular formulae. In conclusion, we established an efficient extraction strategy for untargeted hair metabolomics, which the method is deliverable to any analytical laboratories and the sample can be directly profiled by means of a conventional RPLC-HRMS gradient.