Data-dependent Acquisition Method Research Articles

Bioinformatics-driven untargeted metabolomic profiling for clinical screening of methamphetamine abuse.

Amphetamine-type stimulants are very common, and their usage is becoming a very big social problem all over the world. Thousands of addicts encounter several health problems including mental, metabolic, behavioral and neurological disorders. In addition to these, there are several reports about the elevated risk of tendency on committing criminal cases by addicted persons. Hence, methamphetamine addiction is not only an individual health problem but also a social problem. In our study, we aimed to investigate the pathogenesis of chronic usage of methamphetamine via untargeted metabolomics approach. 38 plasma samples were carefully collected and extracted for untargeted metabolomics assay. A liquid-liquid extraction was performed to get as much metabolite as possible from the samples. After the extraction procedure, samples were transferred into vials and they were evaluated via time of flight mass spectrometry instrument. Significantly, altered metabolites were identified by the fold analysis and Welch's test between the groups. 42 different compounds were annotated regarding to data-dependent acquisition method. Pathway analysis were also performed to understand the hazardous effect of methamphetamine on human body. It has been reported that drug exposure may affect several metabolic pathways for amino acids, fats, energy metabolism and vitamins. An alternative bioinformatic model was also developed and validated in order to predict the chronic methamphetamine drug users in any criminal cases. This generated model passes the ROC curve analysis and permutation test and classify the controls and drug users correctly by evaluating the metabolic alterations between the groups.

In-Depth Host Cell Protein Analysis and Viral Protein Impurity Monitoring in Adeno-Associated Virus-Based Gene Therapy Products Using Optimized Wide Window Data-Dependent Acquisition Method.

Compared to other protein therapeutics, there is currently limited knowledge about the residual host cell proteins (HCPs) in adeno-associated virus (AAV)-based gene therapy products. This is primarily due to the lack of a robust and sensitive mass spectrometry-based method for HCP analysis in AAV samples. Existing liquid chromatography-mass spectrometry methods used for analyzing HCPs in therapeutic monoclonal antibodies (mAbs) often cannot be directly applied to AAVs, due to some unique characteristics of AAV samples encountered during their development such as limited sample availability/protein concentration and the presence of surfactants. In this study, we have developed a novel workflow for robust and in-depth HCP analysis of AAV samples by combining wide-window data-dependent acquisition for improved low-abundance HCP detection with single-pot, solid-phase-enhanced sample preparation (SP3) for low-input sample preparation. Using this newly developed method, we were able to detect more than 650 HCPs in a commercial AAV1 sample with a high quantitative reproducibility. This represents a greater than 5-fold increase in HCP protein identification compared to an in-solution digestion method followed by traditional data-dependent acquisition. Similar benefits can also be achieved for other AAV serotypes that were produced internally and purified through different processes. The detection limit of this method is as low as 0.06 ng/mL, enabling more comprehensive HCP coverage in AAV samples. Moreover, for the first time, we have identified several process-related viral proteins, such as Rep 78 and E4. These proteins need to be closely monitored during AAV process development as they may present a greater risk for immunogenicity compared to HCPs that are derived from human HEK293 cells.

Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme.

In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430-670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).

Proteomics analysis of serum and urine identifies VCP and CTSA as potential biomarkers associated with multiple myeloma

AimsWe analyzed the differentially expressed proteins (DEPs) in serum and urine in order to provide new potential biomarkers for MM. MethodsData-Independent Acquisition-based proteomics of serum and urine was performed to identify potential biomarkers for MM patients. Then we performed Western Blotting (WB), ELISA along with their ROC curve analysis to confirm DEPs. ResultsA total of 1653 proteins in serum and 4519 proteins in urine were identified using Data-Dependent Acquisition method. VCP was the only protein that showed significant differences in different comparison groups in both serum and urine. Pathway analysis revealed that protein processing in the endoplasmic reticulum was the most relevant pathway associated with MM. Furthermore, the increased expression of HSP90B1, VCP, CTSA, HYOU1, PDIA4, and RAB7A was detected by WB. The results of ELISA indicated that a combination of VCP and CTSA provided a high area under curve (AUC) value of 0.883 (95 % CI, 0.769-0.997, p < 0.001) to diagnose NDMM. The combination of VCP, CTSA, ALB, and HGB exhibited better performance (AUC = 0.981), with 100 % specificity and 86.7 % sensitivity. ConclusionThese findings suggest VCP and CTSA exhibit potential as biomarkers for MM, which may be helpful in the molecular mechanisms and pathogenesis upon further investigation.

Differential lipid analysis of oxaliplatin-sensitive and resistant HCT116 cells reveals different levels of drug-induced lipid droplet formation.

Lipid droplets (LDs) are intracellular storage vesicles composed of a neutral lipid core surrounded by a glycerophospholipid membrane. LD accumulation is associated with different stages of cancer progression and stress responses resulting from chemotherapy. In previous work, a novel dual nano-electrospray ionization source and data-dependent acquisition method for measuring the relative abundances of lipid species between two extracts were described and validated. Here, this same source and method were used to determine if oxaliplatin-sensitive and resistant cells undergo similar lipid profile changes, with the goal of identifying potential signatures that could predict the effectiveness of an oxaliplatin-containing treatment. Oxaliplatin is commonly used in the treatment of colorectal cancer. When compared to a no-drug control, oxaliplatin dosing caused significant increases in triglyceride (TG) and cholesterol ester (CE) species. These increases were more pronounced in the oxaliplatin-sensitive cells than in oxaliplatin-resistant cells. The increased neutral lipid abundance correlated with LD formation, as confirmed by confocal micrographs of Nile Red-stained cells. Untargeted proteomic analyses also support LD formation after oxaliplatin treatment, with an increased abundance of LD-associated proteins in both the sensitive and resistant cells.

BoxCar data-dependent acquisition improves the MS/MS coverage in liquid chromatography-mass spectrometry-based metabolomics analysis

Tandem mass spectrometry (MS2) information is always applied for compound identification or annotation in liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics study. Due to the complex chemical composition of the biological sample, acquiring MS2 spectra that cover all components is still challenging. Data-dependent acquisition (DDA) is commonly applied to obtain the MS2 data, but intensity-based trigger criteria restrict low abundance ions for fragmentation. BoxCar DDA was introduced on an LC- quadrupole time of flight (Q-TOF) MS in this study to improve the MS2 coverage of conventional DDA. BoxCar DDA with sub-mass ranges of 50, 25, and 10 was applied to perform sample analysis, respectively. The performance of multi-parallel collision-induced dissociation MS (MSe), DDA, and BoxCar DDA was compared. The results showed BoxCar DDA significantly increased the MS2 coverage and improved the signal quality. The MS2 coverage increased with the reduction of the sub-mass range. BoxCar (10) DDA exhibited the highest coverage of MS2 information. The signal quality acquired from DDA is better than that obtained under MSe, and the BoxCar (10) also showed superior for ions with low intensity. The plant material of gross saponin of Tribulus Terrestris L. fruit (GSTTF) was detected using the established BoxCar DDA, and the informative MS2 data was used to perform compound identification. The newly developed BoxCar DDA method could provide comprehensive MS2 data of analyte, which will not only be helpful to increase the confidence of compound identification in untargeted metabolomics but also facilitate ion pair detection in pseudotargeted analysis.

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.

Prioritize biologically relevant ions for data-independent acquisition (BRI-DIA) in LC-MS/MS-based lipidomics analysis.

Data-dependent acquisition (DDA) is the most commonly used MS/MS scan method for lipidomics analysis on orbitrap-based instrument. However, MS instrument associated software decide the top N precursors for fragmentation, resulting in stochasticity of precursor selection and compromised consistency and reproducibility. We introduce a novel workflow using biologically relevant lipids to construct inclusion list for data-independent acquisition (DIA), named as BRI-DIA workflow. To ensure consistent coverage of biologically relevant lipids in LC-MS/MS-based lipidomics analysis. Biologically relevant ion list was constructed based on LIPID MAPS and lipidome atlas in MS-DIAL 4. Lipids were extracted from mouse tissues and used to assess different MS/MS scan workflow (DDA, BRI-DIA, and hybrid mode) on LC-Orbitrap Exploris 480 mass spectrometer. DDA resulted in more MS/MS events, but the total number of unique lipids identified by three methods (DDA, BRI-DIA, and hybrid MS/MS scan mode) is comparable (580 unique lipids across 44 lipid subclasses in mouse liver). Major cardiolipin molecular species were identified by data generated using BRI-DIA and hybrid methods and allowed calculation of cardiolipin compositions, while identification of the most abundant cardiolipin CL72:8 was missing in data generated using DDA method, leading to wrong calculation of cardiolipin composition. The method of using inclusion list comprised of biologically relevant lipids in DIA MS/MS scan is as efficient as traditional DDA method in profiling lipids, but offers better consistency of lipid identification, compared to DDA method. This study was performed using Orbitrap Exploris 480, and we will further evaluate this workflow on other platforms, and if verified by future work, this biologically relevant ion fragmentation workflow could be routinely used in many studies to improve MS/MS identification capacities.

Improved profiling of low molecular weight serum proteome for gastric carcinoma by data-independent acquisition.

Low molecular weight proteins (LMWPs) in the bloodstream participate in various biological processes and are closely associated with disease status, whereas identification of serous LMWPs remains a great technical challenge due to the wide dynamic range of protein components. In this study, we constructed an integrated LMWP library by combining the LMWPs obtained by three enrichment methods (50% ACN, 20% ACN + 20mM ABC, and 30kDa) and their fractions identified by the data-dependent acquisition method. With this newly constructed library, we comprehensively profiled LMWPs in serum using data-independent acquisition and reliably achieved quantitative results for 75% serous LMWPs. When applying this strategy to quantify LMWPs in human serum samples, we could identify 405 proteins on average per sample, of which 136 proteins were with a MW less than 30kDa and 293 proteins were with a MW less than 65kDa. Of note, pre- and post-operative gastric carcinoma (GC) patients showed differentially expressed serous LWMPs, which was also different from the pattern of LWMP expression in healthy controls. In conclusion, our results showed that LMWPs could efficiently distinguish GC patients from healthy controls as well as between pre- and post-operative statuses, and more importantly, our newly developed LMWP profiling platform could be used to discover candidate LMWP biomarkers for disease diagnosis and status monitoring.

Mass spectrometry-based ganglioside profiling provides potential insights into Alzheimer's disease development

Gangliosides are a family of glycosphingolipids which are particularly enriched in the nervous system. They play crucial roles in neuroprotection and neurological diseases. Alzheimer's disease (AD) is a neurodegenerative disease with cognitive, judgment and memory dysfunction. In this study, a mass spectrometry-based data-dependent acquisition method assisted with fragmentation characteristics screening by computer algorithm was developed for qualitative and quantitative analysis of gangliosides at low concentration. The developed method was applied to obtain detailed ganglioside species content in hippocampus of model mice (APPswe/PS1dE9 transgenic mice) with AD at 3- to 8-month-old. Up-regulated acetylated and N-acetylgalactosaminylated ganglioside species, and the down-regulated major gangliosides were observed with the development of AD from early to late stage. We speculated that deterioration of AD may be related to the acetylation/N-acetylgalactosaminylation transformation of complex gangliosides due to the inhibition of GD3 synthase activity. Moreover, the ganglioside species di-O-Ac-GT1a (d36:1), O-Ac-GD1b (d36:1) and O-Ac-GD1b (d36:0) were considered as the time-coursed biomarkers, and O-Ac-GT1a (d36:2) could be a candidate for early diagnosis of AD.

High-Throughput Plasma Proteomic Profiling.

Plasma and serum are rich sources of proteins that are commonly used for clinical proteome profiling and biomarkers discovery. However, high-throughput plasma proteome profiling and quantitative analysis using mass spectrometry are challenging because of the large dynamic range of protein abundance and complexity. To overcome these challenges, we developed a convenient high-throughput workflow of depleted plasma using the 4D-Proteomics feature of the Bruker timsTOF Pro mass spectrometer with data-dependent (PASEF) and data-independent acquisition (diaPASEF) method that can potentially be used in a clinical proteome profiling and biomarker discoveries. This workflow is robust, optimal for high throughput, high proteome depth, and is reproducible. In our sample preparation steps, we used immuno-depletion steps to remove high-abundance plasma proteins, and without any further cleanup steps, we can use depleted plasma samples directly for enzymatic digestion. Immuno-depletion steps and 4D-Proteomics features of timsTOF Pro increase the plasma proteome depth, and accuracy with the identification of >800 protein groups.

BoxCar increases the depth and reproducibility of diabetic urinary proteome analysis.

Mass spectrometry-based proteomics performs well in high throughput detection of urinary proteins. Nonetheless, protein identification depth and reproducibility remain the challenges in diabetic urinary proteome with high complexity and broad dynamic range, especially for low-abundant proteins. As a new data acquisition strategy, the BoxCar method was reported to benefit for low-abundant protein identification. Whether it is propitious to diabetic samples with high dynamic range proteomes has not been discussed yet. We aimed to apply BoxCar method to diabetic urine sample analysis, and to compare it with standard data dependent acquisition (DDA) method on protein identification in detail. We performed seven technical replicates analysis on two urine samples from healthy individuals and diabetic patients to evaluate protein detection of BoxCar and standard DDA methods on single sample. Further comparison of two methods was made on multiple diabetic urine samples. BoxCar could increase over 20% of identified proteins and performed better quantitative reproducibility than standard DDA method either in single or multiple diabetic urinary samples. BoxCar also improved the detection of low-abundant proteins. Functional enrichment analysis of normal albuminuria or microalbuminuria samples indicated that BoxCar acquired more diabetes-related biological information. The study demonstrates that BoxCar could enhance the depth and reproducibility in diabetic urinary proteome analysis, which provides reference for mass spectrometry approach selection in clinical urinary proteomic research.

Mass Spectrometry‐Based Proteomic Profiling of Efavirenz‐, Nevirapine‐, and Acetaminophen‐Induced Toxicity in Primary Human Hepatocytes

Unexpected hepatotoxicity is one of the key reasons that drugs fail in clinical trials. A detailed understanding of the mechanism(s) that underlie drug-induced hepatotoxicity is needed. Mass-spectrometry (MS) based proteomics allows for in-depth characterization of the protein components of biological systems and cellular pathways at the levels of protein expression changes. The objective of this study was to employ proteomics to investigate the impact of a subset of known hepatotoxic drugs, namely efavirenz (EFV), nevirapine (NVP), acetaminophen (APAP), on the hepatocyte proteome in vitro. Using cultured cryopreserved primary human hepatocytes, the IC50 values for each of these drugs was determined from 8-point titrations following a 24-hour exposure time. Cell death was measured using the CellTiter-Glo® luminescent reagent. The IC50 values for NVP, EFV, APAP were 50 ± 30 μM, 20 ± 10 μM, 18 ± 10 μM, respectively. For proteomic analysis, the hepatocytes were incubated with each drug individually at the IC50 or higher than the IC50 to induce substantial hepatotoxicity. After 24 hours of incubation, the cells were then harvested and lysed, and a suspension-trapping filter-based approach was utilized to prepare the samples for subsequent proteomics studies. Each sample was injected onto a nano-liquid chromatography tandem MS system. The MS data were acquired using a data-dependent acquisition method that dynamically selected the top 10 most abundant precursor ions from the survey scan (400-1600 Th) for C-trap fragmentation. Of the proteins detected, 930 were common to all the samples analyzed. Following, treatment with 50 μM NVP, 50 μM EFV, and 200 μM APAP, the abundance of 81 proteins, 56 proteins, and 36 proteins, respectively, were significantly (p ≤ 0.05) different as compared to controls groups. These proteins included those involved in T cell differentiation, endoplasmic reticulum (ER) -Golgi transport, and inflammation among other pathways. These data can be leveraged to build a proteome map of drug-induced hepatotoxicity.

Mesh Fragmentation Improves Dissociation Efficiency in Top-down Proteomics

Top-down proteomics is a key mass spectrometry-based technology for comprehensive analysis of proteoforms. Proteoforms exhibit multiple high charge states and isotopic forms in full MS scans. The dissociation behavior of proteoforms in different charge states and subjected to different collision energies is highly variable. The current widely employed data-dependent acquisition (DDA) method selects a narrow m/z range (corresponding to a single proteoform charge state) for dissociation from the most abundant precursors. We describe here Mesh, a novel dissociation strategy, to dissociate multiple charge states of one proteoform with multiple collision energies. We show that the Mesh strategy has the potential to generate fragment ions with improved sequence coverage and improve identification ratios in top-down proteomic analyses of complex samples. The strategy is implemented within an open-source instrument control software program named MetaDrive to perform real time deconvolution and precursor selection.

Cerebrospinal Fluid of Preeclamptic and Normotensive Pregnant Women Compared to Nonpregnant Women Analyzed with Mass Spectrometry.

Preeclampsia is a pregnancy-specific multiorgan disorder in which impaired placental functioning and excessive oxidative stress play an important role. We previously showed distinct differences between cerebrospinal fluid proteins in patients with preeclampsia and normotensive pregnant women. An additional group of nonpregnant women was included to study the presence of pregnancy-related proteins in normotensive and preeclamptic pregnancies and whether pregnancy-related proteins were associated with preeclampsia. Cerebrospinal fluid samples were tryptically digested and subsequently measured with a nano-LC-tribrid Orbitrap mass spectrometry system. Proteins were identified by shotgun proteomic analysis based on a data-dependent acquisition method. Proteins identified in preeclampsia, normotensive pregnant controls, and nonpregnant groups were compared to the Progenesis method according to the criteria as previously described and with a secondary analysis using a Scaffold method including Benjamini–Hochberg correction for multiple testing. For preeclampsia, the Progenesis and the Scaffold method together identified 15 (eight proteins for both analyses with one overlap) proteins that were significantly different compared to normotensive control pregnancies. Three of these 15 proteins, which were elevated in cerebrospinal fluid of preeclamptic women, were described to be pregnancy proteins with a calcium-binding function. Using two analysis methods (Progenesis and Scaffold), four out of 15 differential proteins were associated with pregnancy, as described in the literature. Three out of the four pregnancy-related proteins were elevated in preeclampsia. Furthermore, the contribution of elevated (n = 4/15) and downregulated (n = 2/15) calcium-binding proteins in preeclampsia is remarkably high (40%) and needs to be elucidated further.

Comparative Proteomic Profiling of Marine and Freshwater Synechocystis Strains Using Liquid Chromatography-Tandem Mass Spectrometry

Freshwater Synechocystis sp. PCC 6803 has been considered to be a platform for the production of the next generation of biofuels and is used as a model organism in various fields. Various genomics, transcriptomics, metabolomics, and proteomics studies have been performed on this strain, whereas marine Synechocystis sp. PCC 7338 has not been widely studied despite its wide distribution. This study analyzed the proteome profiles of two Synechocystis strains using a liquid chromatography–tandem mass spectrometry-based bottom-up proteomic approach. Proteomic profiling of Synechocystis sp. PCC 7338 was performed for the first time with a data-dependent acquisition method, revealing 18,779 unique peptides and 1794 protein groups. A data-independent acquisition method was carried out for the comparative quantitation of Synechocystis sp. PCC 6803 and 7338. Among 2049 quantified proteins, 185 up- and 211 down-regulated proteins were defined in Synechocystis sp. PCC 7338. Some characteristics in the proteome of Synechocystis sp. PCC 7338 were revealed, such as its adaptation to living conditions, including the down-regulation of some photosynthesis proteins, the up-regulation of kdpB, and the use of osmolyte glycine as a substrate in C1 metabolism for the regulation of carbon flow. This study will facilitate further studies on Synechocystis 7338 to define in depth the proteomic differences between it and other Synechocystis strains.

An integrated data-dependent and data-independent acquisition method for hazardous compounds screening in foods using a single UHPLC-Q-Orbitrap run

Thousands of hazardous compounds that contaminate foods and feeds pose potential risks for human and animal health. However, it remains a challenge to perform a fast monitoring for safety surveillance. Herein we report a novel approach, integrated data-dependent and data-independent acquisition (DDIA) method, to efficiently screen for hundreds of chemicals in a single run using ultra-high-performance liquid chromatography coupled with quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap). This method was successfully applied to analyze 180 veterinary drugs in milk, 220 pesticides in tomato and 50 mycotoxins in maize, respectively. Compared with the widely used approaches of data-dependent acquisition (DDA) or data-independent acquisition (DIA), the obtained results indicate that DDIA-based method combines the advantages of both DDA and DIA, since it achieves higher reproducibility of identification, lower false results for targeted compounds. Notably, the advantage of DDIA approach is that it enables better date retroactivity for untargeted compounds, such as metabolites and decomposition products. With the improvement in high-resolution mass spectrometry (HRMS) as well as data-mining techniques, we believe that DDIA data acquisition approach based on LC-HRMS will be widely applied in various fields in the near future, especially in compound screening and omics field, such as metabolomics and proteomics.

Guidance Document: Validation of a High-Performance Liquid Chromatography-Tandem Mass Spectrometry Immunopeptidomics Assay for the Identification of HLA Class I Ligands Suitable for Pharmaceutical Therapies

For more than two decades naturally presented, human leukocyte antigen (HLA)-restricted peptides (immunopeptidome) have been eluted and sequenced using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Since, identified disease-associated HLA ligands have been characterized and evaluated as potential active substances. Treatments based on HLA-presented peptides have shown promising results in clinical application as personalized T cell-based immunotherapy. Peptide vaccination cocktails are produced as investigational medicinal products under GMP conditions. To support clinical trials based on HLA-presented tumor-associated antigens, in this study the sensitive LC-MS/MS HLA class I antigen identification pipeline was fully validated for our technical equipment according to the current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidelines.The immunopeptidomes of JY cells with or without spiked-in, isotope labeled peptides, of peripheral blood mononuclear cells of healthy volunteers as well as a chronic lymphocytic leukemia and a bladder cancer sample were reliably identified using a data-dependent acquisition method. As the LC-MS/MS pipeline is used for identification purposes, the validation parameters include accuracy, precision, specificity, limit of detection and robustness.

Capsaicin metabolites and GSH-associated detoxification and biotransformation pathways in human liver microsomes revealed by LC-HRMS/MS with data-mining tools

Capsaicin (CAP) is a principal pungent ingredient in hot peppers, it is also employed as a common food additive, an efficient pharmaceutical component, or even a riot control agent. CAP exerts various pharmacological activities as well as associated adverse physiological responses and causes moderate toxicity if overused. A full screening and identification of CAP metabolites in combination with its main detoxification pathways are crucial for the clear demonstration on its pharmacological and toxicological significance. Here, we employed a post-acquisition data-mining metabolic screening approach to rapidly find and identify a broad range of CAP metabolites generated from in vitro human liver microsomes, based on an ultra-performance liquid chromatography-quadrupole orbitrap high resolution tandem mass spectrometric method. First, we collected full scan MS and MS/MS data sets by a data-dependent acquisition method in positive ion mode, and then we employed a modified mass defect filter and a diagnostic ion filter to screen and identify all the probable CAP metabolites, combining with information including retention time, accurate mass, characteristic fragments, and relevant drug biotransformation patterns. In comparison with the stable isotope-labeled CAP involved biotransformation products, we confirmed 19 functionalized metabolites and 13 glutathione (GSH) conjugates of CAP, in which 13 metabolites are reported for the first time. We then briefly depicted an overview metabolic pathway of CAP from the GSH detoxification viewpoint, revealed that various metabolites of CAP can be generated from single or multiple biotransformation and metabolic reactions. Both CAP and its reactive metabolites produced relevant GSH conjugates, which indicates a wide and important detoxification value of GSH conjugation way.

Metabolomics responses of Bambusa pervariabilis × Dendrocalamopsis grandis varieties to Biotic (pathogenic fungus) stress

Bambusa pervariabilis × Dendrocalamopsis grandis blight, caused by Arthrinium phaeospermum, is one of the most common and serious diseases in bamboo and occurs in the newly born twigs. Bamboo has suffered large dead areas, including more than 3000 hm2, which greatly threatens the process of returning farmlands to forests and the construction of ecological barriers. To identify differential metabolites and metabolic pathways associated with B. pervariabilis × D. grandis to A. phaeospermum, ultra-performance liquid chromatography (UPLC) and quadrupole-time of flight (Q-TOF) Mass Spectrometry (MS) combined with a data-dependent acquisition method was used to analyse the entire sample spectrum. In total, 13223 positive ion peaks and 10616 negative ion peaks were extracted. OPLS-DA and several other analyses were performed using the original data. The OPLS-DA models showed good quality and had strong predictive power, indicating clear trends in the analyses of the treatment and control groups. Clustering and KEGG pathway analyses were used to screen the differential metabolites in the treatment and control groups from the three B. pervariabilis × D. grandis varieties and reflected their metabolic responses induced by A. phaeospermum infection. The results showed that the three B. pervariabilis × D. grandis varieties mode showed significant changes in the following six resistance-related metabolites after A. phaeospermum invasion in positive and negative ion modes: proline, glutamine, dictamnine, apigenin 7-O-neohesperidoside, glutamate, and cis-Aconitate. The following four main metabolic pathways are involved: Arginine and proline metabolism, Glyoxylate and dicarboxylate metabolism, Biosynthesis of alkaloids derived from shikimate pathway, and Flavone and flavonol biosynthesis. This study lays a foundation for the later detection of differential metabolites and metabolic pathways for targeting, and provides a theoretical basis for disease-resistant breeding and the control of B. pervariabilis × D. grandis blight.