Mass Spectrometry Features Research Articles

Characterization of the Volatile Composition of Fermented Ciders Made From Dessert Apple Cultivars With and Without Maceration

Cider is often produced from difficult-to-grow heirloom apple cultivars not commonly produced in North America. As such, the value of widely grown dessert apples for fermented cider was explored, with ciders produced from eleven cultivars analyzed. Additionally, the practice of maceration, or fermentation with pomace, was also investigated. Volatile compounds were analyzed by gas chromatography-mass spectrometry. Raw mass spectrometry (MS) data was then followed by an initial untargeted statistical analysis. Principal component analysis of the MS features demonstrated clear separation between cultivars and between maceration effects, prompting further investigation into the compounds impacting cultivar and maceration discrimination. The aroma-active compounds identified are known to elicit sensorial responses often described as sweet, fruity, and floral. Some compounds have been previously identified as primarily apple-derived. While esters and higher alcohols, originating from yeast metabolic activities, were also identified as contributing factors of the observed differences. Despite these compounds being impacted by yeast metabolism, they were highly influenced by cultivar and treatment, indicating apple-derived precursors were, in part, influencing the yeast-mediated aroma differences. ‘Galaxy Gala’ cider samples had the highest overall intensities of quantified volatiles in both years, driven by high eugenol and 1-butanol concentrations. Extended apple pomace maceration did not induce new aromas but increased the content of some compounds. Maceration impact was cultivar dependent, e.g., ethyl 2-methylbutyrate in the ‘Yataka Fuji’ increased 14-fold with maceration but less in other cultivars. This work demonstrates that dessert apples produce ciders with a diversity of aromas that may be further increased via maceration and are worth consideration by producers.

Data Fusion-based Discovery (DAFdiscovery) pipeline to aid compound annotation and bioactive compound discovery across diverse spectral data.

Data Fusion-based Discovery (DAFdiscovery) is a pipeline designed to help users combine mass spectrometry (MS), nuclear magnetic resonance (NMR), and bioactivity data in a notebook-based application to accelerate annotation and discovery of bioactive compounds. It applies Statistical Total Correlation Spectroscopy (STOCSY) and Statistical HeteroSpectroscopy (SHY) calculation in their data using an easy-to-follow Jupyter Notebook. Different case studies are presented for benchmarking, and the resultant outputs are shown to aid natural products identification and discovery. The goal is to encourage users to acquire MS and NMR data from their samples (in replicated samples and fractions when available) and to explore their variance to highlight MS features, NMR peaks, and bioactivity that might be correlated to accelerated bioactive compound discovery or for annotation-identification studies. Different applications were demonstrated using data from different research groups, and it was shown that DAFdiscovery reproduced their findings using a more straightforward method. DAFdiscovery has proven to be a simple-to-use method for different situations where data from different sources are required to be analyzed together.

PH/Acetonitrile-Gradient Reversed-Phase Fractionation of Enriched Hyper-Citrullinated Library in Combination with LC-MS/MS Analysis for Confident Identification of Citrullinated Peptides.

Citrullination, the Ca2+-driven enzymatic conversion of arginine residues to citrulline, is a posttranslational modification, implicated in several physiological and pathological processes. Several methods to detect citrullinated proteins have been developed, including color development reagent, fluorescence, phenylglyoxal, and antibody-based methods. These methods yet suffer from limitations in sensitivity, specificity, or citrullinated site determination. Mass spectrometry (MS)-based proteomic analysis has emerged as a promising method to resolve these problems. However, due to low abundance of citrullinated proteins and similar MS features to deamidation of asparagine and glutamine, confident identification of citrullinated proteome is challenging. Here, we present a systematic approach to identify a compendium of steps to enhance the number of detected citrullinated residue and implement diagnostic MS feature that allow the confidence of MS-based identifications. Our method is based on the concept of generation of hyper-citrullinated library with high-pH reversed-phase peptide fractionation that allows to enrich in low abundance citrullinated peptides and amplify the effect of charge loss upon citrullination. Application of our approach to complex global citrullino-proteome datasets demonstrates the confident assessment of citrullinated peptides, thereby enhancing the size and functional interpretation of citrullinated proteomes.

Identification of the Tyrosine- and Phenylalanine-Derived Soluble Metabolomes of Sorghum.

The synthesis of small organic molecules, known as specialized or secondary metabolites, is one mechanism by which plants resist and tolerate biotic and abiotic stress. Many specialized metabolites are derived from the aromatic amino acids phenylalanine (Phe) and tyrosine (Tyr). In addition, the improved characterization of compounds derived from these amino acids could inform strategies for developing crops with greater resilience and improved traits for the biorefinery. Sorghum and other grasses possess phenylalanine ammonia-lyase (PAL) enzymes that generate cinnamic acid from Phe and bifunctional phenylalanine/tyrosine ammonia-lyase (PTAL) enzymes that generate cinnamic acid and p-coumaric acid from Phe and Tyr, respectively. Cinnamic acid can, in turn, be converted into p-coumaric acid by cinnamate 4-hydroxylase. Thus, Phe and Tyr are both precursors of common downstream products. Not all derivatives of Phe and Tyr are shared, however, and each can act as a precursor for unique metabolites. In this study, 13C isotopic-labeled precursors and the recently developed Precursor of Origin Determination in Untargeted Metabolomics (PODIUM) mass spectrometry (MS) analytical pipeline were used to identify over 600 MS features derived from Phe and Tyr in sorghum. These features comprised 20% of the MS signal collected by reverse-phase chromatography and detected through negative-ionization. Ninety percent of the labeled mass features were derived from both Phe and Tyr, although the proportional contribution of each precursor varied. In addition, the relative incorporation of Phe and Tyr varied between metabolites and tissues, suggesting the existence of multiple pools of p-coumaric acid that are fed by the two amino acids. Furthermore, Phe incorporation was greater for many known hydroxycinnamate esters and flavonoid glycosides. In contrast, mass features derived exclusively from Tyr were the most abundant in every tissue. The Phe- and Tyr-derived metabolite library was also utilized to retrospectively annotate soluble MS features in two brown midrib mutants (bmr6 and bmr12) identifying several MS features that change significantly in each mutant.

Statistical Modeling for Enhancing the Discovery Power of Citrullination from Tandem Mass Spectrometry Data.

Citrullination is a post-translational modification implicated in various human diseases including rheumatoid arthritis, Alzheimer's disease, multiple sclerosis, and cancers. Due to a relatively low concentration of citrullinated proteins in the total proteome, confident identification of citrullinated proteome is challenging in mass spectrometry (MS)-based proteomic analysis. From these MS-based analyses, MS features that characterize citrullination, such as immonium ions (IMs) and neutral losses (NLs), called diagnostic ions, have been reported. However, there has been a lack of systematic approaches to comprehensively search for diagnostic ions and no statistical methods for the identification of citrullinated proteome based on these diagnostic ions. Here, we present a systematic approach to identify diagnostic IMs, internal ions (INTs), and NLs for citrullination from tandem mass (MS/MS) spectra. Diagnostic INTs mainly consisted of internal fragment ions for di- and tripeptides that contained two and three amino acids with at least one citrullinated arginine, respectively. A statistical logistic regression model was built for a confident assessment of citrullinated peptides that database searches identified (true positives) and prediction of citrullinated peptides that database searches failed to identify (false negatives) using the diagnostic IMs, INTs, and NLs. Applications of our model to complex global proteome data sets demonstrated the increased accuracy in the identification of citrullinated peptides, thereby enhancing the size and functional interpretation of citrullinated proteomes.

A prospective study of serum metabolomic and lipidomic changes in myopic children and adolescents

BackgroundMyopia is a prevalent eye disorder, especially among children and adolescents in eastern Asian countries. Multiple measures have already been taken to prevent and treat myopia, including atropine and dopamine. However, the serum metabolic picture of myopia has not yet been studied as a whole and remains largely unclear. In this paper, a prospective and panoramic study was carried out to find out the whole serum metabolomic and lipidomic picture of myopia. MethodsWith untargeted mass spectrometry (MS), myopia among 211 children and adolescents was studied. The MS features were first grouped across the samples. Then, compound annotation was carried out based on these features. Finally, the metabolite features were mapped to pathways, whose biological functions in myopia were studied and discussed. ResultsA total of 275 metabolite features were derived from 92 aligned MS peak groups with significant fold changes, and then mapped to 33 pathways. By a comprehensive consideration of significance, fold change, importance score and appearance in different omics, 9 pathways were selected, and their biological functions were further analyzed. Among these selected pathways, 5 pathways were related with oxidative stress, a validated phenomenon during myopia development, while 5 pathways were related with dopamine receptor D2, whose molecular function in myopia treatment is not fully understood. A total of 177 metabolite features from 45 peak groups were related with the studied pathways. ConclusionThis prospective study shed light on the whole picture of metabolomic mechanism underlying myopia and provided guidance to further elucidation of compounds and pathways in this whole picture.

A Serum Protein Classifier Identifying Patients with Advanced Non-Small Cell Lung Cancer Who Derive Clinical Benefit from Treatment with Immune Checkpoint Inhibitors.

Pretreatment selection of patients with non-small cell lung cancer (NSCLC) who would derive clinical benefit from treatment with immune checkpoint inhibitors (CPIs) would fulfill an unmet clinical need by reducing unnecessary toxicities from treatment and result in substantial health care savings. In a retrospective study, mass spectrometry (MS)-based proteomic analysis was performed on pretreatment sera derived from patients with advanced NSCLC treated with nivolumab as part of routine clinical care (n = 289). Machine learning combined spectral and clinical data to stratify patients into three groups with good ("sensitive"), intermediate, and poor ("resistant") outcomes following treatment in the second-line setting. The test was applied to three independent patient cohorts and its biology was investigated using protein set enrichment analyses (PSEA). A signature consisting of 274 MS features derived from a development set of 116 patients was associated with progression-free survival (PFS) and overall survival (OS) across two validation cohorts (N = 98 and N = 75). In pooled analysis, significantly better OS was demonstrated for "sensitive" relative to "not sensitive" patients treated with nivolumab; HR, 0.58 (95% confidence interval, 0.38-0-87; P = 0.009). There was no significant association with clinical factors including PD-L1 expression, available from 133 of 289 patients. The test demonstrated no significant association with PFS or OS in a historical cohort (n = 68) of second-line NSCLC patients treated with docetaxel. PSEA revealed proteomic classification to be significantly associated with complement and wound-healing cascades. This serum-derived protein signature successfully stratified outcomes in cohorts of patients with advanced NSCLC treated with second-line PD-1 CPIs and deserves further prospective study.

Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation.

Mass spectrometry (MS) offers unrivalled sensitivity for the metabolite profiling of complex biological matrices encountered in natural products (NP) research. The massive and complex sets of spectral data generated by such platforms require computational approaches for their interpretation. Within such approaches, computational metabolite annotation automatically links spectral data to candidate structures via a score, which is usually established between the acquired data and experimental or theoretical spectral databases (DB). This process leads to various candidate structures for each MS features. However, at this stage, obtaining high annotation confidence level remains a challenge notably due to the extensive chemodiversity of specialized metabolomes. The design of a metascore is a way to capture complementary experimental attributes and improve the annotation process. Here, we show that integrating the taxonomic position of the biological source of the analyzed samples and candidate structures enhances confidence in metabolite annotation. A script is proposed to automatically input such information at various granularity levels (species, genus, and family) and complement the score obtained between experimental spectral data and output of available computational metabolite annotation tools (ISDB-DNP, MS-Finder, Sirius). In all cases, the consideration of the taxonomic distance allowed an efficient re-ranking of the candidate structures leading to a systematic enhancement of the recall and precision rates of the tools (1.5- to 7-fold increase in the F1 score). Our results clearly demonstrate the importance of considering taxonomic information in the process of specialized metabolites annotation. This requires to access structural data systematically documented with biological origin, both for new and previously reported NPs. In this respect, the establishment of an open structural DB of specialized metabolites and their associated metadata, particularly biological sources, is timely and critical for the NP research community.

Metabolome and mass spectrometry: new biomedical analysis perspectives

Metabolomics is defined as an integrative approach consisting in the comprehensive analysis of all of the small molecules of a biological system (the "metabolome"). The main objective of metabolomics in medecine is to discover metabolic biomarkers for diseases. Mass spectrometry (MS) coupled to liquid or gas chromatography is amongst major analytical tools used in metabolomics. However, the holistic approach used in metabolomics requires very good performances of the analytical system (chromatographic column and MS equipment) and the use of non-conventional validation strategies. Metabolomics workflow can be divided in three main steps: sample preparation, MS data acquisition and processing, and statistical analysis. Processing of the "raw" data (obtained after MS acquisition) is mostly required to normalise chromatographic conditions and to carry out accurate quantification of MS features. Features resulting from this processing may be identified later. The statistical analyses include typically multivariate techniques such as supervised and non-supervised methods. Supervised methods make use of the response variable (e.g., case/control) for model construction while non-supervised methods do not use this piece of information. When the study is focused on a particular set of metabolites, targeted metabolomics could be an interesting alternative to the holistic approach since it may allow absolute quantitation and be associated with a reduced cost.

Normalization to specific gravity prior to analysis improves information recovery from high resolution mass spectrometry metabolomic profiles of human urine.

Extraction of meaningful biological information from urinary metabolomic profiles obtained by liquid-chromatography coupled to mass spectrometry (MS) necessitates the control of unwanted sources of variability associated with large differences in urine sample concentrations. Different methods of normalization either before analysis (preacquisition normalization) through dilution of urine samples to the lowest specific gravity measured by refractometry, or after analysis (postacquisition normalization) to urine volume, specific gravity and median fold change are compared for their capacity to recover lead metabolites for a potential future use as dietary biomarkers. Twenty-four urine samples of 19 subjects from the European Prospective Investigation into Cancer and nutrition (EPIC) cohort were selected based on their high and low/nonconsumption of six polyphenol-rich foods as assessed with a 24 h dietary recall. MS features selected on the basis of minimum discriminant selection criteria were related to each dietary item by means of orthogonal partial least-squares discriminant analysis models. Normalization methods ranked in the following decreasing order when comparing the number of total discriminant MS features recovered to that obtained in the absence of normalization: preacquisition normalization to specific gravity (4.2-fold), postacquisition normalization to specific gravity (2.3-fold), postacquisition median fold change normalization (1.8-fold increase), postacquisition normalization to urinary volume (0.79-fold). A preventative preacquisition normalization based on urine specific gravity was found to be superior to all curative postacquisition normalization methods tested for discovery of MS features discriminant of dietary intake in these urinary metabolomic datasets.

A Mass Spectrometric Study for Comparative Analysis and Evaluation of Metabolite Recovery from Plasma by Various Solvent Systems

A solvent system that extracts a maximum number of metabolites belonging to diverse chemical classes from complex biofluids, such as plasma, may offer useful inputs to understand the metabolic and physiological state of an individual. The present study compared seven solvent systems for extraction of metabolites from plasma. The extracts were analyzed by mass spectrometry (MS) and MS/MS (MS2) using a quadrupole time-of-flight liquid chromatography/MS system in positive and negative modes of ionization. Metabolites with molecular mass below 400 were identified using Human Metabolome Database MS2 and MS search interfaces. The acetone/isopropanol (2:1) system yielded promising results in positive ionization mode, as the maximum number of MS and MS2 features was detected in the extract. It was found to be superior in extraction of various classes of metabolites, especially organic acids, nucleosides and nucleoside derivatives, and heterocyclic molecules. Glycerophosphocholines in the mass range of 400-700 were found to be efficiently extracted by the methanol/chloroform/water (8:1:1) system. In negative mode as well, the maximum number of MS2 features was detected in methanol/chloroform/water and acetone/isopropanol extracts. The fingerprints of molecular features obtained in the negative and positive modes differed from each other to a significant extent.

A Software Toolkit and Interface for Performing Stable Isotope Labeling and Top3 Quantification Using Progenesis LC-MS

Numerous software packages exist to provide support for quantifying peptides and proteins from mass spectrometry (MS) data. However, many support only a subset of experimental methods or instrument types, meaning that laboratories often have to use multiple software packages. The Progenesis LC-MS software package from Nonlinear Dynamics is a software solution for label-free quantitation. However, many laboratories using Progenesis also wish to employ stable isotope-based methods that are not natively supported in Progenesis. We have developed a Java programming interface that can use the output files produced by Progenesis, allowing the basic MS features quantified across replicates to be used in a range of different experimental methods. We have developed post-processing software (the Progenesis Post-Processor) to embed Progenesis in the analysis of stable isotope labeling data and top3 pseudo-absolute quantitation. We have also created export ability to the new data standard, mzQuantML, produced by the Proteomics Standards Initiative to facilitate the development and standardization process. The software is provided to users with a simple graphical user interface for accessing the different features. The underlying programming interface may also be used by Java developers to develop other routines for analyzing data produced by Progenesis.

Warfarin Untargeted Metabolomics Study Identifies Novel Procoagulant Ethanolamide Lipids

Abstract 1200Untargeted metabolomics denotes the profiling of several thousand low molecular weight biochemicals, including lipids, hormones, saccharides, nucleotides, organic acids, and amino acids. The analytes are measured using mass spectrometry (MS) as molecule mass peaks without specific “targeting” of specific metabolites or analytes. We undertook an untargeted metabolomics investigation to define the effect of warfarin on plasma metabolite profiles in a crossover study of 17 venous thrombosis patients (9 male, 8 female) (age 58.1 ± 18.6 yr old; range 27–83 yr old). Blood was drawn at 4–6 weeks after initiation of warfarin (patients with an average INR 2.50 ± 0.65) and again at ∼ 10 days after discontinuation of the clinically determined course of warfarin. Plasmas were methanol-extracted and applied to a capillary reverse phase HPLC column, interfaced to an ESI-TOF mass spectrometer. Data were converted and aligned in the time domain using the program XCMS, with 3500–4500 distinct MS features found. Statistically significant differences were ranked using a paired t-test. In addition to warfarin metabolites, other changes were discovered. Several unique lipid metabolites, lyso-phosphatidylcholines (lyso-PC) (16:0 and 18:2), palmitoyl-ethanolamide (N-palmitoylethanolamine)(PEA) and stearoyl-ethanolamide (N-palmitoylethanolamine)(SEA), were identified as significantly decreased during warfarin therapy (p < 0.03)(See Table). PEA and SEA are naturally occurring saturated N-acylethanolamines (ethanolamides), both of which are structurally related to arachidonoyl (20:4) ethanolamide which binds to the cannabinoid receptor. PEA and SEA are devoid of affinity for cannabinoid receptors and physiological roles for PEA and SEA are unknown. When tested for effects on coagulation, exogenously added PEA (16:0) dose-dependently enhanced thrombin generation in plasma induced by tissue factor or calcium ions. SEA (18:0) and other lipid ethanolamides, e.g., arachidonoyl ethanolamide (20:4), also enhanced recalcification-induced thrombin generation in plasma. However, two analogs of PEA, palmitoyl N-isopropylamide and N-palmitoyl taurine which are lacking hydroxyl molecule in the head group, showed little enhancement of thrombin generation in plasma. These results suggest that the free hydroxyl group in the head group appears to be a key component for the observed procoagulant activity in ethanolamide family. How is it that warfarin might affect the metabolism of these lipids? The R-racemer of warfarin interacts with the pregnane X receptor (PNR) and increases CYP3A4 and CYP2C9 mRNAs levels in cultured human cells. Alternations in the catabolic activity of CYP3A4 might alter levels of ethanolamides and lyso-PC in patients. In summary, we employed the innovative untargeted metabolomics methods to identify endogenous biomarkers in warfarin-treated patients to help reveal biochemical changes triggered by this widely used drug which led to the discovery of a new family of plasma procoagulant molecules, namely ethanolamides. The downregulation of procoagulant ethanolamides by warfarin and its anticoagulant consequent effect might be minor, but may yet be an important additional anticoagulant property of warfarin. This study has implications for coagulation and plasma lipid relationships with potential clinical and pharmacologic applications.TablePlasma metabolites changed by warfarin treatmentNameMolecular formulaMH+ calculatedm/z observedFold changeP valuePEAC18H37NO2300.2897300.29082.8 ↓0.026SEAC20H41NO2328.3210328.31933.5 ↓0.030Lyso-PC(18:2)C26H50NO7P520.3397520.34243.5 ↓0.029Lyso-PC(16:0)C24H50NO7P496.3397496.33721.6 ↓0.006 Disclosures:No relevant conflicts of interest to declare.

Biomarker Discovery for Arsenic Exposure Using Functional Data. Analysis and Feature Learning of Mass Spectrometry Proteomic Data

Plasma biomarkers of exposure to environmental contaminants play an important role in early detection of disease. The emerging field of proteomics presents an attractive opportunity for candidate biomarker discovery, as it simultaneously measures and analyzes a large number of proteins. This article presents a case study for measuring arsenic concentrations in a population residing in an As-endemic region of Bangladesh using plasma protein expressions measured by SELDI-TOF mass spectrometry. We analyze the data using a unified statistical method based on functional learning to preprocess mass spectra and extract mass spectrometry (MS) features and to associate the selected MS features with arsenic exposure measurements. The task is challenging due to several factors, the high dimensionality of mass spectrometry data, complicated error structures, and a multiple comparison problem. We use nonparametric functional regression techniques for MS modeling, peak detection based on the significant zero-downcrossing method, and peak alignment using a warping algorithm. Our results show significant associations of arsenic exposure to either under- or overexpressions of 20 proteins.