Project description:LC-MS-based untargeted metabolomics is heavily dependent on algorithms for automated peak detection and data preprocessing due to the complexity and size of the raw data generated. These algorithms are generally designed to be as inclusive as possible in order to minimize the number of missed peaks. This is known to result in an abundance of false positive peaks that further complicate downstream data processing and analysis. As a consequence, considerable effort is spent identifying features of interest that might represent peak detection artifacts. Here, we present the CPC algorithm, which allows automated characterization of detected peaks with subsequent filtering of low quality peaks using quality criteria familiar to analytical chemists. We provide a thorough description of the methods in addition to applying the algorithms to authentic metabolomics data. In the example presented, the algorithm removed about 35% of the peaks detected by XCMS, a majority of which exhibited a low signal-to-noise ratio. The algorithm is made available as an R-package and can be fully integrated into a standard XCMS workflow.
Project description:Liquid chromatography (LC) separation combined with electrochemical coulometric array detection (EC) is a sensitive, reproducible, and robust technique that can detect hundreds of redox-active metabolites down to the level of femtograms on column, making it ideal for metabolomics profiling. EC detection cannot, however, structurally characterize unknown metabolites that comprise these profiles. Several aspects of LC-EC methods prevent a direct transfer to other structurally informative analytical methods, such as LC-MS and NMR. These include system limits of detection, buffer requirements, and detection mechanisms. To address these limitations, we developed a workflow based on the concentration of plasma, metabolite extraction, and offline LC-UV fractionation. Pooled human plasma was used to provide sufficient material necessary for multiple sample concentrations and platform analyses. Offline parallel LC-EC and LC-MS methods were established that correlated standard metabolites between the LC-EC profiling method and the mass spectrometer. Peak retention times (RT) from the LC-MS and LC-EC system were linearly related (r(2) = 0.99); thus, LC-MS RTs could be directly predicted from the LC-EC signals. Subsequent offline microcoil-NMR analysis of these collected fractions was used to confirm LC-MS characterizations by providing complementary, structural data. This work provides a validated workflow that is transferrable across multiple platforms and provides the unambiguous structural identifications necessary to move primary mathematically driven LC-EC biomarker discovery into biological and clinical utility.
Project description:Using an in solution based approach with a sub-proteomic fraction enriched in cardiac sarcomeric proteins; we identified protein abundance in ischemic and non-ischemic regions of rat hearts stressed by acute myocardial ischemia by ligating the left-anterior descending coronary artery in vivo for 1h without reperfusion. Sub-cellular fractionation permitted more in depth analysis of the proteome by reducing the sample complexity. A series of differential centrifugations produced nuclear, mitochondrial, cytoplasmic, microsomal, and sarcomeric enriched fractions of ischemic and non-ischemic tissues. The sarcomeric enriched fractions were labeled with isobaric tags for relative quantitation (iTRAQ), and then fractionated with an Agilent 3100 OFFGEL fractionator. The OFFGEL fractions were run on a Dionex U-3000 nano LC coupled to a ThermoFinnigan LTQ running in PQD (pulsed Q dissociation) mode. The peptides were analyzed using two search engines MASCOT (MatrixScience), and MassMatrix with false discovery rate of <5%. Compared to no fractionation prior to LC-MS/MS, fractionation with OFFGEL improved the identification of proteins approximately four-fold. We found that approximately 22 unique proteins in the sarcomeric enriched fraction had changed at least 20%. Our workflow provides an approach for discovery of unique biomarkers or changes in the protein profile of tissue in disorders of the heart.
Project description:Herein, we report a computational algorithm that follows a spectroscopist-driven elucidation process of the structure of an organic molecule based on IR, 1H and 13C NMR, and MS tabular data. The algorithm is independent from database searching and is based on a bottom-up approach, building the molecular structure from small structural fragments visible in spectra. It employs an analytical combinatorial approach with a graph search technique to determine the connectivity of structural fragments that is based on the analysis of the NMR spectra, to connect the identified structural fragments into a molecular structure. After the process is completed, the interface lists the compound candidates, which are visualized by the WolframAlpha computational knowledge engine within the interface. The candidates are ranked according to the predefined rules for analyzing the spectral data. The developed elucidator has a user-friendly web interface and is publicly available (http://schmarnica.si).
Project description:(1) Background: Filipendula ulmaria (L.) Maxim. (Rosaceae) (meadowsweet) is widely used in phytotherapy against inflammatory diseases. However, its active constituents are not exactly known. Moreover, it contains many constituents, such as flavonoid glycosides, which are not absorbed, but metabolized in the colon by gut microbiota, producing potentially active metabolites that can be absorbed. The aim of this study was to characterize the active constituents or metabolites. (2) Methods: A F. ulmaria extract was processed in an in vitro gastrointestinal biotransformation model, and the metabolites were characterized using UHPLC-ESI-QTOF-MS analysis. In vitro anti-inflammatory activity was evaluated by testing the inhibition of NF-κB activation, COX-1 and COX-2 enzyme inhibition. (3) Results: The simulation of gastrointestinal biotransformation showed a decrease in the relative abundance of glycosylated flavonoids such as rutin, spiraeoside and isoquercitrin in the colon compartment, and an increase in aglycons such as quercetin, apigenin, naringenin and kaempferol. The genuine as well as the metabolized extract showed a better inhibition of the COX-1 enzyme as compared to COX-2. A mix of aglycons present after biotransformation showed a significant inhibition of COX-1. (4) Conclusions: The anti-inflammatory activity of F. ulmaria may be explained by an additive or synergistic effect of genuine constituents and metabolites.
Project description:DNA can be damaged through covalent modifications of the nucleobases by endogenous processes. These modifications, commonly referred to as DNA adducts, can persist and may lead to mutations, and ultimately to the initiation of cancer. A screening methodology for the majority of known endogenous DNA adducts would be a powerful tool for investigating the etiology of cancer and for the identification of individuals at high-risk to the detrimental effects of DNA damage. This idea led to the development of a DNA adductomic approach using high resolution data-dependent scanning, an extensive MS2 fragmentation inclusion list of known endogenous adducts, and neutral loss MS3 triggering to profile all DNA modifications. In this method, the detection of endogenous DNA adducts is performed by observation of their corresponding MS3 neutral loss triggered events and their relative quantitation using the corresponding full scan extracted ion chromatograms. The method's inclusion list consists of the majority of known endogenous DNA adducts, compiled, and reported here, as well as adducts specific to tobacco exposure included to compare the performance of the method with previously developed targeted approaches. The sensitivity of the method was maximized by reduction of extraneous background signal through the purification and minimization of the amount of commercially obtained enzymes used for the DNA hydrolysis. In addition, post-hydrolysis sample purification was performed using off-line HPLC fraction collection to eliminate the highly abundant unmodified bases, and to avoid introduction of plasticizers found in solid-phase extraction cartridges. Also, several instrument parameters were evaluated to optimize the ion signal intensities and fragmentation spectra quality. The method was tested on an animal model of lung carcinogenesis where A/J mice were exposed to the tobacco specific lung carcinogen 4-methylnitrosamino-1-3-pyridyl-1-butanone (NNK) with its effects enhanced by co-exposure to the pro-inflammatory agent lipopolysaccharide (LPS). Lung DNA were screened for endogenous DNA adducts known to result from oxidative stress and LPS-induced lipid peroxidation, as well as for adducts due to NNK exposure. The relative quantitation of the detected DNA adducts was performed using parallel reaction monitoring MS2 analysis, demonstrating a general workflow for analysis of endogenous DNA adducts.
Project description:Acylcarnitines are fatty acyl esters of L-carnitine and facilitate the entry of long-chain fatty acids into mitochondria via the carnitine shuttle, where they are metabolized via ß-oxidation. Alterations of acylcarnitine species can be diagnostic for fatty acid oxidation disorders and organic aciduria and are thus frequently used to screen newborns. Only a subfraction of all known acylcarnitines is thereby monitored and quantified. Therefore, a method for the simultaneous fast and robust detection of all known acylcarnitines was developed using a single concise liquid chromatography mass spectrometry (LC-MS) approach. Derivatization by 3-nitrophenylhydrazine increased the signal intensity of the acylcarnitines and a linear elution from a reversed phase column was observed that was dependent on the length of the carbon chain. This allowed a precise prediction of the exact elution time for each acylcarnitine class, which depended solely on the chemical nature of the carbon chain. This method can be further used to screen for yet unknown acylcarnitine species and adds a layer of confidence for their correct identification. Altogether 123 acylcarnitines species were used to establish a targeted low-resolution LC-MS method. The method was applied to acylcarnitine profiling in several mouse tissues and fluids, in order to identify large differences in the quantity and composition of acylcarnitines.
Project description:Filipendula ulmaria is a plant commonly used for the treatment of several pathologies, such as diarrhoea, ulcers, pain, stomach aches, fevers, and gout. Our study focused on the use of F. ulmaria for the treatment of gout disease. We first studied the chemical composition of a methanolic extract of the aerial parts and demonstrated its xanthine oxidase (XO) inhibitory activity. Then, we performed a fractionation and evaluated the most XO inhibitory active fractions by UV measurement. Purification of some fractions allowed the determination of the inhibitory activity of pure compounds. We demonstrated that spiraeoside, a glycosylated flavonoid, possesses an activity around 25 times higher than allopurinol, used as a reference in the treatment of gout disease. In order to easily and quickly identify potent inhibitors in complex matrix, we developed a complementary strategy based on an HPLC method and an Effect Directed Assay (EDA) method combining HPTLC and biochemical assays. The HPLC method, capable of determining compounds exhibiting interactions with the enzyme, could be an efficient strategy for evaluating potent enzyme inhibitors in a complex mixture. This strategy could be applied for quantitative assays using LC/MS experiments.