Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification.

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability.

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra.

The high-throughput identification of unknown metabolites in biological samples remains challenging. Most current non-targeted metabolomics studies rely on mass spectrometry, followed by computational methods that rank thousands of candidate structures based on how closely their predicted mass spectra match the experimental mass spectrum of an unknown. We reasoned that the infrared (IR) spectra could be used in an analogous manner and could add orthologous structure discrimination; however, this has never been evaluated on large data sets.

MolFind: A Protocol for Acquiring and Integrating MS Data to Improve In Silico Chemical Structure Elucidation for Metabolomics.

Structure elucidation of metabolites (<1000 Da) in biofluids is extremely challenging due to the diversity and complexity of chemical structure space. Generally, due to lack of reference tandem mass data (MS), in silico fragmenters are used to rank candidates acquired from chemical databases as a function on how well they explain an experimental collision-induced dissociation spectrum. However, multistage fragmentation data (i.

Evaluation of an Artificial Neural Network Retention Index Model for Chemical Structure Identification in Nontargeted Metabolomics.

Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is a major analytical technique used for nontargeted identification of metabolites in biological fluids. Typically, in LC-ESI-MS/MS based database assisted structure elucidation pipelines, the exact mass of an unknown compound is used to mine a chemical structure database to acquire an initial set of possible candidates. Subsequent matching of the collision induced dissociation (CID) spectrum of the unknown to the CID spectra of candidate structures facilitates identification.

Development of a Reverse Phase HPLC Retention Index Model for Nontargeted Metabolomics Using Synthetic Compounds.

The MolFind application has been developed as a nontargeted metabolomics chemometric tool to facilitate structure identification when HPLC biofluids analysis reveals a feature of interest. Here synthetic compounds are selected and measured to form the basis of a new, more accurate, HPLC retention index model for use with MolFind. We show that relatively inexpensive synthetic screening compounds with simple structures can be used to develop an artificial neural network model that is successful in making quality predictions for human metabolites.

Development of Database Assisted Structure Identification (DASI) Methods for Nontargeted Metabolomics.

Metabolite structure identification remains a significant challenge in nontargeted metabolomics research. One commonly used strategy relies on searching biochemical databases using exact mass. However, this approach fails when the database does not contain the unknown metabolite (i.

Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data.

Background: Artificial Neural Networks (ANN) are extensively used to model 'omics' data. Different modeling methodologies and combinations of adjustable parameters influence model performance and complicate model optimization.

Methodology: We evaluated optimization of four ANN modeling parameters (learning rate annealing, stopping criteria, data split method, network architecture) using retention index (RI) data for 390 compounds.

Correction of precursor and product ion relative abundances in order to standardize CID spectra and improve Ecom accuracy for non-targeted metabolomics.

Quantitative biases in the abundance of precursor and product ions due to mass discrimination in RF-only ion guides results in inaccurate collision induced dissociation (CID) spectra. We evaluated the effects of collision cell RF voltage and collision energy on CID spectra using ten singly protonated compounds (46-854 Da) in an orthogonal acceleration time-of-flight mass spectrometer. The relative ion transfer efficiency, i.

Development of a two-step indirect method for modeling Ecom50.

A novel approach is developed for modeling situations in which the modeled property is an algebraically transformed version of the original experimental data. In many cases such a transformation results in a data set with a significantly smaller data range. Here we explore the effects of range-of-data on modeling statistics.

In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics.

Current methods of structure identification in mass-spectrometry-based nontargeted metabolomics rely on matching experimentally determined features of an unknown compound to those of candidate compounds contained in biochemical databases. A major limitation of this approach is the relatively small number of compounds currently included in these databases. If the correct structure is not present in a database, it cannot be identified, and if it cannot be identified, it cannot be included in a database.

BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space.

The structural identification of unknown biochemical compounds in complex biofluids continues to be a major challenge in metabolomics research. Using LC/MS, there are currently two major options for solving this problem: searching small biochemical databases, which often do not contain the unknown of interest or searching large chemical databases which include large numbers of nonbiochemical compounds. Searching larger chemical databases (larger chemical space) increases the odds of identifying an unknown biochemical compound, but only if nonbiochemical structures can be eliminated from consideration.

Chemical structure identification in metabolomics: computational modeling of experimental features.

The identification of compounds in complex mixtures remains challenging despite recent advances in analytical techniques. At present, no single method can detect and quantify the vast array of compounds that might be of potential interest in metabolomics studies. High performance liquid chromatography/mass spectrometry (HPLC/MS) is often considered the analytical method of choice for analysis of biofluids.

MolFind: a software package enabling HPLC/MS-based identification of unknown chemical structures.

In this paper, we present MolFind, a highly multithreaded pipeline type software package for use as an aid in identifying chemical structures in complex biofluids and mixtures. MolFind is specifically designed for high-performance liquid chromatography/mass spectrometry (HPLC/MS) data inputs typical of metabolomics studies where structure identification is the ultimate goal. MolFind enables compound identification by matching HPLC/MS-based experimental data obtained for an unknown compound with computationally derived HPLC/MS values for candidate compounds downloaded from chemical databases such as PubChem.

Correlation of Ecom50 values between mass spectrometers: effect of collision cell radiofrequency voltage on calculated survival yield.

Rationale: The determination of the center-of-mass energy at which 50% of a precursor ion decomposes (Ecom(50)) during collision-induced dissociation (CID) is dependent on the chemical structure of the ion as well as the physical and electrical characteristics of the collision cell. The current study was designed to identify variables influencing Ecom(50) values measured on four different mass spectrometers.

Methods: Fifteen test compounds were protonated using + ve electrospray ionization and the resulting ions were fragmented across a range of collision energies by CID.

Development of Ecom₅₀ and retention index models for nontargeted metabolomics: identification of 1,3-dicyclohexylurea in human serum by HPLC/mass spectrometry.

The goal of many metabolomic studies is to identify the molecular structure of endogenous molecules that are differentially expressed among sampled or treatment groups. The identified compounds can then be used to gain an understanding of disease mechanisms. Unfortunately, despite recent advances in a variety of analytical techniques, small molecule (<1000 Da) identification remains difficult.

Database searching for structural identification of metabolites in complex biofluids for mass spectrometry-based metabonomics.

MS and HPLC are commonly used for compound characterization and obtaining structural information; in the field of metabonomics, these two analytical techniques are often combined to characterize unknown endogenous or exogenous metabolites present in complex biological samples. Since the structures of a majority of these metabolites are not actually identified, the result of most metabonomic studies is a list of m/z values and retention times. However, without knowing actual structures, the biological significance of these 'features' cannot be determined.

CE50: quantifying collision induced dissociation energy for small molecule characterization and identification.

Survival yield analysis is routinely used in mass spectroscopy as a tool for assessing precursor ion stability and internal energy. Because ion internal energy and decomposition reaction rates are dependent on chemical structure, we reasoned that survival yield curves should be compound-specific and therefore useful for chemical identification. In this study, a quantitative approach for analyzing the correlation between survival yield and collision energy was developed and validated.

Prediction of HPLC retention index using artificial neural networks and IGroup E-state indices.

A back-propagation artificial neural network (ANN) was used to create a 10-fold leave-10%-out cross-validated ensemble model of high performance liquid chromatography retention index (HPLC-RI) for a data set of 498 diverse druglike compounds. A 10-fold multiple linear regression (MLR) ensemble model of the same data was developed for comparison. Molecular structure was described using IGroup E-state indices, a novel set of structure-information representation (SIR) descriptors, along with molecular connectivity chi and kappa indices and other SIR descriptors previously reported.

Mass spectral metabonomics beyond elemental formula: chemical database querying by matching experimental with computational fragmentation spectra.

Despite recent advances in NMR and mass spectrometry, the structural identification of organic compounds in complex biofluids remains a significant analytical challenge. For mass spectroscopy applications, chemical identification is generally limited to determination of elemental formula. Here we test the hypothesis that unknown chemical structures can be determined by matching their experimental collision-induced dissociation (CID) fragmentation spectra with computational fragmentation spectra of compounds retrieved from chemical databases.

A semiparametric modeling framework for potential biomarker discovery and the development of metabonomic profiles.

Background: The discovery of biomarkers is an important step towards the development of criteria for early diagnosis of disease status. Recently electrospray ionization (ESI) and matrix assisted laser desorption (MALDI) time-of-flight (TOF) mass spectrometry have been used to identify biomarkers both in proteomics and metabonomics studies. Data sets generated from such studies are generally very large in size and thus require the use of sophisticated statistical techniques to glean useful information.

Alcohol dehydrogenase and catalase content in perinatal infant and adult livers: potential influence on neonatal alcohol metabolism.

Methanol and ethanol are primarily metabolized through the alcohol dehydrogenase (ADH) system in adults. Under saturating substrate concentrations, blood alcohol concentrations decline at a constant rate (i.e.

NMR and HPLC-MS/MS analysis of synthetically prepared linoleic acid diol glucuronides.

Hydroxylated fatty acids are important mediators of many physiological and pathophysiological processes in a variety of human tissues. Recent evidence shows that in humans many of these are ultimately excreted in the urine as the glucuronide conjugates. In this paper we describe a general approach for the chemical synthesis of glucuronide conjugate derivatives of fatty acids.

The use of plasma metanephrine to normetanephrine ratio to determine epinephrine poisoning.

Background: Intravenous epinephrine (EPI) is used as a pharmacologic agent to acutely treat patients in cardiac arrest. Unfortunately, there have been several homicide cases where hospitalized patients died due to a purposeful overdose of epinephrine. We measured plasma epinephrine metabolites (metanephrine, MET, and normetanephrine, NMET) to determine if exogenous epinephrine can be distinguished from endogenous epinephrine concentrations in a controlled animal study.

Contribution of acetaminophen-cysteine to acetaminophen nephrotoxicity II. Possible involvement of the gamma-glutamyl cycle.

Acetaminophen (APAP) nephrotoxicity has been observed both in humans and research animals. Our recent investigations have focused on the possible involvement of glutathione-derived APAP metabolites in APAP nephrotoxicity and have demonstrated that administration of acetaminophen-cysteine (APAP-CYS) potentiated APAP-induced renal injury with no effects on APAP-induced liver injury. Additionally, APAP-CYS treatment alone resulted in a dose-responsive renal GSH depletion.