Structural mass spectrometry: rapid methods for separation and analysis of peptide natural products

Exploring the Effects of Cyclosporin A to Isocyclosporin A Rearrangement on Ion Mobility Separation

Cyclosporin A (CycA) is a peptide secondary metabolite derived from fungi that plays a crucial role in transplantation surgery. Cyclic traveling wave ion mobility mass spectrometry (IM-MS) revealed an N → O peptidyl shift in singly protonated CycA to isocyclosporin A (isoA), whereas no such isomerization was observed for doubly protonated and sodiated molecules. CycA and isoA were able to be separated by considering doubly protonated precursors using a specific ion fragment. In parallel, sodium ion stabilization facilitated the simultaneous separation and quantitation of singly charged cyclosporin isomers with the limit of detection and coefficient of determination of 1.3% and 0.9908 for CycA in isoA and 1.0% and 0.9830 for isoA in CycA, respectively. Finally, 1H-13C gHSQC NMR experiments permitted parallel recording of up to 11 cyclosporin conformers. The ratios were determined by integrating the volume of cross-peaks of the upfield resonating hydrogen in the diastereotopic methylene group of sarcosine-3.

Advances in Mass Spectrometry-Metabolomics Based Approaches

Highly selective and sensitive analytical techniques are necessary for microbial metabolomics due to the complexity of the microbial sample matrix. Hence, mass spectrometry (MS) has been successfully applied in microbial metabolomics due to its high precision, versatility, sensitivity, and wide dynamic range. The different analytical tools using MS have been employed in microbial metabolomics investigations and can contribute to the discovery or accelerate the search for bioactive substances. The coupling with chromatographic and electrophoretic separation techniques has resulted in more efficient technologies for the analysis of microbial compounds occurring in trace levels. This book chapter describes the current advances in the application of mass spectrometry-based metabolomics in the search for new biologically active agents from microbial sources; the development of new approaches for in silico annotation of natural products; the different technologies employing mass spectrometry imaging to deliver more comprehensive analysis and elucidate the metabolome involved in ecological interactions as they enable visualization of the spatial dispersion of small molecules. We also describe other ambient ionization techniques applied to the fingerprint of microbial natural products and modern techniques such as ion mobility mass spectrometry used to microbial metabolomic analyses and the dereplication of natural microbial products through MS.

Ion Mobility Mass Spectrometry Development and Applications

Although as an analytical method with high specificity and high sensitivity, mass spectrometry (MS) has a wide range of applications in many fields, it still needs other technologies as the assist and supplement to enhance the scope and capability of analysis. Coupling with ion mobility (IM) can make an enhancement effect in the field of pharmaceutical analysis as a supplementary method. The two-dimensional mass technology improves the confidence of compounds annotations while increasing peak capacity, with the gradual deepening of theoretical research on IM-MS, it has shown unique advantages in the complex analysis conditions. IM-MS owns great potential for improving the depth, range, dimension of in-depth drug research. In this review, the principle, instruments and methods, applications, advantages and limitations of IM-MS are described. Here, we also elaborate on the prospects in structural evaluation, separation, and identification of complex compounds for the drug discovery and development phase and the great advantages of macromolecules and omics.

Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning

The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, transformation, excretion, and coexistence with other endogenous molecules, however, greatly complicate the interpretation of features detected in nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites from xenobiotics is also often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate a xenobiotic structural annotation workflow using ion mobility spectrometry coupled with MS (IMS-MS), mass defect filtering, and machine learning to uncover potential xenobiotic classes and species in large metabolomic feature lists. Xenobiotic classes examined included those of known high toxicities, including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and pesticides. Specifically, when the workflow was applied to identify PFAS in the NIST SRM 1957 and 909c human serum samples, it greatly reduced the hundreds of detected liquid chromatography (LC)-IMS-MS features by utilizing both mass defect filtering and m/z versus IMS collision cross sections relationships. These potential PFAS features were then compared to the EPA CompTox entries, and while some matched within specific m/z tolerances, there were still many unknowns illustrating the importance of nontargeted studies for detecting new molecules with known chemical characteristics. Additionally, this workflow can also be utilized to evaluate other xenobiotics and enable more confident annotations from nontargeted studies.

Multidimensional Separations of Intact Phase II Steroid Metabolites Utilizing LC-Ion Mobility-HRMS

The detection and unambiguous identification of anabolic-androgenic steroid metabolites are essential in clinical, forensic, and antidoping analyses. Recently, sulfate phase II steroid metabolites have received increased attention in steroid metabolism and drug testing. In large part, this is because phase II steroid metabolites are excreted for an extended time, making them a potential long-term chemical marker of choice for tracking steroid misuse in sports. Comprehensive analytical methods, such as liquid chromatography-tandem mass spectrometry (LC-MS/MS), have been used to detect and identify glucuronide and sulfate steroids in human urine with high sensitivity and reliability. However, LC-MS/MS identification strategies can be hindered by the fact that phase II steroid metabolites generate nonselective ion fragments across the different metabolite markers, limiting the confidence in metabolite identifications that rely on exact mass measurement and MS/MS information. Additionally, liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is sometimes insufficient at fully resolving the analyte peaks from the sample matrix (commonly urine) chemical noise, further complicating accurate identification efforts. Therefore, we developed a liquid chromatography-ion mobility-high resolution mass spectrometry (LC-IM-HRMS) method to increase the peak capacity and utilize the IM-derived collision cross section (CCS) values as an additional molecular descriptor for increased selectivity and to improve identifications of intact steroid analyses at low concentrations.

Structural Flexibility of Cyclosporine A Is Mediated by Amide Cis-Trans Isomerization and the Chameleonic Roles of Calcium

Falling outside of Lipinski's rule of five, macrocyclic drugs have accessed unique binding sites of their target receptors unreachable by traditional small molecules. Cyclosporin(e) A (CycA), an extensively studied macrocyclic natural product, is an immunosuppressant with undesirable side effects such as electrolytic imbalances. In this work, a comprehensive view on the conformational landscape of CycA, its interactions with Ca²⁺, and host-guest interactions with cyclophilin A (CypA) is reported through exhaustive analyses that combine ion-mobility spectrometry-mass spectrometry (IMS-MS), nuclear magnetic resonance (NMR) spectroscopy, distance-geometry modeling, and NMR-driven molecular dynamics. Our IMS-MS data show that CycA can adopt extremely compact conformations with significantly smaller collisional cross sections than the closed conformation observed in CDCl₃. To adopt these conformations, the macrocyclic ring has to twist and bend via cis-trans isomerization of backbone amides, and thus, we termed this family of structures the "bent" conformation. Furthermore, NMR measurements indicate that the closed conformation exists at 19% in CD₃OD/H₂O and 55% in CD₃CN. However, upon interacting with Ca²⁺, in addition to the bent and previously reported closed conformations of free CycA, the CycA:Ca²⁺ complex is open and has all-trans peptide bonds. Previous NMR studies using calcium perchlorate reported only the closed conformation of CycA (which contains one cis peptide bond). Here, calcium chloride, a more biologically relevant salt, was used, and interestingly, it helps converting the cis-MeLeu9-MeLeu10 peptide bond into a trans bond. Last, we were able to capture the native binding of CycA and CypA to give forth evidence that IMS-MS is able to probe the solution-phase structures of the complexes and that the Ca²⁺:CycA complex may play an essential role in the binding of CycA to CypA.

Metabolite identification using an ion mobility enhanced data-independent acquisition strategy and automated data processing

RATIONALE

Liquid chromatography/mass spectrometry is an essential tool for efficient and reliable quantitative and qualitative analysis and underpins much of contemporary drug metabolism and pharmacokinetics. Data-independent acquisition methods such as MS^E have reduced the potential to miss metabolites, but do not formally generate quadrupole-resolved product ion spectra. The addition of ion mobility separation to these approaches, for example, in High-Definition MS^E (HDMS^E ) has the potential to reduce the time needed to set up an experiment and maximize the chance that all metabolites present can be resolved and characterized. We compared High-Definition Data-Dependent Acquisition (HD-DDA), MS^E and HDMS^E approaches using automated software processing with Mass-MetaSite and WebMetabase.

METHODS

Metabolite identification was performed on incubations of glucagon-like peptide-1 (7-37) (GLP-1) and verapamil hydrochloride. The HD-DDA, MS^E and HDMS^E experiments were conducted on a Waters ACQUITY UPLC I-Class LC system with a VION IMS quadrupole time-of-flight (QTOF) mass spectrometer operating under UNIFI control. All acquired data were processed using MassMetaSite able to read data from UNIFI 1.9.4. WebMetabase was used to review the detected chromatographic peaks and the spectral data interpretations.

RESULTS

A comparison of outcomes obtained for MS^E and HDMS^E data demonstrated that the same structures were proposed for metabolites of both verapamil and GLP-1. The ratio of structurally matched to mismatched product ions found by MassMetaSite was slightly greater for HDMS^E than for MS^E , and HD-DDA, thus improving confidence in the structures proposed through the addition of ion mobility based data acquisitions_. CONCLUSIONS: HDMS^E data acquisition is an effective approach for the elucidation of metabolite structures for both small molecules and peptides, with excellent accuracy and quality, requiring minimal tailoring for the compound under investigation.

Rapid Characterization of Per- and Polyfluoroalkyl Substances (PFAS) by Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS)

Per- and polyfluoroalkyl substances (PFAS) are an ensemble of persistent organic pollutants of global interest because of their associations with adverse health outcomes. Currently, environmental PFAS pollution is prolific as a result of the widespread manufacturing of these compounds and their chemical persistence. In this work, we demonstrate the advantages of adding ion mobility spectrometry (IMS) separation to existing LC-MS workflows for PFAS analysis. Using a commercially available drift tube IMS-MS, we characterized PFAS species and isomeric content in both analytical standards and environmental water samples. Molecular trendlines based on intrinsic mass and structural relationships were also explored for individual PFAS subclasses (e.g. PFSA, PFCA, etc.). Results from rapid IMS-MS analyses provided a link between mass and collision cross sections (CCS) for specific PFAS families and are linked to compositional differences in molecular structure. In addition, CCS values provide additional confidence of annotating prioritized features in untargeted screening studies for potential environmental pollutants. Results from this study show that the IMS separation provides novel information to support traditional LC-MS PFAS analyses and will greatly benefit the evaluation of unknown pollutants in future environmental studies.

Challenges in Identifying the Dark Molecules of Life

Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.

Isolation of depsipeptides and optimization for enhanced production of valinomycin from the North-Western Himalayan cold desert strain Streptomyces lavendulae

Exploration of microbial dynamics of Streptomyces lavendulae ACR-DA1, a psychrotrophic isolate from the North-Western Himalayan cold desert, was carried out using matrix-assisted laser desorbtion ionisation-time of flight mass spectrometer. Valinomycin was found as a major produce and cyclic depsipeptide montanastatin as a minor produce. The yield of the valinomycin was found to be 0.3 mg l^-1 in submerged growth condition at the batch scale. Miniaturization of optimization experiments was adept to maximize the production using the expeditious and efficient technique of intact cell mass spectrometry. The present study showed that using optimized conditions and growing the culture in synthetic mineral base starch medium at 10 °C enhanced the production to 19.4 mg l^-1. Our results demonstrated 64-fold increase in yield from the wild-type S. lavendulae ACR-DA1 strain using a simple and economical downstream process.

Collision cross section compendium to annotate and predict multi-omic compound identities

Ion mobility mass spectrometry (IM-MS) expands the analyte coverage of existing multi-omic workflows by providing an additional separation dimension as well as a parameter for characterization and identification of molecules - the collision cross section (CCS). This work presents a large, Unified CCS compendium of >3800 experimentally acquired CCS values obtained from traceable molecular standards and measured with drift tube ion mobility-mass spectrometers. An interactive visualization of this compendium along with data analytic tools have been made openly accessible. Represented in the compendium are 14 structurally-based chemical super classes, consisting of a total of 80 classes and 157 subclasses. Using this large data set, regression fitting and predictive statistics have been performed to describe mass-CCS correlations specific to each chemical ontology. These structural trends provide a rapid and effective filtering method in the traditional untargeted workflow for identification of unknown biochemical species. The utility of the approach is illustrated by an application to metabolites in human serum, quantified trends of which were used to assess the probability of an unknown compound belonging to a given class. CCS-based filtering narrowed the chemical search space by 60% while increasing the confidence in the remaining isomeric identifications from a single class, thus demonstrating the value of integrating predictive analyses into untargeted experiments to assist in identification workflows. The predictive abilities of this compendium will improve in specificity and expand to more chemical classes as additional data from the IM-MS community is contributed. Instructions for data submission to the compendium and criteria for inclusion are provided.

Untargeted Molecular Discovery in Primary Metabolism: Collision Cross Section as a Molecular Descriptor in Ion Mobility-Mass Spectrometry

In this work, we established a collision cross section (CCS) library of primary metabolites based on analytical standards in the Mass Spectrometry Metabolite Library of Standards (MSMLS) using a commercially available ion mobility-mass spectrometer (IM-MS). From the 554 unique compounds in the MSMLS plate library, we obtained a total of 1246 CCS measurements over a wide range of biochemical classes and adduct types. Resulting data analysis demonstrated that the curated CCS library provides broad molecular coverage of metabolic pathways and highlights intrinsic mass-mobility relationships for specific metabolite superclasses. The separation and characterization of isomeric metabolites were assessed, and all molecular species contained within the plate library, including isomers, were critically evaluated to determine the analytical separation efficiency in both the mass ( m/ z) and mobility (CCS/ΔCCS) dimension required for untargeted metabolomic analyses. To further demonstrate the analytical utility of CCS as an additional molecular descriptor, a well-characterized biological sample of human plasma serum (NIST SRM 1950) was examined by LC-IM-MS and used to provide a detailed isomeric analysis of carbohydrate constituents by ion mobility.

Improving the discovery of secondary metabolite natural products using ion mobility-mass spectrometry

Secondary metabolite discovery requires an unbiased, comprehensive workflow to detect unknown unknowns for which little to no molecular knowledge exists. Untargeted mass spectrometry-based metabolomics is a powerful platform, particularly when coupled with ion mobility for high-throughput gas-phase separations to increase peak capacity and obtain gas-phase structural information. Ion mobility data are described by the amount of time an ion spends in the drift cell, which is directly related to an ion's collision cross section (CCS). The CCS parameter describes the size, shape, and charge of a molecule and can be used to characterize unknown metabolomic species. Here, we describe current and emerging applications of ion mobility-mass spectrometry for prioritization, discovery and structure elucidation, and spatial/temporal characterization.

An Electrospray Ionization Mass Spectrometry Study on the "In Vacuo" Hetero-Oligomers Formed by the Antimicrobial Peptides, Surfactin and Gramicidin S

It was previously observed that the lipopeptide surfactants in surfactin (Srf) have an antagonistic action towards the highly potent antimicrobial cyclodecapeptide, gramicidin S (GS). This study reports on some of the molecular aspects of the antagonism as investigated through complementary electrospray ionization mass spectrometry techniques. We were able to detect stable 1:1 and 2:1 hetero-oligomers in a mixture of surfactin and gramicidin S. The noncovalent interaction between GS and Srf, with the proposed equilibrium: GS~Srf↔GS+Srf correlated to apparent K _d values of 6-9 μM in gas-phase and 1 μM in aqueous solution. The apparent K _d values decreased with a longer incubation time and indicated a slow oligomerization equilibrium. Furthermore, the low μM K _d^app values of GS~Srf↔GS+Srf fell within the biological concentration range and related to the 2- to 3-fold increase in [GS] needed for bacterial growth inhibition in the presence of Srf. Competition studies indicated that neither Na⁺ nor Ca²⁺ had a major effect on the stability of preformed heterodimers and that GS in fact out-competed Ca²⁺ and Na⁺ from Srf. Traveling wave ion mobility mass spectrometry revealed near symmetrical peaks of the heterodimers correlating to a compact dimer conformation that depend on specific interactions. Collision-induced dissociation studies indicated that the peptide interaction is most probably between one Orn residue in GS and the Asp residue, but not the Glu residue in Srf. We propose that flanking hydrophobic residues in both peptides stabilize the antagonistic and inactive peptide hetero-oligomers and shield the specific polar interactions in an aqueous environment. Graphical Abstract ᅟ.

Ion Mobility Collision Cross Section Compendium

In this review, we focus on an important aspect of ion mobility (IM) research, namely the reporting of quantitative ion mobility measurements in the form of the gas-phase collision cross section (CCS), which has provided a common basis for comparison across different instrument platforms and offers a unique form of structural information, namely size and shape preferences of analytes in the absence of bulk solvent. This review surveys the over 24,000 CCS values reported from IM methods spanning the era between 1975 to 2015, which provides both a historical and analytical context for the contributions made thus far, as well as insight into the future directions that quantitative ion mobility measurements will have in the analytical sciences. The analysis was conducted in 2016, so CCS values reported in that year are purposely omitted. In another few years, a review of this scope will be intractable, as the number of CCS values which will be reported in the next three to five years is expected to exceed the total amount currently published in the literature.

Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites

Covering: 2000 to 2016The labor-intensive process of microbial natural product discovery is contingent upon identifying discrete secondary metabolites of interest within complex biological extracts, which contain inventories of all extractable small molecules produced by an organism or consortium. Historically, compound isolation prioritization has been driven by observed biological activity and/or relative metabolite abundance and followed by dereplication via accurate mass analysis. Decades of discovery using variants of these methods has generated the natural pharmacopeia but also contributes to recent high rediscovery rates. However, genomic sequencing reveals substantial untapped potential in previously mined organisms, and can provide useful prescience of potentially new secondary metabolites that ultimately enables isolation. Recently, advances in comparative metabolomics analyses have been coupled to secondary metabolic predictions to accelerate bioactivity and abundance-independent discovery work flows. In this review we will discuss the various analytical and computational techniques that enable MS-based metabolomic applications to natural product discovery and discuss the future prospects for comparative metabolomics in natural product discovery.

High-throughput platforms for metabolomics

Mass spectrometry has become a choice method for broad-spectrum metabolite analysis in both fundamental and applied research. This can range from comprehensive analysis achieved through time-consuming chromatography to the rapid analysis of a few target metabolites without chromatography. In this review article, we highlight current high-throughput MS-based platforms and their potential application in metabolomics. Although current MS platforms can reach throughputs up to 0.5 seconds per sample, the metabolite coverage of these platforms are low compared to low-throughput, separation-based MS methods. High-throughput comes at a cost, as it's a trade-off between sample throughput and metabolite coverage. As we will discuss, promising emerging technologies, including microfluidics and miniaturization of separation techniques, have the potential to achieve both rapid and more comprehensive metabolite analysis.

Systems-Wide High-Dimensional Data Acquisition and Informatics Using Structural Mass Spectrometry Strategies

BACKGROUND

Untargeted multiomics data sets are obtained for samples in systems, synthetic, and chemical biology by integrating chromatographic separations with ion mobility-mass spectrometry (IM-MS) analysis. The data sets are interrogated using bioinformatics strategies to organize the data for identification prioritization.

CONTENT

The use of big data approaches for data mining of massive data sets in systems-wide analyses is presented. Untargeted biological data across multiomics dimensions are obtained using a variety of chromatography strategies with structural MS. Separation timescales for different techniques and the resulting data deluge when combined with IM-MS are presented. Data mining self-organizing map strategies are used to rapidly filter the data, highlighting those features describing uniqueness to the query. Examples are provided in longitudinal analyses in synthetic biology and human liver exposure to acetaminophen, and in chemical biology for natural product discovery from bacterial biomes.

CONCLUSIONS

Matching the separation timescales of different forms of chromatography with IM-MS provides sufficient multiomics selectivity to perform untargeted systems-wide analyses. New data mining strategies provide a means for rapidly interrogating these data sets for feature prioritization and discovery in a range of applications in systems, synthetic, and chemical biology.

Structuring Microbial Metabolic Responses to Multiplexed Stimuli via Self-Organizing Metabolomics Maps

Secondary metabolite biosynthesis in microorganisms responds to discrete chemical and biological stimuli; however, untargeted identification of these responses presents a significant challenge. Herein we apply multiplexed stimuli to Streptomyces coelicolor and collect the resulting response metabolomes via ion mobility-mass spectrometric analysis. Self-organizing map (SOM) analytics adapted for metabolomic data demonstrate efficient characterization of the subsets of primary and secondary metabolites that respond similarly across stimuli. Over 60% of all metabolic features inventoried from responses are either not observed under control conditions or produced at greater than 2-fold increase in abundance in response to at least one of the multiplexing conditions, reflecting how metabolites encode phenotypic changes in an organism responding to multiplexed challenges. Using abundance as an additional filter, each of 16 known S. coelicolor secondary metabolites is prioritized via SOM and observed at increased levels (1.2- to 22-fold compared with unperturbed) in response to one or more challenge conditions.

Distance geometry protocol to generate conformations of natural products to structurally interpret ion mobility-mass spectrometry collision cross sections

Ion mobility-mass spectrometry (IM-MS) allows the separation of ionized molecules based on their charge-to-surface area (IM) and mass-to-charge ratio (MS), respectively. The IM drift time data that is obtained is used to calculate the ion-neutral collision cross section (CCS) of the ionized molecule with the neutral drift gas, which is directly related to the ion conformation and hence molecular size and shape. Studying the conformational landscape of these ionized molecules computationally provides interpretation to delineate the potential structures that these CCS values could represent, or conversely, structural motifs not consistent with the IM data. A challenge in the IM-MS community is the ability to rapidly compute conformations to interpret natural product data, a class of molecules exhibiting a broad range of biological activity. The diversity of biological activity is, in part, related to the unique structural characteristics often observed for natural products. Contemporary approaches to structurally interpret IM-MS data for peptides and proteins typically utilize molecular dynamics (MD) simulations to sample conformational space. However, MD calculations are computationally expensive, they require a force field that accurately describes the molecule of interest, and there is no simple metric that indicates when sufficient conformational sampling has been achieved. Distance geometry is a computationally inexpensive approach that creates conformations based on sampling different pairwise distances between the atoms within the molecule and therefore does not require a force field. Progressively larger distance bounds can be used in distance geometry calculations, providing in principle a strategy to assess when all plausible conformations have been sampled. Our results suggest that distance geometry is a computationally efficient and potentially superior strategy for conformational analysis of natural products to interpret gas-phase CCS data.

Ion mobility-mass spectrometry strategies for untargeted systems, synthetic, and chemical biology

Contemporary strategies that concentrate on only one or a handful of molecular targets limits the utility of the information gained for diagnostic and predictive purposes. Recent advances in the sensitivity, speed, and precision of measurements obtained from ion mobility coupled to mass spectrometry (IM-MS) have accelerated the utility of IM-MS in untargeted, discovery-driven studies in biology. Perhaps most evident is the impact that such wide-scale discovery capabilities have yielded in the areas of systems, synthetic, and chemical biology, where the need for comprehensive, hypothesis-driving studies from multidimensional and unbiased data is required.

Mass spectrometry of natural products: current, emerging and future technologies

Although mass spectrometry is a century old technology, we are entering into an exciting time for the analysis of molecular information directly from complex biological systems. In this Highlight, we feature emerging mass spectrometric methods and tools used by the natural product community and give a perspective of future directions where the mass spectrometry field is migrating towards over the next decade.

Amino acids as metabolic substrates during cardiac ischemia

The heart is well known as a metabolic omnivore in that it is capable of consuming fatty acids, glucose, ketone bodies, pyruvate, lactate, amino acids and even its own constituent proteins, in order of decreasing preference. The energy from these substrates supports not only mechanical contraction, but also the various transmembrane pumps and transporters required for ionic homeostasis, electrical activity, metabolism and catabolism. Cardiac ischemia - for example, due to compromise of the coronary vasculature or end-stage heart failure - will alter both electrical and metabolic activity. While the effects of myocardial ischemia on electrical propagation and stability have been studied in depth, the effects of ischemia on metabolic substrate preference has not been fully appreciated: oxygen deprivation during ischemia will significantly alter the relative ability of the heart to utilize each of these substrates. Although changes in cardiac metabolism are understood to be an underlying component in almost all cardiac myopathies, the potential contribution of amino acids in maintaining cardiac electrical conductance and stability during ischemia is underappreciated. Despite clear evidence that amino acids exert cardioprotective effects in ischemia and other cardiac disorders, their role in the metabolism of the ischemic heart has yet to be fully elucidated. This review synthesizes the current literature of the metabolic contribution of amino acids during ischemia by analyzing relevant historical and recent research.