Cody R. Goodwin

Conformational ordering of biomolecules in the gas phase: nitrogen collision cross sections measured on a prototype high resolution drift tube ion mobility-mass spectrometer.

Ion mobility-mass spectrometry measurements which describe the gas-phase scaling of molecular size and mass are of both fundamental and pragmatic utility. Fundamentally, such measurements expand our understanding of intrinsic intramolecular folding forces in the absence of solvent. Practically, reproducible transport properties, such as gas-phase collision cross-section (CCS), are analytically useful metrics for identification and characterization purposes. Here, we report 594 CCS values obtained in nitrogen drift gas on an electrostatic drift tube ion mobility-mass spectrometry (IM-MS) instrument. The instrument platform is a newly developed prototype incorporating a uniform-field drift tube bracketed by electrodynamic ion funnels and coupled to a high resolution quadrupole time-of-flight mass spectrometer. The CCS values reported here are of high experimental precision (±0.5% or better) and represent four chemically distinct classes of molecules (quaternary ammonium salts, lipids, peptides, and carbohydrates), which enables structural comparisons to be made between molecules of different chemical compositions for the rapid “omni-omic” characterization of complex biological samples. Comparisons made between helium and nitrogen-derived CCS measurements demonstrate that nitrogen CCS values are systematically larger than helium values; however, general separation trends between chemical classes are retained regardless of the drift gas. These results underscore that, for the highest CCS accuracy, care must be exercised when utilizing helium-derived CCS values to calibrate measurements obtained in nitrogen, as is the common practice in the field.

Engineering Challenges for Instrumenting and Controlling Integrated Organ-on-Chip Systems

The sophistication and success of recently reported microfabricated organs-on-chips and human organ constructs have made it possible to design scaled and interconnected organ systems that may significantly augment the current drug development pipeline and lead to advances in systems biology. Physiologically realistic live microHuman (μHu) and milliHuman (mHu) systems operating for weeks to months present exciting and important engineering challenges such as determining the appropriate size for each organ to ensure appropriate relative organ functional activity, achieving appropriate cell density, providing the requisite universal perfusion media, sensing the breadth of physiological responses, and maintaining stable control of the entire system, while maintaining fluid scaling that consists of ~5 mL for the mHu and ~5 μL for the μHu. We believe that successful mHu and μHu systems for drug development and systems biology will require low-volume microdevices that support chemical signaling, microfabricated pumps, valves and microformulators, automated optical microscopy, electrochemical sensors for rapid metabolic assessment, ion mobility-mass spectrometry for real-time molecular analysis, advanced bioinformatics, and machine learning algorithms for automated model inference and integrated electronic control. Toward this goal, we are building functional prototype components and are working toward top-down system integration.

Neurovascular unit on a chip: implications for translational applications

The blood-brain barrier (BBB) dynamically controls exchange between the brain and the body, but this interaction cannot be studied directly in the intact human brain or sufficiently represented by animal models. Most existing in vitro BBB models do not include neurons and glia with other BBB elements and do not adequately predict drug efficacy and toxicity. Under the National Institutes of Health Microtissue Initiative, we are developing a three-dimensional, multicompartment, organotypic microphysiological system representative of a neurovascular unit of the brain. The neurovascular unit system will serve as a model to study interactions between the central nervous system neurons and the cerebral spinal fluid (CSF) compartment, all coupled to a realistic blood-surrogate supply and venous return system that also incorporates circulating immune cells and the choroid plexus. Hence all three critical brain barriers will be recapitulated: blood-brain, brain-CSF, and blood-CSF. Primary and stem cell-derived human cells will interact with a variety of agents to produce critical chemical communications across the BBB and between brain regions. Cytomegalovirus, a common herpesvirus, will be used as an initial model of infections regulated by the BBB. This novel technological platform, which combines innovative microfluidics, cell culture, analytical instruments, bioinformatics, control theory, neuroscience, and drug discovery, will replicate chemical communication, molecular trafficking, and inflammation in the brain. The platform will enable targeted and clinically relevant nutritional and pharmacologic interventions for or prevention of such chronic diseases as obesity and acute injury such as stroke, and will uncover potential adverse effects of drugs. If successful, this project will produce clinically useful technologies and reveal new insights into how the brain receives, modifies, and is affected by drugs, other neurotropic agents, and diseases.

Antimicrobial drug resistance affects broad changes in metabolomic phenotype in addition to secondary metabolism

Bacteria develop resistance to many classes of antibiotics vertically, by engendering mutations in genes encoding transcriptional and translational apparatus. These severe adaptations affect global transcription, translation, and the correspondingly affected metabolism. Here, we characterize metabolome scale changes in transcriptional and translational mutants in a genomically characterized Nocardiopsis, a soil-derived actinomycete, in stationary phase. Analysis of ultra-performance liquid chromatography–ion mobility–mass spectrometry metabolomic features from a cohort of streptomycin- and rifampicin-resistant mutants grown in the absence of antibiotics exhibits clear metabolomic speciation, and loadings analysis catalogs a marked change in metabolic phenotype. Consistent with derepression, up to 311 features are observed in antibiotic-resistant mutants that are not detected in their progenitors. Mutants demonstrate changes in primary metabolism, such as modulation of fatty acid composition and the increased production of the osmoprotectant ectoine, in addition to the presence of abundant emergent potential secondary metabolites. Isolation of three of these metabolites followed by structure elucidation demonstrates them to be an unusual polyketide family with a previously uncharacterized xanthene framework resulting from sequential oxidative carbon skeletal rearrangements. Designated as “mutaxanthenes,” this family can be correlated to a type II polyketide gene cluster in the producing organism. Taken together, these data suggest that biosynthetic pathway derepression is a general consequence of some antibiotic resistance mutations.

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.

Phenotypic Mapping of Metabolic Profiles Using Self-Organizing Maps of High-Dimensional Mass Spectrometry Data

A metabolic system is composed of inherently interconnected metabolic precursors, intermediates, and products. The analysis of untargeted metabolomics data has conventionally been performed through the use of comparative statistics or multivariate statistical analysis-based approaches; however, each falls short in representing the related nature of metabolic perturbations. Herein, we describe a complementary method for the analysis of large metabolite inventories using a data-driven approach based upon a self-organizing map algorithm. This workflow allows for the unsupervised clustering, and subsequent prioritization of, correlated features through Gestalt comparisons of metabolic heat maps. We describe this methodology in detail, including a comparison to conventional metabolomics approaches, and demonstrate the application of this method to the analysis of the metabolic repercussions of prolonged cocaine exposure in rat sera profiles.

Structural Mass Spectrometry: Rapid Methods for Separation and Analysis of Peptide Natural Products

A significant challenge in natural product discovery is the initial discrimination of discrete secondary metabolites alongside functionally similar primary metabolic cellular components within complex biological samples. A property that has yet to be fully exploited for natural product identification and characterization is the gas-phase collision cross section, or, more generally, the mobility-mass correlation. Peptide natural products possess many of the properties that distinguish natural products, as they are frequently characterized by a high degree of intramolecular bonding and possess extended and compact conformations among other structural modifications. This report describes a rapid structural mass spectrometry technique based on ion mobility-mass spectrometry for the comparison of peptide natural products to their primary metabolic congeners using mobility-mass correlation. This property is empirically determined using ion mobility-mass spectrometry, applied to the analysis of linear versus modified peptides, and used to discriminate peptide natural products in a crude microbial extract. Complementary computational approaches are utilized to understand the structural basis for the separation of primary metabolism derived linear peptides from secondary metabolite cyclic and modified cyclic species. These findings provide a platform for enhancing the identification of secondary metabolic peptides with distinct mobility-mass ratios within complex biological samples.

Metabolic consequences of interleukin-6 challenge in developing neurons and astroglia

BackgroundMaternal immune activation and subsequent interleukin-6 (IL-6) induction disrupt normal brain development and predispose the offspring to developing autism and schizophrenia. While several proteins have been identified as having some link to these developmental disorders, their prevalence is still small and their causative role, if any, is not well understood. However, understanding the metabolic consequences of environmental predisposing factors could shed light on disorders such as autism and schizophrenia.MethodsTo gain a better understanding of the metabolic consequences of IL-6 exposure on developing central nervous system (CNS) cells, we separately exposed developing neuron and astroglia cultures to IL-6 for 2 hours while collecting effluent from our gravity-fed microfluidic chambers. By coupling microfluidic technologies to ultra-performance liquid chromatography-ion mobility-mass spectrometry (UPLC-IM-MS), we were able to characterize the metabolic response of these CNS cells to a narrow window of IL-6 exposure.ResultsOur results revealed that 1) the use of this technology, due to its superb media volume:cell volume ratio, is ideally suited for analysis of cell-type-specific exometabolome signatures; 2) developing neurons have low secretory activity at baseline, while astroglia show strong metabolic activity; 3) both neurons and astroglia respond to IL-6 exposure in a cell type-specific fashion; 4) the astroglial response to IL-6 stimulation is predominantly characterized by increased levels of metabolites, while neurons mostly depress their metabolic activity; and 5) disturbances in glycerophospholipid metabolism and tryptophan/kynurenine metabolite secretion are two putative mechanisms by which IL-6 affects the developing nervous system.ConclusionsOur findings are potentially critical for understanding the mechanism by which IL-6 disrupts brain function, and they provide information about the molecular cascade that links maternal immune activation to developmental brain disorders.

Wavelet-based peak detection and a new charge inference procedure for MS/MS implemented in ProteoWizard's msConvert.

We report the implementation of high-quality signal processing algorithms into ProteoWizard, an efficient, open-source software package designed for analyzing proteomics tandem mass spectrometry data. Specifically, a new wavelet-based peak-picker (CantWaiT) and a precursor charge determination algorithm (Turbocharger) have been implemented. These additions into ProteoWizard provide universal tools that are independent of vendor platform for tandem mass spectrometry analyses and have particular utility for intralaboratory studies requiring the advantages of different platforms convergent on a particular workflow or for interlaboratory investigations spanning multiple platforms. We compared results from these tools to those obtained using vendor and commercial software, finding that in all cases our algorithms resulted in a comparable number of identified peptides for simple and complex samples measured on Waters, Agilent, and AB SCIEX quadrupole time-of-flight and Thermo Q-Exactive mass spectrometers. The mass accuracy of matched precursor ions also compared favorably with vendor and commercial tools. Additionally, typical analysis runtimes (∼1–100 ms per MS/MS spectrum) were short enough to enable the practical use of these high-quality signal processing tools for large clinical and research data sets.

Untargeted metabolic profiling identifies interactions between Huntington's disease and neuronal manganese status

Manganese (Mn) is an essential micronutrient for development and function of the nervous system. Deficiencies in Mn transport have been implicated in the pathogenesis of Huntingtons disease (HD), an autosomal dominant neurodegenerative disorder characterized by loss of medium spiny neurons of the striatum. Brain Mn levels are highest in striatum and other basal ganglia structures, the most sensitive brain regions to Mn neurotoxicity. Mouse models of HD exhibit decreased striatal Mn accumulation and HD striatal neuron models are resistant to Mn cytotoxicity. We hypothesized that the observed modulation of Mn cellular transport is associated with compensatory metabolic responses to HD pathology. Here we use an untargeted metabolomics approach by performing ultraperformance liquid chromatography-ion mobility-mass spectrometry (UPLC-IM-MS) on control and HD immortalized mouse striatal neurons to identify metabolic disruptions under three Mn exposure conditions, low (vehicle), moderate (non-cytotoxic) and high (cytotoxic). Our analysis revealed lower metabolite levels of pantothenic acid, and glutathione (GSH) in HD striatal cells relative to control cells. HD striatal cells also exhibited lower abundance and impaired induction of isobutyryl carnitine in response to increasing Mn exposure. In addition, we observed induction of metabolites in the pentose shunt pathway in HD striatal cells after high Mn exposure. These findings provide metabolic evidence of an interaction between the HD genotype and biologically relevant levels of Mn in a striatal cell model with known HD by Mn exposure interactions. The metabolic phenotypes detected support existing hypotheses that changes in energetic processes underlie the pathobiology of both HD and Mn neurotoxicity.