A differential proteomics study of cerebrospinal fluid from individuals with Niemann-Pick disease, Type C1.

Niemann-Pick, type C1 (NPC1) is a fatal, neurodegenerative disease, which belongs to the family of lysosomal diseases. In NPC1, endo/lysosomal accumulation of unesterified cholesterol and sphingolipids arise from improper intracellular trafficking resulting in multi-organ dysfunction. With the proximity between the brain and cerebrospinal fluid (CSF), performing differential proteomics provides a means to shed light to changes occurring in the brain. In this study, CSF samples obtained from NPC1 individuals and unaffected controls were used for protein biomarker identification. A subset of these individuals with NPC1 are being treated with miglustat, a glycosphingolipid synthesis inhibitor. Of the 300 identified proteins, 71 proteins were altered in individuals with NPC1 compared to controls including cathepsin D, and members of the complement family. Included are a report of 10 potential markers for monitoring therapeutic treatment. We observed that pro-neuropeptide Y (NPY) was significantly increased in NPC1 individuals relative to healthy controls; however, individuals treated with miglustat displayed levels comparable to healthy controls. In further investigation, NPY levels in a NPC1 mouse model corroborated our findings. We posit that NPY could be a potential therapeutic target for NPC1 due to its multiple roles in the central nervous system such as attenuating neuroinflammation and reducing excitotoxicity.

Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction.

MOTIVATION: Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross-section (CCS). RESULTS: We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class. AVAILABILITY AND IMPLEMENTATION: All relevant source code is available at https://github.com/McLeanResearchGroup/CCS-filter. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Chemical Class Prediction of Unknown Biomolecules Using Ion Mobility-Mass Spectrometry and Machine Learning: Supervised Inference of Feature Taxonomy from Ensemble Randomization.

This work presents a machine learning algorithm referred to as the supervised inference of feature taxonomy from ensemble randomization (SIFTER), which supports the identification of features derived from untargeted ion mobility-mass spectrometry (IM-MS) experiments. SIFTER utilizes random forest machine learning on three analytical measurements derived from IM-MS (collision cross section, CCS), mass-to-charge (m/z), and mass defect (Δm) to classify unknown features into a taxonomy of chemical kingdom, super class, class, and subclass. Each of these classifications is assigned a calculated probability as well as alternate classifications with associated probabilities. After optimization, SIFTER was tested against a set of molecules not used in the training set. The average success rate in classifying all four taxonomy categories correctly was found to be >99%. Analysis of molecular features detected from a complex biological matrix and not used in the training set yielded a lower success rate where all four categories were correctly predicted for ∼80% of the compounds. This decline in performance is in part due to incompleteness of the training set across all potential taxonomic categories, but also resulting from a nearest-neighbor bias in the random forest algorithm. Ongoing efforts are focused on improving the class prediction accuracy of SIFTER through expansion of empirical data sets used for training as well as improvements to the core algorithm.

Mass spectrometry and ion mobility study of poly(ethylene glycol)-based polyurethane oligomers.

RATIONALE: Commercial-grade polymer synthesis is performed via melt polymerization, which leads to polydispersion. The work reported herein provides a synthetic strategy to produce mono-dispersive polyurethane oligomers and an analytical strategy to distinguish these oligomers, providing chemists with the tools necessary to synthesize and identify specific polymer structures that exhibit a desired property. METHODS: Three isomeric poly(ethylene glycol)-polyurethane (PEG-PUR) oligomers were synthesized and analyzed via flow-injection ion mobility mass spectrometry (IM-MS). Each polymer oligomer was injected and run independently via flow injection at 100 µL•min-1 and analyzed in positive ion mode on a drift tube quadrupole time-of-flight (QTOF) instrument. Mobility measurements were determined using a single-field approach. For tandem mass spectrometry (MS/MS) experiments, the sodium-adducted singly charged precursor ion was isolated in the quadrupole and subjected to a range of collision energies. RESULTS: In MS experiments, both +1 and +2 sodium-adducted species were observed for each oligomer at m/z 837.4 and 430.2, respectively. When isolated and fragmented via MS/MS, the +1 precursor yielded distinct product ions for each of the three isomeric oligomers. Fragmentation generally occurred at urethane linkages via 1,3- and 1,5-H shift mechanisms. IM was also used to distinguish the three isomers, with greater IM separation observed for the +2 versus the +1 species. CONCLUSIONS: Mono-disperse PEG-PUR oligomers were synthesized and analyzed. Although the polymeric oligomers analyzed in this study are quite small and structurally simple, this work serves as a model system for the synthesis and structural characterization of larger, more complex block copolymers.

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.

Gastrointestinal Tract Pathology in a BALB/c Niemann-Pick Disease Type C1 Null Mouse Model.

BACKGROUND: Niemann-Pick disease, type C (NPC) is a rare lysosomal storage disorder characterized by progressive neurodegeneration, splenomegaly, hepatomegaly, and early death. NPC is caused by mutations in either the NPC1 or NPC2 gene. Impaired NPC function leads to defective intracellular transport of unesterified cholesterol and its accumulation in late endosomes and lysosomes. A high frequency of Crohn disease has been reported in NPC1 patients, suggesting that gastrointestinal tract pathology may become a more prominent clinical issue if effective therapies are developed to slow the neurodegeneration. The Npc1 nih mouse model on a BALB/c background replicates the hepatic and neurological disease observed in NPC1 patients. Thus, we sought to characterize the gastrointestinal tract pathology in this model to determine whether it can serve as a model of Crohn disease in NPC1. METHODS: We analyzed the gastrointestinal tract and isolated macrophages of BALB/cJ cNctr-Npc1m1N/J (Npc1-/-) mouse model to determine whether there was any Crohn-like pathology or inflammatory cell activation. We also evaluated temporal changes in the microbiota by 16S rRNA sequencing of fecal samples to determine whether there were changes consistent with Crohn disease. RESULTS: Relative to controls, Npc1 mutant mice demonstrate increased inflammation and crypt abscesses in the gastrointestinal tract; however, the observed pathological changes are significantly less than those observed in other Crohn disease mouse models. Analysis of Npc1 mutant macrophages demonstrated an increased response to lipopolysaccharides and delayed bactericidal activity; both of which are pathological features of Crohn disease. Analysis of the bacterial microbiota does not mimic what is reported in Crohn disease in either human or mouse models. We did observe significant increases in cyanobacteria and epsilon-proteobacteria. The increase in epsilon-proteobacteria may be related to altered cholesterol homeostasis since cholesterol is known to promote growth of this bacterial subgroup. CONCLUSIONS: Macrophage dysfunction in the BALB/c Npc1-/- mouse is similar to that observed in other Crohn disease models. However, neither the degree of pathology nor the microbiota changes are typical of Crohn disease. Thus, this mouse model is not a good model system for Crohn disease pathology reported in NPC1 patients.

An Efficient Approach to Evaluate Reporter Ion Behavior from MALDI-MS/MS Data for Quantification Studies Using Isobaric Tags.

Protein quantification, identification, and abundance determination are important aspects of proteome characterization and are crucial in understanding biological mechanisms and human diseases. Different strategies are available to quantify proteins using mass spectrometric detection, and most are performed at the peptide level and include both targeted and untargeted methodologies. Discovery-based or untargeted approaches oftentimes use covalent tagging strategies (i.e., iTRAQ, TMT), where reporter ion signals collected in the tandem MS experiment are used for quantification. Herein we investigate the behavior of the iTRAQ 8-plex chemistry using MALDI-TOF/TOF instrumentation. The experimental design and data analysis approach described is simple and straightforward, which allows researchers to optimize data collection and proper analysis within a laboratory. iTRAQ reporter ion signals were normalized within each spectrum to remove peptide biases. An advantage of this approach is that missing reporter ion values can be accepted for purposes of protein identification and quantification without the need for ANOVA analysis. We investigate the distribution of reporter ion peak areas in an equimolar system and a mock biological system and provide recommendations for establishing fold-change cutoff values at the peptide level for iTRAQ data sets. These data provide a unique data set available to the community for informatics training and analysis.

Therapeutic Strategies For Tay-Sachs Disease.

Tay-Sachs disease (TSD) is an autosomal recessive disease that features progressive neurodegenerative presentations. It affects one in 100,000 live births. Currently, there is no approved therapy or cure. This review summarizes multiple drug development strategies for TSD, including enzyme replacement therapy, pharmaceutical chaperone therapy, substrate reduction therapy, gene therapy, and hematopoietic stem cell replacement therapy. In vitro and in vivo systems are described to assess the efficacy of the aforementioned therapeutic strategies. Furthermore, we discuss using MALDI mass spectrometry to perform a high throughput screen of compound libraries. This enables discovery of compounds that reduce GM2 and can lead to further development of a TSD therapy.

Crowd-Sourced Chemistry: Considerations for Building a Standardized Database to Improve Omic Analyses.

Mass spectrometry (MS) is used in multiple omics disciplines to generate large collections of data. This data enables advancements in biomedical research by providing global profiles of a given system. One of the main barriers to generating these profiles is the inability to accurately annotate omics data, especially small molecules. To complement pre-existing large databases that are not quite complete, research groups devote efforts to generating personal libraries to annotate their data. Scientific progress is impeded during the generation of these personal libraries because the data contained within them is often redundant and/or incompatible with other databases. To overcome these redundancies and incompatibilities, we propose that communal, crowd-sourced databases be curated in a standardized fashion. A small number of groups have shown this model is feasible and successful. While the needs of a specific field will dictate the functionality of a communal database, we discuss some features to consider during database development. Special emphasis is made on standardization of terminology, documentation, format, reference materials, and quality assurance practices. These standardization procedures enable a field to have higher confidence in the quality of the data within a given database. We also discuss the three conceptual pillars of database design as well as how crowd-sourcing is practiced. Generating open-source databases requires front-end effort, but the result is a well curated, high quality data set that all can use. Having a resource such as this fosters collaboration and scientific advancement.

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.