Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare no competing financial interest., (Copyright © 2020 American Chemical Society.)

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.