Mass Spectrometry Adduct Calculator

Combating the Dust Devil: Utilizing Naturally Occurring Soil Microbes in Arizona to Inhibit the Growth of Coccidioides spp., the Causative Agent of Valley Fever

The fungal disease Valley fever causes a significant medical and financial burden for affected people in the endemic region, and this burden is on the rise. Despite the medical importance of this disease, little is known about ecological factors that influence the geographic point sources of high abundance of the pathogens Coccidioides posadasii and C. immitis, such as competition with co-occurring soil microbes. These “hot spots”, for instance, those in southern Arizona, are areas in which humans are at greater risk of being infected with the fungus due to consistent exposure. The aim of this study was to isolate native microbes from soils collected from Tucson, Arizona (endemic area for C. posadasii) and characterize their relationship (antagonistic, synergistic, or neutral) to the fungal pathogen with in vitro challenge assays. Secreted metabolites from the microbes were extracted and described using analytical techniques including high-performance liquid chromatography (HPLC) and mass spectrometry. Bacteria belonging to the genus Bacillus and fungi in the Fennellomyces and Ovatospora genera were shown to significantly decrease the growth of Coccidioides spp. In vitro. In contrast, other bacteria in the Brevibacillus genus, as well as one species of Bacillus bacteria, were shown to promote growth of Coccidioides when directly challenged. The metabolites secreted from the antagonistic bacteria were described using HPLC and matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). The microbes identified in this study as antagonists to Coccidioides and/or the metabolites they secrete have the potential to be used as natural biocontrol agents to limit the amount of fungal burden at geographic point sources, and therefore limit the potential for human infection.

DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data

We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among data sets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS metabolomics data to illustrate the advantages of a multidimensional approach in each data processing step.