Niche partitioning of a pathogenic microbiome driven by chemical gradients

© 2018 The Authors, some rights reserved. Environmental microbial communities are stratified by chemical gradients that shape the structure and function of these systems. Similar chemical gradients exist in the human body, but how they influence these microbial systems is more poorly understood. Understanding these effects can be particularly important for dysbiotic shifts in microbiome structure that are often associated with disease. We show that pH and oxygen strongly partition the microbial community from a diseased human lung into two mutually exclusive communities of pathogens and anaerobes. Antimicrobial treatment disrupted this chemical partitioning, causing complex death, survival, and resistance outcomes that were highly dependent on the individual microorganism and on community stratification. These effects were mathematically modeled, enabling a predictive understanding of this complex polymicrobial system. Harnessing the power of these chemical gradients could be a drug-free method of shaping microbial communities in the human body from undesirable dysbiotic states

Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry

BACKGROUND: Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms. FINDINGS: High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma. CONCLUSIONS: With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets

Convergent evolution of pain-inducing defensive venom components in spitting cobras

Convergent evolution provides insights into the selective drivers underlying evolutionary change. Snake venoms, with a direct genetic basis and clearly defined functional phenotype, provide a model system for exploring the repeated evolution of adaptations. While snakes use venom primarily for predation, and venom composition often reflects diet specificity, three lineages of cobras have independently evolved the ability to spit venom at adversaries. Using gene, protein, and functional analyses, we show that the three spitting lineages possess venoms characterized by an up-regulation of phospholipase A2 (PLA2) toxins, which potentiate the action of preexisting venom cytotoxins to activate mammalian sensory neurons and cause enhanced pain. These repeated independent changes provide a fascinating example of convergent evolution across multiple phenotypic levels driven by selection for defense

Data-independent microbial metabolomics with ambient ionization mass spectrometry

Atmospheric ionization methods are ideally suited for prolonged MS/MS analysis. Data-independent MS/MS is a complementary technique for analysis of biological samples as compared to data-dependent analysis. Here, we pair data-independent MS/MS with the ambient ionization method nanospray desorption electrospray ionization (nanoDESI) for untargeted analysis of bacterial metabolites. Proof-of-principle data and analysis are illustrated by sampling Bacillus subtilis and Pseudomonas aeruginosa directly from Petri dishes. We found that this technique enables facile comparisons between strains via MS and MS/MS plots which can be translated to chemically informative molecular maps through MS/MS networking. The development of novel techniques to characterize microbial metabolites allows rapid and efficient analysis of metabolic exchange factors. This is motivated by our desire to develop novel techniques to explore the role of interspecies interactions in the environment, health, and disease. This is a contribution to honor Professor Catherine C. Fenselau in receiving the prestigious ASMS Award for a Distinguished Contribution in Mass Spectrometry for her pioneering work on microbial mass spectrometry. [Figure not available: see fulltext.] 2013 American Society for Mass Spectrometr

Prioritizing natural product diversity in a collection of 146 bacterial strains based on growth and extraction protocols

In order to expedite the rapid and efficient discovery and isolation of novel specialized metabolites, whilst minimizing the waste of resources on rediscovery of known compounds, it is crucial to develop efficient approaches for strain prioritization, rapid dereplication, and the assessment of favored cultivation and extraction conditions. Herein we interrogated bacterial strains by systematically evaluating cultivation and extraction parameters with LC-MS/MS analysis and subsequent dereplication through the Global Natural Product Social Molecular Networking (GNPS) platform. The developed method is fast, requiring minimal time and sample material, and is compatible with high throughput extract analysis, thereby streamlining strain prioritization and evaluation of culturing parameters. With this approach, we analyzed 146 marine Salinispora and Streptomyces strains that were grown and extracted using multiple different protocols. In total, 603 samples were analyzed, generating approximately 1.8 million mass spectra. We constructed a comprehensive molecular network and identified 15 molecular families of diverse natural products and their analogues. The size and breadth of this network shows statistically supported trends in molecular diversity when comparing growth and extraction conditions. The network provides an extensive survey of the biosynthetic capacity of the strain collection and a method to compare strains based on the variety and novelty of their metabolites. This approach allows us to quickly identify patterns in metabolite production that can be linked to taxonomy, culture conditions, and extraction methods, as well as informing the most valuable growth and extraction conditions

Prioritizing natural product diversity in a collection of 146 bacterial strains based on growth and extraction protocols

In order to expedite the rapid and efficient discovery and isolation of novel specialized metabolites, whilst minimizing the waste of resources on rediscovery of known compounds, it is crucial to develop efficient approaches for strain prioritization, rapid dereplication, and the assessment of favored cultivation and extraction conditions. Herein we interrogated bacterial strains by systematically evaluating cultivation and extraction parameters with LC-MS/MS analysis and subsequent dereplication through the Global Natural Product Social Molecular Networking (GNPS) platform. The developed method is fast, requiring minimal time and sample material, and is compatible with high throughput extract analysis, thereby streamlining strain prioritization and evaluation of culturing parameters. With this approach, we analyzed 146 marine Salinispora and Streptomyces strains that were grown and extracted using multiple different protocols. In total, 603 samples were analyzed, generating approximately 1.8 million mass spectra. We constructed a comprehensive molecular network and identified 15 molecular families of diverse natural products and their analogues. The size and breadth of this network shows statistically supported trends in molecular diversity when comparing growth and extraction conditions. The network provides an extensive survey of the biosynthetic capacity of the strain collection and a method to compare strains based on the variety and novelty of their metabolites. This approach allows us to quickly identify patterns in metabolite production that can be linked to taxonomy, culture conditions, and extraction methods, as well as informing the most valuable growth and extraction conditions

An overview and early results from the HOMEChem indoor air chemistry field campaign

The HOMEChem (House Observations of Microbial and Environmental Chemistry) is a collaborative indoor air chemistry field study to be performed in June 2018 at the UTest House, a manufactured research house located in the University of Texas at Austin's research campus. The HOMEChem experiment investigates the effects of building occupants and their activities, such as cooking and cleaning, on the chemistry of the gas phase, particle phase, and surfaces in a simulated home environment. Specifically, this study focuses on the presence of organic species, chemical oxidants, and reactive nitrogen species indoors compared to outdoor levels. This study incorporates state-of the art atmospheric chemistry instrumentation from multiple research groups to build a shared dataset from those measurements