Bullock, K. | bioRxiv Science

bioRxiv2018

netome: a computational framework for metabolite profiling and omics network analysis

SummaryAdvances in metabolomics technologies have enabled comprehensive analyses of associations between metabolites and human disease and have provided a means to study biochemical pathways and processes in detail using model systems. Liquid chromatography tandem mass spectrometry (LC-MS) is an analytical technique commonly used by metabolomics labs to measure hundreds of metabolites of known identity and thousands of \"peaks\" from yet to be identified compounds that are tracked by their measured masses and chromatographic retention times. netome is a computational framework that provides tools for analyzing processed LC-MS data. In this framework, we develop and provide various computational resources including individual software modules to inspect and adjust trends in raw data, align unknown peaks between separately acquired data sets, and to remove redundancies in nontargeted LC-MS data arising from multiple ionization products of a single metabolite. These tools are deployed through computing resources such as web servers and virtual machines with detailed documentation in order to support researchers.\n\nAvailability and implementationnetome is publicly available with extensive documentation and support via issue tracker at https://broadinstitute.github.io/netome under the MIT license. netome includes a set of computational methods that have been designed to execute quality control and post-raw data processing tasks for metabolomics data (e.g. scaling and clustering metabolite abundances), as well as statistical association testing in a network manner (e.g., testing relationship between metabolites and microbes). Each individual tool is available with source code, workshop-oriented documentation which includes instructions for installation and using tools with demonstration examples, and a web server with all services. We also provide a complete image of the netome package with all pre-installed dependencies and support for Google Compute Engine and Amazon EC2. All tools and related services are maintained, and upon new developments, new modules will be added to the environment.\n\nContactrah@broadinstitute.org, clary@broadinstitute.org\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

Endospores and other lysis-resistant bacteria comprise a widely shared core community within the human microbiota

Endospore-formers in the human microbiota are well adapted for host-to-host transmission, and an emerging consensus points to their role in determining health and disease states in the gut. The human gut, more than any other environment, encourages the maintenance of endospore formation, with recent culture-based work suggesting that over 50% of genera in the microbiome carry genes attributed to this trait. However, there has been limited work on the ecological role of endospores and other stress-resistant cellular states in the human gut. In fact, there is no data to indicate whether organisms with the genetic potential to form endospores actually form endospores in situ and how sporulation varies across individuals and over time. Here, we applied a culture-independent protocol to enrich for endospores and other stress-resistant cells in human feces to identify variation in these states across people and within an individual over time. We see that cells with resistant states are more likely than those without to be shared among multiple individuals, which suggests that these resistant states are particularly adapted for cross-host dissemination. Furthermore, we use untargeted fecal metabolomics in 24 individuals and within a person over time to show that these organisms respond to shared environmental signals, and in particular, dietary fatty acids, that likely mediate colonization of recently disturbed human guts.

microbiology↗