ABSTRACTAs our understanding of the structure and diversity of the microbial world grows, interpreting its function is of critical interest for understanding and managing the many systems microbes influence. Despite advances in sequencing, lack of standardization challenges comparisons among studies that could provide insight into the structure and function of microbial communities across multiple habitats on a planetary scale. Technical variation among distinct studies without proper standardization of approaches prevents robust meta-analysis. Here, we present a multi-omics, meta-analysis of a novel, diverse set of microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry), centering our description on relationships and co-occurrences of microbially-related metabolites and microbial taxa across environments. Standardized protocols and analytical methods for characterizing microbial communities, including assessment of molecular diversity using untargeted metabolomics, facilitate identification of shared microbial and metabolite features, permitting us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of a community resource that will become more valuable with time. To provide examples of applying this database, we outline important ecological questions that can be addressed, and test the hypotheses that every microbe and metabolite is everywhere, but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment. The relative abundances of microbially-related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner, and highlight the power of certain chemistry – in particular terpenoids – in distinguishing Earth’s environments.

It is currently unknown whether endovascular filament (EF) models of acute subarachnoid hemorrhage (SAH) introduce any ‘side bias’ upon cerebrovascular reactivity by virtue of unilateral carotid manipulation, unilateral EF insertion, or potentially ipsilateral site of intracranial rupture. In this study, three experimental groups of adult male wistar rats were used: non-operated controls (n=20), sham operated controls (n=8), and SAH groups (n=26). All were anaesthetized with intraperitoneal 25% urethane (1 g/kg) except n=2 anaesthetized with intraperitoneal hypnorm/hypnovel. SAH was created by advancing an intraluminal thread through the intracranial internal carotid artery from an extracranial source in the neck. Using in vitro wire myography in all three groups, middle cerebral artery (MCA) responses ipsilateral and contralateral to EF insertion were compared using a range of vasoconstrictors and vasodilators within 3 h of SAH. No significant side differences in MCA reactivity were found with any agent in any group or sub-group analysed. In conclusion, EF-SAH models do not appear to introduce any significant ‘side bias’ upon ipsilateral MCA reactivity by virtue of the potentially confounding combination: (1) unilateral carotid manipulation, (2) unilateral EF insertion or (3) potentially ipsilateral intracranial rupture. They may thus be more confidentally used for post-SAH ex vivo cerebral vessel study—acute or delayed.

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth’s environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.

Meta-analyses at the whole-community level have been important in microbiome studies, revealing profound features that structure Earth's microbial communities, such as the unique differentiation of microbes from the mammalian gut relative to free-living microbial communities, the separation of microbiomes in saline and nonsaline environments, and the role of pH in driving soil microbial compositions. However, our ability to identify the specific features of a microbiome that differentiate these community-level patterns have lagged behind, especially as ever-cheaper DNA sequencing has yielded increasingly large data sets. One critical gap is the ability to search for samples that contain specific features (for example, sub-operational taxonomic units [sOTUs] identified by high-resolution statistical methods for removing amplicon sequencing errors). Here we introduce redbiom, a microbiome caching layer, which allows users to rapidly query samples that contain a given feature, retrieve sample data and metadata, and search for samples that match specified metadata values or ranges (e.g., all samples with a pH of >7), implemented using an in-memory NoSQL database called Redis. By default, redbiom allows public anonymous sample access for over 100,000 publicly available samples in the Qiita database. At over 100,000 samples, the caching server requires only 35 GB of resident memory. We highlight how redbiom enables a new type of characterization of microbiome samples and provide tutorials for using redbiom with QIIME 2. redbiom is open source under the BSD license, hosted on GitHub, and can be deployed independently of Qiita to enable search of proprietary or clinically restricted microbiome databases.IMPORTANCE Although analyses that combine many microbiomes at the whole-community level have become routine, searching rapidly for microbiomes that contain a particular sequence has remained difficult. The software we present here, redbiom, dramatically accelerates this process, allowing samples that contain microbiome features to be rapidly identified. This is especially useful when taxonomic annotation is limited, allowing users to identify environments in which unannotated microbes of interest were previously observed. This approach also allows environmental or clinical factors that correlate with specific features, or vice versa, to be identified rapidly, even at a scale of billions of sequences in hundreds of thousands of samples. The software is integrated with existing analysis tools to enable fast, large-scale microbiome searches and discovery of new microbiome relationships.

Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.

Multi-omics methods have greatly advanced our understanding of the biological organism and its microbial associates. However, they are not routinely used in clinical or industrial applications, due to the length of time required to generate and analyze omics data. Here, we applied a novel integrated omics pipeline for the analysis of human and environmental samples in under 48 h. Human subjects that ferment their own foods provided swab samples from skin, feces, oral cavity, fermented foods, and household surfaces to assess the impact of home food fermentation on their microbial and chemical ecology. These samples were analyzed with 16S rRNA gene sequencing, inferred gene function profiles, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics through the Qiita, PICRUSt, and GNPS pipelines, respectively. The human sample microbiomes clustered with the corresponding sample types in the American Gut Project (http://www.americangut.org), and the fermented food samples produced a separate cluster. The microbial communities of the household surfaces were primarily sourced from the fermented foods, and their consumption was associated with increased gut microbial diversity. Untargeted metabolomics revealed that human skin and fermented food samples had separate chemical ecologies and that stool was more similar to fermented foods than to other sample types. Metabolites from the fermented foods, including plant products such as procyanidin and pheophytin, were present in the skin and stool samples of the individuals consuming the foods. Some food metabolites were modified during digestion, and others were detected in stool intact. This study represents a first-of-its-kind analysis of multi-omics data that achieved time intervals matching those of classic microbiological culturing.IMPORTANCE Polymicrobial infections are difficult to diagnose due to the challenge in comprehensively cultivating the microbes present. Omics methods, such as 16S rRNA sequencing, metagenomics, and metabolomics, can provide a more complete picture of a microbial community and its metabolite production, without the biases and selectivity of microbial culture. However, these advanced methods have not been applied to clinical or industrial microbiology or other areas where complex microbial dysbioses require immediate intervention. The reason for this is the length of time required to generate and analyze omics data. Here, we describe the development and application of a pipeline for multi-omics data analysis in time frames matching those of the culture-based approaches often used for these applications. This study applied multi-omics methods effectively in clinically relevant time frames and sets a precedent toward their implementation in clinical medicine and industrial microbiology.