Guild and Niche Determination Enable Targeted Alteration of the Microbiome.

Microbiome science has greatly contributed to our understanding of microbial life and its essential roles for the environment and human health1-5. However, the nature of microbial interactions and how microbial communities respond to perturbations remains poorly understood, resulting in an often descriptive and correlation-based approach to microbiome research6-8. Achieving causal and predictive microbiome science would require direct functional measurements in complex communities to better understand the metabolic role of each member and its interactions with others. In this study we present a new approach that integrates transcription and translation measurements to predict competition and substrate preferences within microbial communities, consequently enabling the selective manipulation of the microbiome. By performing metatranscriptomic (metaRNA-Seq) and metatranslatomic (metaRibo-Seq) analysis in complex samples, we classified microbes into functional groups (i.e. guilds) and demonstrated that members of the same guild are competitors. Furthermore, we predicted preferred substrates based on importer proteins, which specifically benefited selected microbes in the community (i.e. their niche) and simultaneously impaired their competitors. We demonstrated the scalability of microbial guild and niche determination to natural samples and its ability to successfully manipulate microorganisms in complex microbiomes. Thus, the approach enhances the design of pre- and probiotic interventions to selectively alter members within microbial communities, advances our understanding of microbial interactions, and paves the way for establishing causality in microbiome science.

synDNA-a Synthetic DNA Spike-in Method for Absolute Quantification of Shotgun Metagenomic Sequencing.

Microbiome studies have the common goal of determining which microbial taxa are present, respond to specific conditions, or promote phenotypic changes in the host. Most of these studies rely on relative abundance measurements to drive conclusions. Inherent limitations of relative values are the inability to determine whether an individual taxon is more or less abundant and the magnitude of this change between the two samples. These limitations can be overcome by using absolute abundance quantifications, which can allow for a more complete understanding of community dynamics by measuring variations in total microbial loads. Obtaining absolute abundance measurements is still technically challenging. Here, we developed synthetic DNA (synDNA) spike-ins that enable precise and cost-effective absolute quantification of microbiome data by adding defined amounts of synDNAs to the samples. We designed 10 synDNAs with the following features: 2,000-bp length, variable GC content (26, 36, 46, 56, or 66% GC), and negligible identity to sequences found in the NCBI database. Dilution pools were generated by mixing the 10 synDNAs at different concentrations. Shotgun metagenomic sequencing showed that the pools of synDNAs with different percentages of GC efficiently reproduced the serial dilution, showing high correlation (r = 0.96; R2 ≥ 0.94) and significance (P < 0.01). Furthermore, we demonstrated that the synDNAs can be used as DNA spike-ins to generate linear models and predict with high accuracy the absolute number of bacterial cells in complex microbial communities. IMPORTANCE The synDNAs designed in this study enable accurate and reproducible measurements of absolute amount and fold changes of bacterial species in complex microbial communities. The method proposed here is versatile and promising as it can be applied to bacterial communities or genomic features like genes and operons, in addition to being easily adaptable by other research groups at a low cost. We also made the synDNAs' sequences and the plasmids available to encourage future application of the proposed method in the study of microbial communities.

Carbohydrates great and small, from dietary fiber to sialic acids: How glycans influence the gut microbiome and affect human health.

Gut microbiome composition depends heavily upon diet and has strong ties to human health. Dietary carbohydrates shape the gut microbiome by providing a potent nutrient source for particular microbes. This review explores how dietary carbohydrates in general, including individual monosaccharides and complex polysaccharides, influence the gut microbiome with subsequent effects on host health and disease. In particular, the effects of sialic acids, a prominent and influential class of monosaccharides, are discussed. Complex plant carbohydrates, such as dietary fiber, generally promote microbial production of compounds beneficial to the host while preventing degradation of host carbohydrates from colonic mucus. In contrast, simple and easily digestible sugars such as glucose are often associated with adverse effects on health and the microbiome. The monosaccharide class of sialic acids exerts a powerful but nuanced effect on gut microbiota. Sialic acid consumption (in monosaccharide form, or as part of human milk oligosaccharides or certain animal-based foods) drives the growth of organisms with sialic acid metabolism capabilities. Minor chemical modifications of Neu5Ac, the most common form of sialic acid, can alter these effects. All aspects of carbohydrate composition are therefore relevant to consider when designing dietary therapeutic strategies to alter the gut microbiome.

The sum is greater than the parts: exploiting microbial communities to achieve complex functions.

Multi-species microbial communities are ubiquitous in nature. The widespread prevalence of these communities is due to highly elaborated interactions among their members thereby accomplishing metabolic functions that are unattainable by individual members alone. Harnessing these communal capabilities is an emerging field in biotechnology. The rational intervention of microbial communities for the purpose of improved function has been facilitated in part by developments in multi-omics approaches, synthetic biology, and computational methods. Recent studies have demonstrated the benefits of rational interventions to human and animal health as well as agricultural productivity. Emergent technologies, such as in situ modification of complex microbial community and community metabolic modeling, represent an avenue to engineer sustainable microbial communities. In this opinion, we review relevant computational and experimental approaches to study and engineer microbial communities and discuss their potential for biotechnological applications.

Metabotypes of Pseudomonas aeruginosa Correlate with Antibiotic Resistance, Virulence and Clinical Outcome in Cystic Fibrosis Chronic Infections.

Pseudomonas aeruginosa (P.a) is one of the most critical antibiotic resistant bacteria in the world and is the most prevalent pathogen in cystic fibrosis (CF), causing chronic lung infections that are considered one of the major causes of mortality in CF patients. Although several studies have contributed to understanding P.a within-host adaptive evolution at a genomic level, it is still difficult to establish direct relationships between the observed mutations, expression of clinically relevant phenotypes, and clinical outcomes. Here, we performed a comparative untargeted LC/HRMS-based metabolomics analysis of sequential isolates from chronically infected CF patients to obtain a functional view of P.a adaptation. Metabolic profiles were integrated with expression of bacterial phenotypes and clinical measurements following multiscale analysis methods. Our results highlighted significant associations between P.a "metabotypes", expression of antibiotic resistance and virulence phenotypes, and frequency of clinical exacerbations, thus identifying promising biomarkers and therapeutic targets for difficult-to-treat P.a infections.

Feature-based molecular networking in the GNPS analysis environment.

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

High-Resolution Magic Angle Spinning NMR-Based Metabolomics Revealing Metabolic Changes in Lung of Mice Infected with P. aeruginosa Consistent with the Degree of Disease Severity.

Pseudomonas aeruginosa is a critical pathogen for human health, due to increased resistances to antibiotics and to nosocomial infections. There is an urgent need for tools allowing for better understanding mechanisms underlying the disease processes and for evaluating new therapeutic strategies with animal models. Here, we used a novel approach, applying high-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS NMR) directly to lung biopsies of mice to better understand the impact of infection on the tissue at a molecular level. Mice were infected with two P. aeruginosa strains of different virulence levels. Statistical analysis applied to HRMAS NMR data allowed us to build a multivariate discriminant model to distinguish the lungs' metabolic profiles of mice, infected or not. Moreover, a second model was built to appreciate the degree of severity of infection, demonstrating sufficient sensitivity of HRMAS NMR-based metabolomics to investigate this type of infection. The metabolic features that discriminate infection statuses are dominated by some key differentially expressed metabolites that are related, respectively, to bacterial carbon metabolism (glycerophosphocholine) and to septic hypoxic stress response of host (succinate). Finally, to get closer to clinical and diagnosis issues, we proposed to build simple logistic regression models to predict the infection status on the basis of only one metabolite intensity. Thus, we have demonstrated that succinate intensity could discriminate the infected/noninfected status infection with a sensibility of 89% and a specificity of 95%, and leucine/isoleucine intensity could predict the severe/not severe status of infection with a sensibility of 100% and a specificity of 95%. We also looked for the interest of this model in order to predict the efficacy of anti- P. aeruginosa treatment. By HRMAS metabolomics analysis of lungs infected with P. aeruginosa after vaccination, we demonstrated that this model could be a useful tool to predict the efficacy of new anti- P. aeruginosa drugs. This metabolomics approach could therefore be useful both for the definition of biomarkers of severity of infection and for an earlier characterization of therapeutic efficacy.

Ranking occupational contexts associated with risk of non-Hodgkin lymphoma.

BACKGROUND: Risk factors associated with non-Hodgkin lymphoma (NHL) remain unknown, but certain occupational contexts (OCs) have been implicated. The objective of this study was to inventory, from the accumulated knowledge, associations between OCs and NHL risk. METHODS: Literature was used to identify the NHL-associated OCs. For each context, items were ranked both by scientific interest and the association strength. RESULTS: Three ranked lists of OCs related to NHL were constructed. We found that NHL was associated with 31 occupational activities, 91 occupational exposures, and 35 occupational activity-exposure combinations. Among them, 5 activities, 2 exposures, and 3 combinations, involving agricultural or industrial sector and solvents or pesticides, were highlighted with the highest publications number and the strongest association with NHL risk. CONCLUSION: These results could be useful in both providing a ranked inventory of OCs associated with NHL risk and highlighting "hot" occupational activities and exposures. Am. J. Ind. Med. 59:561-574, 2016. © 2016 Wiley Periodicals, Inc.