Inferring Dynamic Signatures of Microbes in Complex Host Ecosystems

The human gut microbiota comprise a complex and dynamic ecosystem that profoundly affects host development and physiology. Standard approaches for analyzing time-series data of the microbiota involve computation of measures of ecological community diversity at each time-point, or measures of dissimilarity between pairs of time-points. Although these approaches, which treat data as static snapshots of microbial communities, can identify shifts in overall community structure, they fail to capture the dynamic properties of individual members of the microbiota and their contributions to the underlying time-varying behavior of host ecosystems. To address the limitations of current methods, we present a computational framework that uses continuous-time dynamical models coupled with Bayesian dimensionality adaptation methods to identify time-dependent signatures of individual microbial taxa within a host as well as across multiple hosts. We apply our framework to a publicly available dataset of 16S rRNA gene sequences from stool samples collected over ten months from multiple human subjects, each of whom received repeated courses of oral antibiotics. Using new diversity measures enabled by our framework, we discover groups of both phylogenetically close and distant bacterial taxa that exhibit consensus responses to antibiotic exposure across multiple human subjects. These consensus responses reveal a timeline for equilibration of sub-communities of micro-organisms with distinct physiologies, yielding insights into the successive changes that occur in microbial populations in the human gut after antibiotic treatments. Additionally, our framework leverages microbial signatures shared among human subjects to automatically design optimal experiments to interrogate dynamic properties of the microbiota in new studies. Overall, our approach provides a powerful, general-purpose framework for understanding the dynamic behaviors of complex microbial ecosystems, which we believe will prove instrumental for future studies in this field

The business of genomic testing: a survey of early adopters

Purpose: The practice of “genomic” (or “personalized”) medicine requires the availability of appropriate diagnostic testing. Our study objective was to identify the reasons for health systems to bring next-generation sequencing into their clinical laboratories and to understand the process by which such decisions were made. Such information may be of value to other health systems seeking to provide next-generation sequencing testing to their patient populations. Methods: A standardized open-ended interview was conducted with the laboratory medical directors and/or department of pathology chairs of 13 different academic institutions in 10 different states. Results: Genomic testing for cancer dominated the institutional decision making, with three primary reasons: more effective delivery of cancer care, the perceived need for institutional leadership in the field of genomics, and the premise that genomics will eventually be cost-effective. Barriers to implementation included implementation cost; the time and effort needed to maintain this newer testing; challenges in interpreting genetic variants; establishing the bioinformatics infrastructure; and curating data from medical, ethical, and legal standpoints. Ultimate success depended on alignment with institutional strengths and priorities and working closely with institutional clinical programs. Conclusion: These early adopters uniformly viewed genomic analysis as an imperative for developing their expertise in the implementation and practice of genomic medicine

Thymus-derived regulatory T cells control tolerance to commensal microbiota

Peripheral mechanisms preventing autoimmunity and maintaining tolerance to commensal microbiota involve CD4+Foxp3+ regulatory T cells1,2 generated in the thymus (tTregs) or extrathymically by induction of naive CD4+Foxp3− T cells (iTregs). Prior studies suggested that the T cell receptor (TCR) repertoires of tTregs and iTregs are biased towards self and non-self antigens, respectively 3–6 but their relative contribution in controlling immunopathology, e.g. colitis and other untoward inflammatory responses triggered by different types of antigens, remains unresolved 7. The intestine, and especially the colon, is a particularly suitable organ to study this question, given the variety of self-, microbiota- and food-derived antigens to which Tregs and other T cell populations are exposed. Intestinal environments can enhance conversion to a regulatory lineage 8,9 and favor tolerogenic presentation of antigens to naive CD4+ T cells 10,11, suggesting that intestinal homeostasis depends on microbiota-specific iTregs 12–15. Here, to identify the origin and antigen-specificity of intestinal Tregs, we performed single cell as well as high-throughput (HT) sequencing of the TCR repertoires of CD4+Foxp3+ and CD4+Foxp3− T cells and analyzed their reactivity against specific commensal species. We show that tTregs constitute the majority of Tregs in all lymphoid and intestinal organs, including colon, where their repertoire is heavily influenced by the composition of the microbiota. Our results suggest that tTregs, and not iTregs, dominantly mediate tolerance to antigens produced by intestinal commensals

Dynamics of the Microbiota in Response to Host Infection

Longitudinal studies of the microbiota are important for discovering changes in microbial communities that affect the host. The complexity of these ecosystems requires rigorous integrated experimental and computational methods to identify temporal signatures that promote physiologic or pathophysiologic responses in vivo. Employing a murine model of infectious colitis with the pathogen Citrobacter rodentium, we generated a 2-month time-series of 16S rDNA gene profiles, and quantitatively cultured commensals, from multiple intestinal sites in infected and uninfected mice. We developed a computational framework to discover time-varying signatures for individual taxa, and to automatically group signatures to identify microbial sub-communities within the larger gut ecosystem that demonstrate common behaviors. Application of this model to the 16S rDNA dataset revealed dynamic alterations in the microbiota at multiple levels of resolution, from effects on systems-level metrics to changes across anatomic sites for individual taxa and species. These analyses revealed unique, time-dependent microbial signatures associated with host responses at different stages of colitis. Signatures included a Mucispirillum OTU associated with early disruption of the colonic surface mucus layer, prior to the onset of symptomatic colitis, and members of the Clostridiales and Lactobacillales that increased with successful resolution of inflammation, after clearance of the pathogen. Quantitative culture data validated findings for predominant species, further refining and strengthening model predictions. These findings provide new insights into the complex behaviors found within host ecosystems, and define several time-dependent microbial signatures that may be leveraged in studies of other infectious or inflammatory conditions

College of American Pathologists\u27 Laboratory Standards for Next-Generation Sequencing Clinical Tests

Context.-The higher throughput and lower per-base cost of next-generation sequencing (NGS) as compared to Sanger sequencing has led to its rapid adoption in clinical testing. The number of laboratories offering NGS-based tests has also grown considerably in the past few years, despite the fact that specific Clinical Laboratory Improvement Amendments of 1988/College of American Pathologists (CAP) laboratory standards had not yet been developed to regulate this technology. Objective.-To develop a checklist for clinical testing using NGS technology that sets standards for the analytic wet bench process and for bioinformatics or \u27\u27 dry bench\u27\u27 analyses. As NGS-based clinical tests are new to diagnostic testing and are of much greater complexity than traditional Sanger sequencing-based tests, there is an urgent need to develop new regulatory standards for laboratories offering these tests. Design.-To develop the necessary regulatory framework for NGS and to facilitate appropriate adoption of this technology for clinical testing, CAP formed a committee in 2011, the NGS Work Group, to deliberate upon the contents to be included in the checklist. Results.-A total of 18 laboratory accreditation checklist requirements for the analytic wet bench process and bioinformatics analysis processes have been included within CAP\u27s molecular pathology checklist (MOL). Conclusions.-This report describes the important issues considered by the CAP committee during the development of the new checklist requirements, which address documentation, validation, quality assurance, confirmatory testing, exception logs, monitoring of upgrades, variant interpretation and reporting, incidental findings, data storage, version traceability, and data transfer confidentiality

15. ゼミノームの放射線治療成績(第5回佐藤外科例会,第488回千葉医学会例会)

Performance of the MDSINE inference algorithms on simulated data with different sequencing depths. Simulations assumed an underlying dynamical systems model with ten species observed over 30 days with 27 time points sampled and an invading species at day 10. Performance of the four MDSINE inference algorithms, maximum likelihood ridge regression (MLRR), maximum likelihood constrained ridge regression (MLCRR), Bayesian adaptive lasso (BAL), and Bayesian variable selection (BVS), were compared. Algorithm performance was assessed using four different metrics: (a) root mean-square error (RMSE) for microbial growth rates; (b) RMSE for microbial interaction parameters; (c) RMSE for prediction of microbe trajectories on held-out subjects given only initial microbe concentrations for the held-out subject; and (d) area under the receiver operator curve (AUC ROC) for the underlying microbial interaction network. Lower RMSE values indicate superior performance, whereas higher AUC ROC values indicate superior performance. (PDF 182 kb

MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE’s utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0980-6) contains supplementary material, which is available to authorized users

A microbiota signature associated with experimental food allergy promotes allergic sensitization and anaphylaxis

Background: Commensal microbiota play a critical role in maintaining oral tolerance. The effect of food allergy on the gut microbial ecology remains unknown. Methods: Food allergy–prone mice with a gain-of-function mutation in the IL-4 receptor α chain (Il4raF709) and wild-type (WT) control animals were subjected to oral sensitization with chicken egg ovalbumin (OVA). Enforced tolerance was achieved by using allergen-specific regulatory T (Treg) cells. Community structure analysis of gut microbiota was performed by using a high-density 16S rDNA oligonucleotide microarrays (PhyloChip) and massively parallel pyrosequencing of 16S rDNA amplicons. Results: OVA-sensitized Il4raF709 mice exhibited a specific microbiota signature characterized by coordinate changes in the abundance of taxa of several bacterial families, including the Lachnospiraceae, Lactobacillaceae, Rikenellaceae, and Porphyromonadaceae. This signature was not shared by similarly sensitized WT mice, which did not exhibit an OVA-induced allergic response. Treatment of OVA-sensitized Il4raF709 mice with OVA-specific Treg cells led to a distinct tolerance-associated signature coincident with the suppression of the allergic response. The microbiota of allergen-sensitized Il4raF709 mice differentially promoted OVA-specific IgE responses and anaphylaxis when reconstituted in WT germ-free mice. Conclusion: Mice with food allergy exhibit a specific gut microbiota signature capable of transmitting disease susceptibility and subject to reprogramming by enforced tolerance. Disease-associated microbiota may thus play a pathogenic role in food allergy

The Short Isoform of the CEACAM1 Receptor in Intestinal T Cells Regulates Mucosal Immunity and Homeostasis via Tfh Cell Induction

Carcinoembryonic antigen cell adhesion molecule like I (CEACAM1) is expressed on activated T cells and signals through either a long (L) cytoplasmic tail containing immune receptor tyrosine based inhibitory motifs, which provide inhibitory function, or a short (S) cytoplasmic tail with an unknown role. Previous studies on peripheral T cells show that CEACAM1-L isoforms predominate with little to no detectable CEACAM1-S isoforms in mouse and human. We show here that this was not the case in tissue resident T cells of intestines and gut associated lymphoid tissues which demonstrated predominant expression of CEACAM1-S isoforms relative to CEACAM1-L isoforms in human and mouse. This tissue resident predominance of CEACAM1-S expression was determined by the intestinal environment where it served a stimulatory function leading to the regulation of T cell subsets associated with generation of secretory IgA immunity, the regulation of mucosal commensalism, and defense of the barrier against enteropathogens