Respiratory Microbiome Disruption and Risk for Ventilator-Associated Lower Respiratory Tract Infection.

BACKGROUND: Ventilator-associated lower respiratory tract infection (VA-LRTI) is common among critically ill patients and has been associated with increased morbidity and mortality. In acute critical illness, respiratory microbiome disruption indices (MDIs) have been shown to predict risk for VA-LRTI, but their utility beyond the first days of critical illness is unknown. We sought to characterize how MDIs previously shown to predict VA-LRTI at initiation of mechanical ventilation change with prolonged mechanical ventilation, and if they remain associated with VA-LRTI risk. METHODS: We developed a cohort of 83 subjects admitted to a long-term acute care hospital due to their prolonged dependence on mechanical ventilation; performed dense, longitudinal sampling of the lower respiratory tract, collecting 1066 specimens; and characterized the lower respiratory microbiome by 16S rRNA sequencing as well as total bacterial abundance by 16S rRNA quantitative polymerase chain reaction. RESULTS: Cross-sectional MDIs, including low Shannon diversity and high total bacterial abundance, were associated with risk for VA-LRTI, but associations had wide posterior credible intervals. Persistent lower respiratory microbiome disruption showed a more robust association with VA-LRTI risk, with each day of (base e) Shannon diversity <2.0 associated with a VA-LRTI odds ratio of 1.36 (95% credible interval, 1.10-1.72). The observed association was consistent across multiple clinical definitions of VA-LRTI. CONCLUSIONS: Cross-sectional MDIs have limited ability to discriminate VA-LRTI risk during prolonged mechanical ventilation, but persistent lower respiratory tract microbiome disruption, best characterized by consecutive days with low Shannon diversity, may identify a population at high risk for infection and may help target infection-prevention interventions.

Bidirectional transfer of Anelloviridae lineages between graft and host during lung transplantation.

Solid organ transplantation disrupts virus-host relationships, potentially resulting in viral transfer from donor to recipient, reactivation of latent viruses, and new viral infections. Viral transfer, colonization, and reactivation are typically monitored using assays for specific viruses, leaving the behavior of full viral populations (the "virome") understudied. Here we sought to investigate the temporal behavior of viruses from donor lungs and transplant recipients comprehensively. We interrogated the bronchoalveolar lavage and blood viromes during the peritransplant period and 6-16 months posttransplant in 13 donor-recipient pairs using shotgun metagenomic sequencing. Anelloviridae, ubiquitous human commensal viruses, were the most abundant human viruses identified. Herpesviruses, parvoviruses, polyomaviruses, and bacteriophages were also detected. Anelloviridae populations were complex, with some donor organs and hosts harboring multiple contemporaneous lineages. We identified transfer of Anelloviridae lineages from donor organ to recipient serum in 4 of 7 cases that could be queried, and immigration of lineages from recipient serum into the allograft in 6 of 10 such cases. Thus, metagenomic analyses revealed that viral populations move between graft and host in both directions, showing that organ transplantation involves implantation of both the allograft and commensal viral communities.

Microbial Lineages in Sarcoidosis. A Metagenomic Analysis Tailored for Low-Microbial Content Samples.

RATIONALE: The etiology of sarcoidosis is unknown, but microbial agents are suspected as triggers. OBJECTIVES: We sought to identify bacterial, fungal, or viral lineages in specimens from patients with sarcoidosis enriched relative to control subjects using metagenomic DNA sequencing. Because DNA from environmental contamination contributes disproportionately to samples with low authentic microbial content, we developed improved methods for filtering environmental contamination. METHODS: We analyzed specimens from subjects with sarcoidosis (n = 93), control subjects without sarcoidosis (n = 72), and various environmental controls (n = 150). Sarcoidosis specimens consisted of two independent sets of formalin-fixed, paraffin-embedded lymph node biopsies, BAL, Kveim reagent, and fresh granulomatous spleen from a patient with sarcoidosis. All specimens were analyzed by bacterial 16S and fungal internal transcribed spacer ribosomal RNA gene sequencing. In addition, BAL was analyzed by shotgun sequencing of fractions enriched for viral particles, and Kveim and spleen were subjected to whole-genome shotgun sequencing. MEASUREMENTS AND MAIN RESULTS: In one tissue set, fungi in the Cladosporiaceae family were enriched in sarcoidosis compared with nonsarcoidosis tissues; in the other tissue set, we detected enrichment of several bacterial lineages in sarcoidosis but not Cladosporiaceae. BAL showed limited enrichment of Aspergillus fungi. Several microbial lineages were detected in Kveim and spleen, including Cladosporium. No microbial lineage was enriched in more than one sample type after correction for multiple comparisons. CONCLUSIONS: Metagenomic sequencing revealed enrichment of microbes in single types of sarcoidosis samples but limited concordance across sample types. Statistical analysis accounting for environmental contamination was essential to avoiding false positives.

swga: a primer design toolkit for selective whole genome amplification.

MOTIVATION: Population genomic analyses are often hindered by difficulties in obtaining sufficient numbers of genomes for analysis by DNA sequencing. Selective whole-genome amplification (SWGA) provides an efficient approach to amplify microbial genomes from complex backgrounds for sequence acquisition. However, the process of designing sets of primers for this method has many degrees of freedom and would benefit from an automated process to evaluate the vast number of potential primer sets. RESULTS: Here, we present swga , a program that identifies primer sets for SWGA and evaluates them for efficiency and selectivity. We used swga to design and test primer sets for the selective amplification of Wolbachia pipientis genomic DNA from infected Drosophila melanogaster and Mycobacterium tuberculosis from human blood. We identify primer sets that successfully amplify each against their backgrounds and describe a general method for using swga for arbitrary targets. In addition, we describe characteristics of primer sets that correlate with successful amplification, and present guidelines for implementation of SWGA to detect new targets. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are freely available on https://www.github.com/eclarke/swga . The program is implemented in Python and C and licensed under the GNU Public License. CONTACT: ecl@mail.med.upenn.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Gene Wiki in 2011: community intelligence applied to human gene annotation.

The Gene Wiki is an open-access and openly editable collection of Wikipedia articles about human genes. Initiated in 2008, it has grown to include articles about more than 10,000 genes that, collectively, contain more than 1.4 million words of gene-centric text with extensive citations back to the primary scientific literature. This growing body of useful, gene-centric content is the result of the work of thousands of individuals throughout the scientific community. Here, we describe recent improvements to the automated system that keeps the structured data presented on Gene Wiki articles in sync with the data from trusted primary databases. We also describe the expanding contents, editors and users of the Gene Wiki. Finally, we introduce a new automated system, called WikiTrust, which can effectively compute the quality of Wikipedia articles, including Gene Wiki articles, at the word level. All articles in the Gene Wiki can be freely accessed and edited at Wikipedia, and additional links and information can be found at the project's Wikipedia portal page: http://en.wikipedia.org/wiki/Portal:Gene_Wiki.

Sunbeam: an extensible pipeline for analyzing metagenomic sequencing experiments.

BACKGROUND: Analysis of mixed microbial communities using metagenomic sequencing experiments requires multiple preprocessing and analytical steps to interpret the microbial and genetic composition of samples. Analytical steps include quality control, adapter trimming, host decontamination, metagenomic classification, read assembly, and alignment to reference genomes. RESULTS: We present a modular and user-extensible pipeline called Sunbeam that performs these steps in a consistent and reproducible fashion. It can be installed in a single step, does not require administrative access to the host computer system, and can work with most cluster computing frameworks. We also introduce Komplexity, a software tool to eliminate potentially problematic, low-complexity nucleotide sequences from metagenomic data. A unique component of the Sunbeam pipeline is an easy-to-use extension framework that enables users to add custom processing or analysis steps directly to the workflow. The pipeline and its extension framework are well documented, in routine use, and regularly updated. CONCLUSIONS: Sunbeam provides a foundation to build more in-depth analyses and to enable comparisons in metagenomic sequencing experiments by removing problematic, low-complexity reads and standardizing post-processing and analytical steps. Sunbeam is written in Python using the Snakemake workflow management software and is freely available at github.com/sunbeam-labs/sunbeam under the GPLv3.

Molecular analysis of bacterial contamination on stethoscopes in an intensive care unit.

BACKGROUND: Culture-based studies, which focus on individual organisms, have implicated stethoscopes as potential vectors of nosocomial bacterial transmission. However, the full bacterial communities that contaminate in-use stethoscopes have not been investigated. METHODS: We used bacterial 16S rRNA gene deep-sequencing, analysis, and quantification to profile entire bacterial populations on stethoscopes in use in an intensive care unit (ICU), including practitioner stethoscopes, individual-use patient-room stethoscopes, and clean unused individual-use stethoscopes. Two additional sets of practitioner stethoscopes were sampled before and after cleaning using standardized or practitioner-preferred methods. RESULTS: Bacterial contamination levels were highest on practitioner stethoscopes, followed by patient-room stethoscopes, whereas clean stethoscopes were indistinguishable from background controls. Bacterial communities on stethoscopes were complex, and community analysis by weighted UniFrac showed that physician and patient-room stethoscopes were indistinguishable and significantly different from clean stethoscopes and background controls. Genera relevant to healthcare-associated infections (HAIs) were common on practitioner stethoscopes, among which Staphylococcus was ubiquitous and had the highest relative abundance (6.8%-14% of contaminating bacterial sequences). Other HAI-related genera were also widespread although lower in abundance. Cleaning of practitioner stethoscopes resulted in a significant reduction in bacterial contamination levels, but these levels reached those of clean stethoscopes in only a few cases with either standardized or practitioner-preferred methods, and bacterial community composition did not significantly change. CONCLUSIONS: Stethoscopes used in an ICU carry bacterial DNA reflecting complex microbial communities that include nosocomially important taxa. Commonly used cleaning practices reduce contamination but are only partially successful at modifying or eliminating these communities.

Upper Respiratory Dysbiosis with a Facultative-dominated Ecotype in Advanced Lung Disease and Dynamic Change after Lung Transplant.

Rationale: The oropharyngeal microbiome is a primary source of lung microbiota, contributes to lower respiratory infection, and is also a driver of oral health.Objectives: We sought to understand oropharyngeal microbial communities in advanced lung disease, community dynamics after lung transplantation, and ecological features of dysbiosis.Methods: Oropharyngeal wash samples were obtained from individuals with end-stage disease awaiting transplantation (n = 22) and longitudinally from individuals at 6 weeks, 3 months, and 6 months after transplantation (n = 33), along with healthy control subjects (n = 14). Bacterial 16S and fungal internal transcribed spacer rRNA regions were deep-sequenced, and bacterial community respiratory patterns were imputed from taxonomic composition.Results: Healthy subjects' oropharyngeal microbiomes showed a gradient of community types reflecting relative enrichment of strictly anaerobic, aerobic, or facultative anaerobic bacteria. Patients with end-stage lung disease showed severe dysbiosis by both taxonomic composition and respiration phenotypes, with reduced richness and diversity, increased facultative and decreased aerobic bacteria, and absence of communities characterized by obligate aerobes. In patients at 6 weeks and 3 months post-transplant, richness and diversity were intermediate between healthy and pretransplant subjects, with near-normal distribution of community types. However, by 6 months post-transplant, oropharyngeal wash resembled the low-diversity facultative-dominated profile of pretransplant subjects. Community ecotype correlated with Candida abundance.Conclusions: End-stage lung disease is associated with marked upper respiratory tract dysbiosis involving both community structure and respiratory metabolism profiles of constituent bacteria. Dynamic changes occur after lung transplantation, with partial normalization early but later appearance of severe dysbiosis similar to pretransplant patients. Aberrant oropharyngeal communities may predispose to abnormal lung microbiota and infection risk both in advanced lung disease and after transplantation.

Lack of detection of a human placenta microbiome in samples from preterm and term deliveries.

BACKGROUND: Historically, the human womb has been thought to be sterile in healthy pregnancies, but this idea has been challenged by recent studies using DNA sequence-based methods, which have suggested that the womb is colonized with bacteria. For example, analysis of DNA from placenta samples yielded small proportions of microbial sequences which were proposed to represent normal bacterial colonization. However, an analysis by our group showed no distinction between background negative controls and placenta samples. Also supporting the idea that the womb is sterile is the observation that germ-free mammals can be generated by sterile delivery of neonates into a sterile isolator, after which neonates remain germ-free, which would seem to provide strong data in support of sterility of the womb. RESULTS: To probe this further and to investigate possible placental colonization associated with spontaneous preterm birth, we carried out another study comparing microbiota in placenta samples from 20 term and 20 spontaneous preterm deliveries. Both 16S rRNA marker gene sequencing and shotgun metagenomic sequencing were used to characterize placenta and control samples. We first quantified absolute amounts of bacterial 16S rRNA gene sequences using 16S rRNA gene quantitative PCR (qPCR). As in our previous study, levels were found to be low in the placenta samples and indistinguishable from negative controls. Analysis by DNA sequencing did not yield a placenta microbiome distinct from negative controls, either using marker gene sequencing as in our previous work, or with shotgun metagenomic sequencing. Several types of artifacts, including erroneous read classifications and barcode misattribution, needed to be identified and removed from the data to clarify this point. CONCLUSIONS: Our findings do not support the existence of a consistent placental microbiome, in either placenta from term deliveries or spontaneous preterm births.

Evaluation of a therapy for Idiopathic Chronic Enterocolitis in rhesus macaques (Macaca mulatta) and linked microbial community correlates.

Idiopathic chronic enterocolitis (ICE) is one of the most commonly encountered and difficult to manage diseases of captive rhesus macaques (Macaca mulatta). The etiology is not well understood, but perturbations in gut microbial communities have been implicated. Here we evaluated the effects of a 14-day course of vancomycin, neomycin, and fluconazole on animals affected with ICE, comparing treated, untreated, and healthy animals. We performed microbiome analysis on duodenal and colonic mucosal samples and feces in order to probe bacterial and/or fungal taxa potentially associated with ICE. All treated animals showed a significant and long-lasting improvement in stool consistency over time when compared to untreated and healthy controls. Microbiome analysis revealed trends associating bacterial community composition with ICE, particularly lineages of the Lactobacillaceae family. Sequencing of DNA from macaque food biscuits revealed that fungal sequences recovered from stool were dominated by yeast-derived food additives; in contrast, bacteria in stool appeared to be authentic gut residents. In conclusion, while validation in larger cohorts is needed, the treatment described here was associated with significantly improved clinical signs; results suggested possible correlates of microbiome structure with disease, though no strong associations were detected between single microbes and ICE.

T cell dynamics and response of the microbiota after gene therapy to treat X-linked severe combined immunodeficiency.

BACKGROUND: Mutation of the IL2RG gene results in a form of severe combined immune deficiency (SCID-X1), which has been treated successfully with hematopoietic stem cell gene therapy. SCID-X1 gene therapy results in reconstitution of the previously lacking T cell compartment, allowing analysis of the roles of T cell immunity in humans by comparing before and after gene correction. METHODS: Here we interrogate T cell reconstitution using four forms of high throughput analysis. (1) Estimation of the numbers of transduced progenitor cells by monitoring unique positions of integration of the therapeutic gene transfer vector. (2) Estimation of T cell population structure by sequencing of the recombined T cell receptor (TCR) beta locus. (3) Metagenomic analysis of microbial populations in oropharyngeal, nasopharyngeal, and gut samples. (4) Metagenomic analysis of viral populations in gut samples. RESULTS: Comparison of progenitor and mature T cell populations allowed estimation of a minimum number of cell divisions needed to generate the observed populations. Analysis of microbial populations showed the effects of immune reconstitution, including normalization of gut microbiota and clearance of viral infections. Metagenomic analysis revealed enrichment of genes for antibiotic resistance in gene-corrected subjects relative to healthy controls, likely a result of higher healthcare exposure. CONCLUSIONS: This multi-omic approach enables the characterization of multiple effects of SCID-X1 gene therapy, including T cell repertoire reconstitution, estimation of numbers of cell divisions between progenitors and daughter T cells, normalization of the microbiome, clearance of microbial pathogens, and modulations in antibiotic resistance gene levels. Together, these results quantify several aspects of the long-term efficacy of gene therapy for SCID-X1. This study includes data from ClinicalTrials.gov numbers NCT01410019, NCT01175239, and NCT01129544.

A task-based approach for Gene Ontology evaluation.

BACKGROUND: The Gene Ontology and its associated annotations are critical tools for interpreting lists of genes. Here, we introduce a method for evaluating the Gene Ontology annotations and structure based on the impact they have on gene set enrichment analysis, along with an example implementation. This task-based approach yields quantitative assessments grounded in experimental data and anchored tightly to the primary use of the annotations. RESULTS: Applied to specific areas of biological interest, our framework allowed us to understand the progress of annotation and structural ontology changes from 2004 to 2012. Our framework was also able to determine that the quality of annotations and structure in the area under test have been improving in their ability to recall underlying biological traits. Furthermore, we were able to distinguish between the impact of changes to the annotation sets and ontology structure. CONCLUSION: Our framework and implementation lay the groundwork for a powerful tool in evaluating the usefulness of the Gene Ontology. We demonstrate both the flexibility and the power of this approach in evaluating the current and past state of the Gene Ontology as well as its applicability in developing new methods for creating gene annotations.

Linking genes to diseases with a SNPedia-Gene Wiki mashup.

BACKGROUND: A variety of topic-focused wikis are used in the biomedical sciences to enable the mass-collaborative synthesis and distribution of diverse bodies of knowledge. To address complex problems such as defining the relationships between genes and disease, it is important to bring the knowledge from many different domains together. Here we show how advances in wiki technology and natural language processing can be used to automatically assemble 'meta-wikis' that present integrated views over the data collaboratively created in multiple source wikis. RESULTS: We produced a semantic meta-wiki called the Gene Wiki+ that automatically mirrors and integrates data from the Gene Wiki and SNPedia. The Gene Wiki+, available at (http://genewikiplus.org/), captures 8,047 distinct gene-disease relationships. SNPedia accounts for 4,149 of the gene-disease pairs, the Gene Wiki provides 4,377 and only 479 appear independently in both sources. All of this content is available to query and browse and is provided as linked open data. CONCLUSIONS: Wikis contain increasing amounts of diverse, biological information useful for elucidating the connections between genes and disease. The Gene Wiki+ shows how wiki technology can be used in concert with natural language processing to provide integrated views over diverse underlying data sources.

Building a biomedical semantic network in Wikipedia with Semantic Wiki Links.

Wikipedia is increasingly used as a platform for collaborative data curation, but its current technical implementation has significant limitations that hinder its use in biocuration applications. Specifically, while editors can easily link between two articles in Wikipedia to indicate a relationship, there is no way to indicate the nature of that relationship in a way that is computationally accessible to the system or to external developers. For example, in addition to noting a relationship between a gene and a disease, it would be useful to differentiate the cases where genetic mutation or altered expression causes the disease. Here, we introduce a straightforward method that allows Wikipedia editors to embed computable semantic relations directly in the context of current Wikipedia articles. In addition, we demonstrate two novel applications enabled by the presence of these new relationships. The first is a dynamically generated information box that can be rendered on all semantically enhanced Wikipedia articles. The second is a prototype gene annotation system that draws its content from the gene-centric articles on Wikipedia and exposes the new semantic relationships to enable previously impossible, user-defined queries. DATABASE URL: http://en.wikipedia.org/wiki/Portal:Gene_Wiki.