Intestinal domination and the risk of bacteremia in patients undergoing allogeneic hematopoietic stem cell transplantation.
ABSTRACT Background. Bacteremia is a frequent complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT). It is unclear whether changes in the intestinal microbiota during allo-HSCT contribute to the development of bacteremia. We examined the microbiota of patients undergoing allo-HSCT, and correlated microbial shifts with the risk of bacteremia. Methods. Fecal specimens were collected longitudinally from 94 patients undergoing allo-HSCT, from before transplant until 35 days after transplant. The intestinal microbiota was characterized by 454 pyrosequencing of the V1-V3 region of bacterial 16S ribosomal RNA genes. Microbial diversity was estimated by grouping sequences into operational taxonomic units and calculating the Shannon diversity index. Phylogenetic classification was obtained using the Ribosomal Database Project classifier. Associations of the microbiota with clinical predictors and outcomes were evaluated. Results. During allo-HSCT, patients developed reduced diversity, with marked shifts in bacterial populations inhabiting the gut. Intestinal domination, defined as occupation of at least 30% of the microbiota by a single predominating bacterial taxon, occurred frequently. Commonly encountered dominating organisms included Enterococcus, Streptococcus, and various Proteobacteria. Enterococcal domination was increased 3-fold by metronidazole administration, whereas domination by Proteobacteria was reduced 10-fold by fluoroquinolone administration. As a predictor of outcomes, enterococcal domination increased the risk of Vancomycin-resistant Enterococcus bacteremia 9-fold, and proteobacterial domination increased the risk of gram-negative rod bacteremia 5-fold. Conclusions. During allo-HSCT, the diversity and stability of the intestinal flora are disrupted, resulting in domination by bacteria associated with subsequent bacteremia. Assessment of fecal microbiota identifies patients at highest risk for bloodstream infection during allo-HCST.
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ABSTRACT: Background Microbiome studies incorporate next-generation sequencing to obtain profiles of microbial communities. Data generated from these experiments are high-dimensional with a rich correlation structure but modest sample sizes. A statistical model that utilizes these microbiome profiles to explain a clinical or biological endpoint needs to tackle high-dimensionality resulting from the very large space of variable configurations. Ensemble models are a class of approaches that can address high-dimensionality by aggregating information across large model spaces. Although such models are popular in fields as diverse as economics and genetics, their performance on microbiome data has been largely unexplored.ResultsWe developed a simulation framework that accurately captures the constraints of experimental microbiome data. Using this setup, we systematically evaluated a selection of both frequentist and Bayesian regression modeling ensembles. These are represented by variants of stability selection in conjunction with elastic net and spike-and-slab Bayesian model averaging (BMA), respectively. BMA ensembles that explore a larger space of models relative to stability selection variants performed better and had lower variability across simulations. However, stability selection ensembles were able to match the performance of BMA in scenarios of low sparsity where several variables had large regression coefficients.Conclusions Given a microbiome dataset of interest, we present a methodology to generate simulated data that closely mimics its characteristics in a manner that enables meaningful evaluation of analytical strategies. Our evaluation demonstrates that the largest ensembles yield the strongest performance on microbiome data with modest sample sizes and high-dimensional measurements. We also demonstrate the ability of these ensembles to identify microbiome signatures that are associated with opportunistic Candida albicans colonization during antibiotic exposure. As the focus of microbiome research evolves from pilot to translational studies, we anticipate that our strategy will aid investigators in making evaluation-based decisions for selecting appropriate analytical methods.BMC Bioinformatics 02/2015; 16(1):31. DOI:10.1186/s12859-015-0467-6 · 2.67 Impact Factor
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ABSTRACT: The diversity of enterococcal populations from fecal samples of hospitalized (n=133) and non-hospitalized individuals (n=173) of different age groups (I:0-19y; II:20-59y; III:≥60y) was analyzed. Enterococci were recovered at similar rates among hospitalized and non-hospitalized persons (77.44%-79.77%) of all age groups (75.0%-82.61%). Enterococcus faecalis (Efc) and Enterococcus faecium (Efm) were predominant although seven other Enterococcus species were identified. Efc and Efm (including ampicillin-resistant-Efm, AREfm) colonization rates in non-hospitalized persons were age-independent. For inpatients, Efc colonization rates were age-independent, but Efm rates (particularly AREfm) significantly increased with age. The population structure of Efm and Efc was determined by superimposing goeBURST and Bayesian Analysis of Population Structure (BAPS). Most Efm STs (150, 75 STs) were linked to BAPS groups 1 (22.0%), 2 (31.3%) and 3 (36.7%). Positive association was found between hospital isolates and BAPS 2.1a and 3.3a (which included major AREfm human lineages), and between community-based ASEfm isolates and BAPS 1.2 and 3.3b. Most Efc isolates (130, 58 STs) were grouped in 3 BAPS: 1 (36.9%), 2 (40.0%) and 3 (23.1%), each one comprising widespread lineages. No positive associations with age or hospitalization were established. The diversity and dynamics of enterococcal populations in fecal microbiota of healthy humans is largely unexplored, available knowledge being fragmented and contradictory. The study offers a novel and comprehensive analysis of enterococcal population landscapes and suggests that Efm populations from hospitalized patients and from community-based individuals differ, with predominance of certain clonal lineages in the hospital setting, often associated with elderly. Copyright © 2014, American Society for Microbiology. All Rights Reserved.Applied and Environmental Microbiology 12/2014; 81(5). DOI:10.1128/AEM.03661-14 · 3.95 Impact Factor
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ABSTRACT: In the past decade, appreciation of the important effects of commensal microbes on immunity has grown exponentially. The effect of the microbiota on transplantation has only recently begun to be explored; however, our understanding of the mechanistic details of host-microbe interactions is still lacking. It has become clear that transplantation is associated with changes in the microbiota in many different settings, although what clinical events and therapeutic interventions contribute to these changes remains to be parsed out. Research groups have begun to identify associations between specific communities of organisms and transplant outcomes, but it remains to be established whether microbial changes precede or follow transplant rejection episodes. Finally, results from continuing exploration of basic mechanisms by which microbial communities affect innate and adaptive immunity in various animal models of disease continue to inform research on the microbiota's effects on immune responses against transplanted organs. Commensal microbes may alter immune responses to organ transplantation, but direct experiments are only beginning in the field to identify species and immune pathways responsible for these putative effects.Current Opinion in Organ Transplantation 02/2015; 20(1):1-7. DOI:10.1097/MOT.0000000000000150 · 2.38 Impact Factor