The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing

© 2008 Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The definitive version was published in PLoS Biology 6 (2008): e280, doi:10.1371/journal.pbio.0060280.The intestinal microbiota is essential to human health, with effects on nutrition, metabolism, pathogen resistance, and other processes. Antibiotics may disrupt these interactions and cause acute disease, as well as contribute to chronic health problems, although technical challenges have hampered research on this front. Several recent studies have characterized uncultured and complex microbial communities by applying a new, massively parallel technology to obtain hundreds of thousands of sequences of a specific variable region within the small subunit rRNA gene. These shorter sequences provide an indication of diversity. We used this technique to track changes in the intestinal microbiota of three healthy humans before and after treatment with the antibiotic ciprofloxacin, with high sensitivity and resolution, and without sacrificing breadth of coverage. Consistent with previous results, we found that the microbiota of these individuals was similar at the genus level, but interindividual differences were evident at finer scales. Ciprofloxacin reduced the diversity of the intestinal microbiota, with significant effects on about one-third of the bacterial taxa. Despite this pervasive disturbance, the membership of the communities had largely returned to the pretreatment state within 4 weeks

DRISEE overestimates errors in metagenomic sequencing data

© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Briefings in Bioinformatics 15 (2014): 783-787, doi:10.1093/bib/bbt010.The extremely high error rates reported by Keegan et al. in ‘A platform-independent method for detecting errors in metagenomic sequencing data: DRISEE’ (PLoS Comput Biol 2012;8:e1002541) for many next-generation sequencing datasets prompted us to re-examine their results. Our analysis reveals that the presence of conserved artificial sequences, e.g. Illumina adapters, and other naturally occurring sequence motifs accounts for most of the reported errors. We conclude that DRISEE reports inflated levels of sequencing error, particularly for Illumina data. Tools offered for evaluating large datasets need scrupulous review before they are implemented.National Institutes of Health [1UH2DK083993 to M.L.S.]; National Science Foundation [BDI- 096026 to S.M.H.]

Reproducible community dynamics of the gastrointestinal microbiota following antibiotic perturbation

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of American Society for Microbiology for personal use, not for redistribution. The definitive version was published in Infection and Immunity 77 (2009): 2367-2375, doi:10.1128/IAI.01520-08.Shifts in microbial communities are implicated in the pathogenesis of a number of gastrointestinal diseases, but we have limited understanding of the mechanisms that lead to altered community structures. One difficulty with studying these mechanisms in human subjects is the inherent baseline variability of the microbiota in different individuals that arise due to varying life histories. To try and overcome this baseline variability we employed a mouse model to control host genotype, diet and other possible influences on the microbiota. This allowed us to determine if the indigenous microbiota in such mice had a stable baseline community structure and whether this community exhibited a consistent response following antibiotic administration. We employed a tag sequencing strategy targeting the V6 hypervariable region of the bacterial small-subunit (16S) ribosomal RNA combined with massively parallel sequencing to determine the community structure of the gut microbiota. Inbred mice in a controlled environment harbored a reproducible baseline community that was significantly impacted by antibiotic administration. The ability of the gut microbial community to recover to baseline following cessation of antibiotic administration varied according to the antibiotic regimen administered. Severe antibiotic pressure resulted in reproducible long-lasting alterations in the gut microbial community including a decrease in overall diversity. The finding of stereotypic responses of the indigenous microbiota to ecologic stress implies that a better understanding of the factors that govern community structure could lead to strategies for the intentional manipulation of this ecosystem to preserve or restore a healthy microbiota.The main projects were funded in whole with federal funds from the NIAID, NIH, Department of Health and Human Services, under contract number N01-AI-30058. Additional funding was supplied via subcontracts from the Woods Hole Center for Oceans and Human Health from the National Institutes of Health and National Science Foundation (NIH/NIEHS 1 P50 ES012742-01 and NSF/OCE 0430724-J. Stegeman PI to H.G.M. and M.L.S. and R01 DK070875 to V.B.Y.) and a grants from the W.M. Keck Foundation and the G. Unger Vetlesen Foundation (to M.L.S.). D.A.A. was supported by the National Institutes of Health under a Ruth L. Kirschstein National Research Service Award (T32 HL07749)

Accuracy and quality of massively parallel DNA pyrosequencing

© 2007 Huse et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The definitive version was published in Genome Biology 8 (2007): R143, doi:10.1186/gb-2007-8-7-r143.Additional data file 1 is a fasta file of the 43 known sequences used. Additional data file 2 is a gzip-compressed fasta file of the sequences output by the GS20. These sequences correspond to those included in Additional data files 3, 4, 5 but include only the final sequence information. Additional data files 3, 4, 5 are three compressed text files representing the text translations of the original GS20 binary output (sff) files for all of the sequencing used in the analysis, including sequence, flowgram and other run information. GS20 data are reported by region of the PicoTiterPlate™; we sequenced three plate regions.Massively parallel pyrosequencing systems have increased the efficiency of DNA sequencing, although the published per-base accuracy of a Roche GS20 is only 96%. In genome projects, highly redundant consensus assemblies can compensate for sequencing errors. In contrast, studies of microbial diversity that catalogue differences between PCR amplicons of ribosomal RNA genes (rDNA) or other conserved gene families cannot take advantage of consensus assemblies to detect and minimize incorrect base calls. We performed an empirical study of the per-base error rate for the Roche GS20 system using sequences of the V6 hypervariable region from cloned microbial ribosomal DNA (tag sequencing). We calculated a 99.5% accuracy rate in unassembled sequences, and identified several factors that can be used to remove a small percentage of low-quality reads, improving the accuracy to 99.75% or better. By using objective criteria to eliminate low quality data, the quality of individual GS20 sequence reads in molecular ecological applications can surpass the accuracy of traditional capillary methods.This work was supported by National Aeronautics and Space Administration Astrobiology Institute Cooperative Agreement NNA04CC04A (to MLS), subcontracts from the Woods Hole Center for Oceans and Human Health from the National Institutes of Health and National Science Foundation (NIH/NIEHS 1 P50 ES012742-01 and NSF/OCE 0430724-J Stegeman PI to HGM and MLS), grants from the WM Keck Foundation and the G Unger Vetlesen Foundation (to MLS), and a National Research Council Research Associateship Award (to JAH)

Exploring microbial diversity and taxonomy using SSU rRNA hypervariable tag sequencing

© 2008 Huse et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License. The definitive version was published in PLoS Genetics 4 (2008): e1000255, doi:10.1371/journal.pgen.1000255.Massively parallel pyrosequencing of hypervariable regions from small subunit ribosomal RNA (SSU rRNA) genes can sample a microbial community two or three orders of magnitude more deeply per dollar and per hour than capillary sequencing of full-length SSU rRNA. As with full-length rRNA surveys, each sequence read is a tag surrogate for a single microbe. However, rather than assigning taxonomy by creating gene trees de novo that include all experimental sequences and certain reference taxa, we compare the hypervariable region tags to an extensive database of rRNA sequences and assign taxonomy based on the best match in a Global Alignment for Sequence Taxonomy (GAST) process. The resulting taxonomic census provides information on both composition and diversity of the microbial community. To determine the effectiveness of using only hypervariable region tags for assessing microbial community membership, we compared the taxonomy assigned to the V3 and V6 hypervariable regions with the taxonomy assigned to full-length SSU rRNA sequences isolated from both the human gut and a deep-sea hydrothermal vent. The hypervariable region tags and full-length rRNA sequences provided equivalent taxonomy and measures of relative abundance of microbial communities, even for tags up to 15% divergent from their nearest reference match. The greater sampling depth per dollar afforded by massively parallel pyrosequencing reveals many more members of the “rare biosphere” than does capillary sequencing of the full-length gene. In addition, tag sequencing eliminates cloning bias and the sequences are short enough to be completely sequenced in a single read, maximizing the number of organisms sampled in a run while minimizing chimera formation. This technique allows the cost-effective exploration of changes in microbial community structure, including the rare biosphere, over space and time and can be applied immediately to initiatives, such as the Human Microbiome Project.Woods Hole Center for Oceans and Human Health from the National Institutes of Health and National Science Foundation (NIH/NIEHS 1 P50 ES012742-01 and NSF/OCE 0430724-J Stegeman PI to MLS). NIH Director's Pioneer Award and Doris Duke Distinguished Clinical Scientist Award to DAR

VAMPS : a website for visualization and analysis of microbial population structures

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in BMC Bioinformatics 15 (2014): 41, doi:10.1186/1471-2105-15-41.The advent of next-generation DNA sequencing platforms has revolutionized molecular microbial ecology by making the detailed analysis of complex communities over time and space a tractable research pursuit for small research groups. However, the ability to generate 105–108 reads with relative ease brings with it many downstream complications. Beyond the computational resources and skills needed to process and analyze data, it is difficult to compare datasets in an intuitive and interactive manner that leads to hypothesis generation and testing. We developed the free web service VAMPS (Visualization and Analysis of Microbial Population Structures, http://vamps.mbl.edu webcite) to address these challenges and to facilitate research by individuals or collaborating groups working on projects with large-scale sequencing data. Users can upload marker gene sequences and associated metadata; reads are quality filtered and assigned to both taxonomic structures and to taxonomy-independent clusters. A simple point-and-click interface allows users to select for analysis any combination of their own or their collaborators’ private data and data from public projects, filter these by their choice of taxonomic and/or abundance criteria, and then explore these data using a wide range of analytic methods and visualizations. Each result is extensively hyperlinked to other analysis and visualization options, promoting data exploration and leading to a greater understanding of data relationships. VAMPS allows researchers using marker gene sequence data to analyze the diversity of microbial communities and the relationships between communities, to explore these analyses in an intuitive visual context, and to download data, results, and images for publication. VAMPS obviates the need for individual research groups to make the considerable investment in computational infrastructure and bioinformatic support otherwise necessary to process, analyze, and interpret massive amounts of next-generation sequence data. Any web-capable device can be used to upload, process, explore, and extract data and results from VAMPS. VAMPS encourages researchers to share sequence and metadata, and fosters collaboration between researchers of disparate biomes who recognize common patterns in shared data.Funding provided by the National Science Foundation [grant NSF/BDI 0960626 to SMH] and the Sloan Foundation through a collaborative project with the Microbiology of the Built Environment program

Prevalence of Streptococci and Increased Polymicrobial Diversity Associated with Cystic Fibrosis Patient Stability

Diverse microbial communities chronically colonize the lungs of cystic fibrosis patients. Pyrosequencing of amplicons for hypervariable regions in the 16S rRNA gene generated taxonomic profiles of bacterial communities for sputum genomic DNA samples from 22 patients during a state of clinical stability (outpatients) and 13 patients during acute exacerbation (inpatients). We employed quantitative PCR (qPCR) to confirm the detection of Pseudomonas aeruginosa and Streptococcus by the pyrosequencing data and human oral microbe identification microarray (HOMIM) analysis to determine the species of the streptococci identified by pyrosequencing. We show that outpatient sputum samples have significantly higher bacterial diversity than inpatients, but maintenance treatment with tobramycin did not impact overall diversity. Contrary to the current dogma in the field that Pseudomonas aeruginosa is the dominant organism in the majority of cystic fibrosis patients, Pseudomonas constituted the predominant genera in only half the patient samples analyzed and reported here. The increased fractional representation of Streptococcus in the outpatient cohort relative to the inpatient cohort was the strongest predictor of clinically stable lung disease. The most prevalent streptococci included species typically associated with the oral cavity (Streptococcus salivarius and Streptococcus parasanguis) and the Streptococcus milleri group species. These species of Streptococcus may play an important role in increasing the diversity of the cystic fibrosis lung environment and promoting patient stability

The Microbiome in Pediatric Cystic Fibrosis Patients: The Role of Shared Environment Suggests a Window of Intervention

Cystic fibrosis (CF) is caused by mutations in the CFTR gene that predispose the airway to infection. Chronic infection by pathogens such as Pseudomonas aeruginosa leads to inflammation that gradually degrades lung function, resulting in morbidity and early mortality. In a previous study of CF monozygotic twins, we demonstrate that genetic modifiers significantly affect the establishment of persistent P. aeruginosa colonization in CF. Recognizing that bacteria other than P. aeruginosa contribute to the CF microbiome and associated pathology, we used deep sequencing of sputum from pediatric monozygotic twins and nontwin siblings with CF to characterize pediatric bacterial communities and the role that genetics plays in their evolution. We found that the microbial communities in sputum from pediatric patients living together were much more alike than those from pediatric individuals living apart, regardless of whether samples were taken from monozygous twins or from nontwin CF siblings living together, which we used as a proxy for dizygous twins. In contrast, adult communities were comparatively monolithic and much less diverse than the microbiome of pediatric patients

Serial Analysis of the Gut and Respiratory Microbiome in Cystic Fibrosis in Infancy: Interaction between Intestinal and Respiratory Tracts and Impact of Nutritional Exposures

Pulmonary damage caused by chronic colonization of the cystic fibrosis (CF) lung by microbial communities is the proximal cause of respiratory failure. While there has been an effort to document the microbiome of the CF lung in pediatric and adult patients, little is known regarding the developing microflora in infants. We examined the respiratory and intestinal microbiota development in infants with CF from birth to 21 months. Distinct genera dominated in the gut compared to those in the respiratory tract, yet some bacteria overlapped, demonstrating a core microbiota dominated by Veillonella and Streptococcus. Bacterial diversity increased significantly over time, with evidence of more rapidly acquired diversity in the respiratory tract. There was a high degree of concordance between the bacteria that were increasing or decreasing over time in both compartments; in particular, a significant proportion (14/16 genera) increasing in the gut were also increasing in the respiratory tract. For 7 genera, gut colonization presages their appearance in the respiratory tract. Clustering analysis of respiratory samples indicated profiles of bacteria associated with breast-feeding, and for gut samples, introduction of solid foods even after adjustment for the time at which the sample was collected. Furthermore, changes in diet also result in altered respiratory microflora, suggesting a link between nutrition and development of microbial communities in the respiratory tract. Our findings suggest that nutritional factors and gut colonization patterns are determinants of the microbial development of respiratory tract microbiota in infants with CF and present opportunities for early intervention in CF with altered dietary or probiotic strategies

Characterization and quantification of the fungal microbiome in serial samples from individuals with cystic fibrosis

Abstract Background Human-associated microbial communities include fungi, but we understand little about which fungal species are present, their relative and absolute abundances, and how antimicrobial therapy impacts fungal communities. The disease cystic fibrosis (CF) often involves chronic airway colonization by bacteria and fungi, and these infections cause irreversible lung damage. Fungi are detected more frequently in CF sputum samples upon initiation of antimicrobial therapy, and several studies have implicated the detection of fungi in sputum with worse outcomes. Thus, a more complete understanding of fungi in CF is required. Results We characterized the fungi and bacteria in expectorated sputa from six CF subjects. Samples were collected upon admission for systemic antibacterial therapy and upon the completion of treatment and analyzed using a pyrosequencing-based analysis of fungal internal transcribed spacer 1 (ITS1) and bacterial 16S rDNA sequences. A mixture of Candida species and Malassezia dominated the mycobiome in all samples (74%–99% of fungal reads). There was not a striking trend correlating fungal and bacterial richness, and richness showed a decline after antibiotic therapy particularly for the bacteria. The fungal communities within a sputum sample resembled other samples from that subject despite the aggressive antibacterial therapy. Quantitative PCR analysis of fungal 18S rDNA sequences to assess fungal burden showed variation in fungal density in sputum before and after antibacterial therapy but no consistent directional trend. Analysis of Candida ITS1 sequences amplified from sputum or pure culture-derived genomic DNA from individual Candida species found little (<0.5%) or no variation in ITS1 sequences within or between strains, thereby validating this locus for the purpose of Candida species identification. We also report the enhancement of the publically available Visualization and Analysis of Microbial Population Structures (VAMPS) tool for the analysis of fungal communities in clinical samples. Conclusions Fungi are present in CF respiratory sputum. In CF, the use of intravenous antibiotic therapy often does not profoundly impact bacterial community structure, and we observed a similar stability in fungal species composition. Further studies are required to predict the effects of antibacterials on fungal burden in CF and fungal community stability in non-CF populations.http://deepblue.lib.umich.edu/bitstream/2027.42/134558/1/40168_2014_Article_67.pd