Citations

Abstract Background Illumina paired-end sequencing has been increasingly popular for 16S rRNA gene-based microbiota profiling. It provides higher phylogenetic resolution than single-end reads due to a longer read length. However, the reverse read (R2) often has significant low base quality, and a large proportion of R2s will be discarded after quality control, resulting in a mixture of paired-end and single-end reads. A typical 16S analysis pipeline usually processes either paired-end or single-end reads but not a mixture. Thus, the quantification accuracy and statistical power will be reduced due to the loss of a large amount of reads. As a result, rare taxa may not be detectable with the paired-end approach, or low taxonomic resolution will result in a single-end approach. Results To have both the higher phylogenetic resolution provided by paired-end reads and the higher sequence coverage by single-end reads, we propose a novel OTU-picking pipeline, hybrid-denovo, that can process a hybrid of single-end and paired-end reads. Using high-quality paired-end reads as a gold standard, we show that hybrid-denovo achieved the highest correlation with the gold standard and performed better than the approaches based on paired-end or single-end reads in terms of quantifying the microbial diversity and taxonomic abundances. By applying our method to a rheumatoid arthritis (RA) data set, we demonstrated that hybrid-denovo captured more microbial diversity and identified more RA-associated taxa than a paired-end or single-end approach. Conclusions Hybrid-denovo utilizes both paired-end and single-end 16S sequencing reads and is recommended for 16S rRNA gene targeted paired-end sequencing data.

Abstract Background Detection and identification of microorganism(s) involved in periprosthetic joint infection (PJI) can inform surgical management and directed antibiotic therapy. Metagenomic shotgun sequencing is a powerful tool with the potential to change how many PJIs are diagnosed as it allows direct detection and identification of pathogens in clinical specimens. In the largest series to date, we utilized a metagenomics-based approach applied to SF to define potential microbial etiologies of failed total knee arthroplasties (TKAs). Methods Synovial fluid was collected from 112 failed TKAs [74 PJI and 38 aseptic implant failure (AF)] via preoperative arthrocentesis. Cell count and differential, standardized culture and DNA-based metagenomic shotgun sequencing were performed. Human DNA was depleted using the MolYsis basic kit prior to DNA extraction, whole genome amplification, and sequencing. Taxonomic assignment of reads and pathogen identification was achieved using a pipeline incorporating k-mer- and marker gene-based classification software. A scheme for analysis and filtration of false-positives was created and applied, incorporating cut-offs for the number of reads, quality scores, and coverage across a reference genome. Patients were classified as having PJI using the IDSA criteria and expert review. Analyses were recorded as percent agreement, with 95% confidence intervals (CI), of metagenomics to SF culture. Results Metagenomic analysis identified the known pathogen in 54 (90%) (CI, 79.5%–96.2%) of the 60 culture-positive PJIs analyzed and one (2%) (CI, 0.0%–8.9%) potential polymicrobial infection not detected by culture. For the 14 culture-negative PJIs tested, metagenomics showed 79% (CI, 49.2%–95.3%) agreement for negative findings; potential pathogens were identified in three (21%) (CI, 4.7%–50.8%) culture-negative PJI cases, with one being polymicrobial. Of the 37 culture-negative AF cases, metagenomics showed 97% (CI, 85.8%–99.9%) agreement with negative culture and identified one (3%) (CI, 0.0%–14.2%) potential pathogen. For the one culture-positive AF case, metagenomic results were negative, suggesting possible culture contamination. Conclusion Metagenomic shotgun sequencing performed on SF can be used to diagnose PJI and may be particularly useful for culture-negative PJI. Disclosures R. Patel, ASM: Board Member, None; CD Diagnostics, BioFire, Curetis, Merck, Hutchison Biofilm Medical Solutions, Accelerate Diagnostics, Allergan, and The Medicines Company: Grant Investigator, Grant recipient; Curetis: Consultant, Monies paid to my employer; A patent on Bordetella pertussis/parapertussis PCR issued, a patent on a device/method for sonication with royalties paid by Samsung to Mayo Clinic, and a patent on an anti-biofilm substance issued: Patents, Patents, any money is paid to my employer; Actelion: DSMB, Money paid to my employer; ASM and IDSA: Editor’s stipends, Editor’s stipends; NBME, Up-to-Date and the Infectious Diseases Board Review Course: NBME, Up-to-Date and the Infectious Diseases Board Review Course, Honoraria; Roche, ASM, and IDSA: Travel reimbursement, Travel reimbursement

ABSTRACT Methanobrevibacter smithii is a common inhabitant of the human gut. Here, we present a draft genome sequence of M. smithii isolate WWM1085, obtained from a human stool sample. This sequence will improve our understanding of the genetic diversity of this human-associated methanogen.

AbstractBackgroundWhile limited data suggest that the fecal microbiota in healthy people is stable over time, the intraindividual variability of the fecal microbiota in constipated patients is unknown.MethodsThis study evaluated the intraindividual reproducibility of fecal microbiota analyzed with 16S rRNA gene sequencing in two stool samples collected without and after a laxative, respectively, in 25 healthy people and 25 constipated women. Participants completed a food record for 3 d before the stool collection. Colonic transit was measured with scintigraphy.Key ResultsThe constipated patients were older (48±15 vs 39±10 y, P=.02) than healthy participants but had a similar BMI. The total daily caloric intake was less (P=.005) in constipated (1265±350 kcal) than healthy participants (1597±402 kcal). Fourteen patients but only two controls (P<.005), had delayed colonic transit. For most measures of alpha (eg, Observed OTU number, Shannon index) and beta diversity (eg, Bray‐Curtis dissimilarity, UniFrac, phyla level abundance), the ICCs between two stool samples were high, indicating moderate or strong agreement, and similar in healthy people and constipated patients. The ICC for the weighted UniFrac distance, which is weighted by abundance, was lower than its unweighted counterpart, indicating that the unweighted measure is more robust and reproducible.Conclusions and InferencesThe intraindividual reproducibility of fecal microbiota in constipated patients is high and comparable to healthy participants. For most purposes, evaluating the fecal microbiota in a single stool sample should generally suffice in adequately powered studies of healthy and constipated patients.

Abstract Endometrial cancer studies have led to a number of well-defined but mechanistically unconnected genetic and environmental risk factors. One of the emerging modulators between environmental triggers and genetic expression is the microbiome. Because of the inflammatory display in endometrial cancer, we set out to inquire about the composition of the uterine microbiome and its putative role in endometrial cancer. We undertook a pilot study of the microbiome in 238 samples taken from different locations along the entire female reproductive tract in hysterectomy patients with endometrial cancer (n=17), patients with endometrial hyperplasia (n=4) and patients afflicted with a benign uterine condition (n=10). Samples were collected aseptically both in the operating room and the pathology laboratory. Following DNA extraction, the 16S rDNA (V3-V5 region) microbial gene was amplified and sequenced to identify the microbiota present. The microbiome sequencing revealed a structural microbiome shift in the cancer and hyperplasia cases, distinguishable from the benign cases (p=0.01). Several taxa were found to be significantly enriched in samples belonging to the endometrial cancer cohort: Firmicutes (Anaerostipes, ph2, Dialister, Peptoniphilus, 1-68, Ruminococcus, and Anaerotruncus), Spirochaetes (Treponema), Actinobacteria (Atopobium), Bacteroidetes (Bacteroides and Porphyromonas), and Proteobacteria (Arthrospira). Of particular relevance, the simultaneous presence of Atopobium vaginae and an uncultured representative of the Porphyromonas sp. (99% match to P.somerae) were found to be predictive of the disease status (sensitivity 73-93%; specificity 67-90%, AUC 0.918). Sensitivity is increased to 100% if combined with a high vaginal pH (&gt;4.5), although specificity is decreased to 60%. Although our numbers are low, the microbiome signature of hyperplasia was even more distinct from benign disease than endometrial cancer itself. This is suggestive of an early microbiome role in the disease. Given the pathogenic profile of the identified microorganisms these findings raise the possibility of a microbiome role in the etiology or progression of endometrial cancer that should be further investigated. Citation Format: Marina Walther-Antonio, Jun Chen, Francesco Multinu, Alexis Hokenstad, Tammy Distad, Heidi Cheek, Gary Keeney, Douglas Creedon, Heidi Nelson, Andrea Mariani, Nicholas Chia. Endometrial cancer microbiome signature and its implications for carcinogenesis. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr B45.

Background The role of microbiota in sinonasal inflammation can be further understood by targeted sampling of healthy and diseased subjects. We compared the microbiota of the middle meatus (MM) and inferior meatus (IM) in healthy, allergic rhinitis (AR), and chronic rhinosinusitis (CRS) subjects to characterize intrasubject, intersubject, and intergroup differences. Methods Subjects were recruited in the office, and characterized into healthy, AR, and CRS groups. Endoscopically‐guided swab samples were obtained from the MM and IM bilaterally. Bacterial microbiota were characterized by sequencing the V3‐V4 region of the 16S ribosomal RNA (rRNA) gene. Results Intersubject microbiome analyses were conducted in 65 subjects: 8 healthy, 11 AR, and 46 CRS (25 CRS with nasal polyps [CRSwNP]; 21 CRS without nasal polyps [CRSsNP]). Intrasubject analyses were conducted for 48 individuals (4 controls, 11 AR, 8 CRSwNP, and 15 CRSwNP). There was considerable intersubject microbiota variability, but intrasubject profiles were similar ( p = 0.001, nonparametric t test). Intrasubject bacterial diversity was significantly reduced in MM of CRSsNP subjects compared to IM samples ( p = 0.022, nonparametric t test). CRSsNP MM samples were enriched in Streptococcus , Haemophilus , and Fusobacterium spp. but exhibited loss of diversity compared to healthy, CRSwNP, and AR subject‐samples ( p < 0.05; nonparametric t test). CRSwNP patients were enriched in Staphylococcus , Alloiococcus , and Corynebacterium spp. Conclusion This study presents the sinonasal microbiome profile in one of the larger populations of non‐CRS and CRS subjects, and is the first office‐based cohort in the literature. In contrast to healthy, AR, and CRSwNP subjects, CRSsNP MM samples exhibited decreased microbiome diversity and anaerobic enrichment. CRSsNP MM samples had reduced diversity compared to same‐subject IM samples, a novel finding.

ABSTRACT Whole-genome sequencing (WGS) can provide excellent resolution in global and local epidemiological investigations of Staphylococcus aureus outbreaks. A variety of sequencing approaches and analytical tools have been used; it is not clear which is ideal. We compared two WGS strategies and two analytical approaches to the standard method of SmaI restriction digestion pulsed-field gel electrophoresis (PFGE) for typing S. aureus . Forty-two S. aureus isolates from three outbreaks and 12 reference isolates were studied. Near-complete genomes, assembled de novo with paired-end and long-mate-pair (8 kb) libraries were first assembled and analyzed utilizing an in-house assembly and analytical informatics pipeline. In addition, paired-end data were assembled and analyzed using a commercial software package. Single nucleotide variant (SNP) analysis was performed using the in-house pipeline. Two assembly strategies were used to generate core genome multilocus sequence typing (cgMLST) data. First, the near-complete genome data generated with the in-house pipeline were imported into the commercial software and used to perform cgMLST analysis. Second, the commercial software was used to assemble paired-end data, and resolved assemblies were used to perform cgMLST. Similar isolate clustering was observed using SNP calling and cgMLST, regardless of data assembly strategy. All methods provided more discrimination between outbreaks than did PFGE. Overall, all of the evaluated WGS strategies yielded statistically similar results for S. aureus typing.

ABSTRACT Whole-genome amplification (WGA) is a useful tool for amplification of very small quantities of DNA for many uses, including metagenomic shotgun sequencing for infection diagnosis. Depending on the application, background DNA from WGA kits can be problematic. Three WGA kits were tested for their utility in a metagenomics approach to identify the pathogens in sonicate fluid comprised of biofilms and other materials dislodged from the surfaces of explanted prosthetic joints using sonication. The Illustra V2 Genomiphi, Illustra single cell Genomiphi, and Qiagen REPLI-g single cell kits were used to test identical sonicate fluid samples. Variations in the number of background reads, the genera identified in the background, and the number of reads from known pathogens known to be present in the samples were observed between kits. These results were then compared to those obtained with a library preparation without prior WGA using an NEBNext Ultra II paired-end kit, which requires a very small amount of input DNA. This approach also resulted in the presence of contaminant bacterial DNA and yielded fewer reads from the known pathogens. These findings highlight the impact that WGA kit selection can have on metagenomic analysis of low-biomass samples and the importance of the careful selection and consideration of the implications of using these tools.

Abstract Summary Reconstructing and analyzing a large number of genome-scale metabolic models is a fundamental part of the integrated study of microbial communities; however, two of the most widely used frameworks for building and analyzing models use different metabolic network representations. Here we describe Mackinac, a Python package that combines ModelSEED’s ability to automatically reconstruct metabolic models with COBRApy’s advanced analysis capabilities to bridge the differences between the two frameworks and facilitate the study of the metabolic potential of microorganisms. Availability and Implementation This package works with Python 2.7, 3.4, and 3.5 on MacOS, Linux and Windows. The source code is available from https://github.com/mmundy42/mackinac.