Chia, Nicholas

The genomic DNA input for stool samples utilized for microbiome composition has not been determined. In this study, we determined the reliable threshold level under which conclusions drawn from the data may be compromised. We also determined the type of microbial bias introduced by less-than-ideal genomic input.

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 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.

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.

ABSTRACT To our knowledge, fecal microbiota collection methods have not been evaluated in low- and middle-income countries. Therefore, we evaluated five different fecal sample collection methods for technical reproducibility, stability, and accuracy within the Health Effects of Arsenic Longitudinal Study (HEALS) in Bangladesh. Fifty participants from the HEALS provided fecal samples in the clinic which were aliquoted into no solution, 95% ethanol, RNAlater, postdevelopment fecal occult blood test (FOBT) cards, and fecal immunochemical test (FIT) tubes. Half of the aliquots were frozen immediately at −80°C (day 0) and the remaining samples were left at ambient temperature for 96 h and then frozen (day 4). Intraclass correlation coefficients (ICC) were calculated for the relative abundances of the top three phyla, for two alpha diversity measures, and for four beta diversity measures. The duplicate samples had relatively high ICCs for technical reproducibility at day 0 and day 4 (range, 0.79 to 0.99). The FOBT card and samples preserved in RNAlater and 95% ethanol had the highest ICCs for stability over 4 days. The FIT tube had lower stability measures overall. In comparison to the “gold standard” method using immediately frozen fecal samples with no solution, the ICCs for many of the microbial metrics were low, but the rank order appeared to be preserved as seen by the Spearman correlation. The FOBT cards, 95% ethanol, and RNAlater were effective fecal preservatives. These fecal collection methods are optimal for future cohort studies, particularly in low- and middle-income countries. IMPORTANCE The collection of fecal samples in prospective cohort studies is essential to provide the opportunity to study the effect of the human microbiota on numerous health conditions. However, these collection methods have not been adequately tested in low- and middle-income countries. We present estimates of technical reproducibility, stability at ambient temperature for 4 days, and accuracy comparing a “gold standard” for fecal samples in no solution, 95% ethanol, RNAlater, postdevelopment fecal occult blood test cards, and fecal immunochemical test tubes in a study conducted in Bangladesh. Fecal occult blood test cards and fecal samples stored in 95% ethanol or RNAlater adequately preserve fecal samples in this setting. Therefore, new studies in low- and middle-income countries should include collection of fecal samples using fecal occult blood test cards, 95% ethanol, or RNAlater for prospective cohort studies.

Abstract Background: Adenomatous polyps are the most common precursor to colorectal cancer, the second leading cause of cancer-related death in the United States. We sought to learn more about early events of carcinogenesis by investigating shifts in the gut microbiota of patients with adenomas. Methods: We analyzed 16S rRNA gene sequences from the fecal microbiota of patients with adenomas (n = 233) and without (n = 547). Results: Multiple taxa were significantly more abundant in patients with adenomas, including Bilophila, Desulfovibrio, proinflammatory bacteria in the genus Mogibacterium, and multiple Bacteroidetes species. Patients without adenomas had greater abundances of Veillonella, Firmicutes (Order Clostridia), and Actinobacteria (family Bifidobacteriales). Our findings were consistent with previously reported shifts in the gut microbiota of colorectal cancer patients. Importantly, the altered adenoma profile is predicted to increase primary and secondary bile acid production, as well as starch, sucrose, lipid, and phenylpropanoid metabolism. Conclusions: These data hint that increased sugar, protein, and lipid metabolism along with increased bile acid production could promote a colonic environment that supports the growth of bile-tolerant microbes such as Bilophilia and Desulfovibrio. In turn, these microbes may produce genotoxic or inflammatory metabolites such as H2S and secondary bile acids, which could play a role in catalyzing adenoma development and eventually colorectal cancer. Impact: This study suggests a plausible biological mechanism to explain the links between shifts in the microbiota and colorectal cancer. This represents a first step toward resolving the complex interactions that shape the adenoma–carcinoma sequence of colorectal cancer and may facilitate personalized therapeutics focused on the microbiota. Cancer Epidemiol Biomarkers Prev; 26(1); 85–94. ©2016 AACR.