Chia, Nicholas

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.

AbstractBackgroundDifferences in the gut microbiota and breath methane production have been observed in chronic constipation, but the relationship between colonic microbiota, transit, and breath tests remains unclear.MethodsIn 25 healthy and 25 constipated females we evaluated the sigmoid colonic mucosal and fecal microbiota using 16S rRNA gene sequencing, abundance of hydrogenogenic FeFe (FeFe‐hydA) and hydrogenotrophic (methyl coenzyme M reductase A [mrcA] and dissimilatory sulfite reductase A [dsrA]) genes with real‐time qPCR assays, breath hydrogen and methane levels after oral lactulose, and colonic transit with scintigraphy.Key ResultsBreath hydrogen and methane were not correlated with constipation, slow colon transit, or with abundance of corresponding genes. After adjusting for colonic transit, the abundance of FeFehydA, dsrA, and mcrA were greater (P<.005) in colonic mucosa, but not stool, of constipated patients. The abundance of the selected functional gene targets also correlated with that of selected taxa. The colonic mucosal abundance of FeFe‐hydA, but not mcrA, correlated positively (P<.05) with breath methane production, slow colonic transit, and overall microbiome composition. In the colonic mucosa and feces, the abundance of hydrogenogenic and hydrogenotrophic genes were positively correlated (P<.05). Breath methane production was not associated with constipation or colonic transit.Conclusions & InferencesCorroborating our earlier findings with 16S rRNA genes, colonic mucosal but not fecal hydrogenogenic and hydrogenotrophic genes were more abundant in constipated vs. healthy subjects independent of colonic transit. Breath gases do not directly reflect the abundance of target genes contributing to their production.

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.

Abstract Background: The gut metabolome may be associated with the incidence and progression of numerous diseases. The composition of the gut metabolome can be captured by measuring metabolite levels in the feces. However, there are little data describing the effect of fecal sample collection methods on metabolomic measures. Methods: We collected fecal samples from 18 volunteers using four methods: no solution, 95% ethanol, fecal occult blood test (FOBT) cards, and fecal immunochemical test (FIT). One set of samples was frozen after collection (day 0), and for 95% ethanol, FOBT, and FIT, a second set was frozen after 96 hours at room temperature. We evaluated (i) technical reproducibility within sample replicates, (ii) stability after 96 hours at room temperature for 95% ethanol, FOBT, and FIT, and (iii) concordance of metabolite measures with the putative “gold standard,” day 0 samples without solution. Results: Intraclass correlation coefficients (ICC) estimating technical reproducibility were high for replicate samples for each collection method. ICCs estimating stability at room temperature were high for 95% ethanol and FOBT (median ICC &gt; 0.87) but not FIT (median ICC = 0.52). Similarly, Spearman correlation coefficients (rs) estimating metabolite concordance with the “gold standard” were higher for 95% ethanol (median rs = 0.82) and FOBT (median rs = 0.70) than for FIT (median rs = 0.40). Conclusions: Metabolomic measurements appear reproducible and stable in fecal samples collected with 95% ethanol or FOBT. Concordance with the “gold standard” is highest with 95% ethanol and acceptable with FOBT. Impact: Future epidemiologic studies should collect feces using 95% ethanol or FOBT if interested in studying fecal metabolomics. Cancer Epidemiol Biomarkers Prev; 25(11); 1483–90. ©2016 AACR.