Browsing UMB Open Access Articles by Subject "16S rRNA gene"
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A framework for assessing 16S rRNA marker-gene survey data analysis methods using mixtures.Background: There are a variety of bioinformatic pipelines and downstream analysis methods for analyzing 16S rRNA marker-gene surveys. However, appropriate assessment datasets and metrics are needed as there is limited guidance to decide between available analysis methods. Mixtures of environmental samples are useful for assessing analysis methods as one can evaluate methods based on calculated expected values using unmixed sample measurements and the mixture design. Previous studies have used mixtures of environmental samples to assess other sequencing methods such as RNAseq. But no studies have used mixtures of environmental to assess 16S rRNA sequencing. Results: We developed a framework for assessing 16S rRNA sequencing analysis methods which utilizes a novel two-sample titration mixture dataset and metrics to evaluate qualitative and quantitative characteristics of count tables. Our qualitative assessment evaluates feature presence/absence exploiting features only present in unmixed samples or titrations by testing if random sampling can account for their observed relative abundance. Our quantitative assessment evaluates feature relative and differential abundance by comparing observed and expected values. We demonstrated the framework by evaluating count tables generated with three commonly used bioinformatic pipelines: (i) DADA2 a sequence inference method, (ii) Mothur a de novo clustering method, and (iii) QIIME an open-reference clustering method. The qualitative assessment results indicated that the majority of Mothur and QIIME features only present in unmixed samples or titrations were accounted for by random sampling alone, but this was not the case for DADA2 features. Combined with count table sparsity (proportion of zero-valued cells in a count table), these results indicate DADA2 has a higher false-negative rate whereas Mothur and QIIME have higher false-positive rates. The quantitative assessment results indicated the observed relative abundance and differential abundance values were consistent with expected values for all three pipelines. Conclusions: We developed a novel framework for assessing 16S rRNA marker-gene survey methods and demonstrated the framework by evaluating count tables generated with three bioinformatic pipelines. This framework is a valuable community resource for assessing 16S rRNA marker-gene survey bioinformatic methods and will help scientists identify appropriate analysis methods for their marker-gene surveys. Copyright 2020 The Author(s).
Gastric microbiota features associated with cancer risk factors and clinical outcomes: A pilot study in gastric cardia cancer patients from Shanxi, ChinaLittle is known about the link between gastric microbiota and the epidemiology of gastric cancer. In order to determine the epidemiologic and clinical relevance of gastric microbiota, we used 16 S ribosomal RNA gene sequencing analysis to characterize the composition and structure of the gastric microbial community of 80 paired samples (non-malignant and matched tumor tissues) from gastric cardia adenocarcinoma (GCA) patients in Shanxi, China. We also used PICRUSt to predict microbial functional profiles. Compared to patients without family history of upper gastrointestinal (UGI) cancer in the non-malignant gastric tissue microbiota, patients with family history of UGI cancer had higher Helicobacter pylori (Hp) relative abundance (median: 0.83 vs. 0.38, p = 0.01) and lower alpha diversity (median observed species: 51 vs. 85, p = 0.01). Patients with higher (vs. lower) tumor grade had higher Hp relative abundance (0.73 vs. 0.18, p = 0.03), lower alpha diversity (observed species, 66 vs. 89, p = 0.01), altered beta diversity (weighted UniFrac, p = 0.002) and significant alterations in relative abundance of five KEGG functional modules in non-malignant gastric tissue microbiota. Patients without metastases had higher relative abundance of Lactobacillales than patients with metastases (0.05 vs. 0.01, p = 0.04) in non-malignant gastric tissue microbiota. These associations were observed in non-malignant tissues but not in tumor tissues. In conclusion, this study showed a link of gastric microbiota to a major gastric cancer risk factor and clinical features in GCA patients from Shanxi, China. Studies with both healthy controls and gastric cardia and noncardia cancer cases across different populations are needed to further examine the association between gastric cancer and the microbiota. Copyright 2017 UICC
Temporal variations in cigarette tobacco bacterial community composition and tobacco-specific nitrosamine content are influenced by brand and storage conditionsTobacco products, specifically cigarettes, are home to microbial ecosystems that may play an important role in the generation of carcinogenic tobacco-specific nitrosamines (TSNAs), as well as the onset of multiple adverse human health effects associated with the use of these products. Therefore, we conducted time-series experiments with five commercially available brands of cigarettes that were either commercially mentholated, custom-mentholated, user-mentholated, or non-mentholated. To mimic user storage conditions, the cigarettes were incubated for 14 days under three different temperatures and relative humidities (i.e., pocket, refrigerator, and room). Overall, 360 samples were collected over the course of 2 weeks and total DNA was extracted, PCR amplified for the V3V4 hypervariable region of the 16S rRNA gene and sequenced using Illumina MiSeq. A subset of samples (n = 32) was also analyzed via liquid chromatography with tandem mass spectrometry for two TSNAs: N'-nitrosonornicotine (NNN) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Comparative analyses of the five tobacco brands revealed bacterial communities dominated by Pseudomonas, Pantoea, and Bacillus, with Pseudomonas relatively stable in abundance regardless of storage condition. In addition, core bacterial operational taxonomic units (OTUs) were identified in all samples and included Bacillus pumilus, Rhizobium sp., Sphingomonas sp., unknown Enterobacteriaceae, Pantoea sp., Pseudomonas sp., Pseudomonas oryzihabitans, and P. putida. Additional OTUs were identified that significantly changed in relative abundance between day 0 and day 14, influenced by brand and storage condition. In addition, small but statistically significant increases in NNN levels were observed in user- and commercially mentholated brands between day 0 and day 14 at pocket conditions. These data suggest that manufacturing and user manipulations, such as mentholation and storage conditions, may directly impact the microbiome of cigarette tobacco as well as the levels of carcinogens. Copyright 2017 Chopyk, Chattopadhyay, Kulkarni, Smyth, Hittle, Paulson, Pop, Buehler, Clark, Mongodin and Sapkota.