Bacterial community structure in High-Arctic snow and freshwater as revealed by pyrosequencing of 16S rRNA genes and cultivation

The bacterial community structures in High-Arctic snow over sea ice and an ice-covered freshwater lake were examined by pyrosequencing of 16S rRNA genes and 16S rRNA gene sequencing of cultivated isolates. Both the pyrosequence and cultivation data indicated that the phylogenetic composition of the microbial assemblages was different within the snow layers and between snow and freshwater. The highest diversity was seen in snow. In the middle and top snow layers, Proteobacteria, Bacteroidetes and Cyanobacteria dominated, although Actinobacteria and Firmicutes were relatively abundant also. High numbers of chloroplasts were also observed. In the deepest snow layer, large percentages of Firmicutes and Fusobacteria were seen. In freshwater, Bacteroidetes, Actinobacteria and Verrucomicrobia were the most abundant phyla while relatively few Proteobacteria and Cyanobacteria were present. Possibly, light intensity controlled the distribution of the Cyanobacteria and algae in the snow while carbon and nitrogen fixed by these autotrophs in turn fed the heterotrophic bacteria. In the lake, a probable lower light input relative to snow resulted in low numbers of Cyanobacteria and chloroplasts and, hence, limited input of organic carbon and nitrogen to the heterotrophic bacteria. Thus, differences in the physicochemical conditions may play an important role in the processes leading to distinctive bacterial community structures in High-Arctic snow and freshwater

Local diversity of heathland Cercozoa explored by in-depth sequencing

Cercozoa are abundant free-living soil protozoa and quantitatively important in soil food webs; yet, targeted high-throughput sequencing (HTS) has not yet been applied to this group. Here we describe the development of a targeted assay to explore Cercozoa using HTS, and we apply this assay to measure Cercozoan community response to drought in a Danish climate manipulation experiment (two sites exposed to artificial drought, two unexposed). Based on a comparison of the hypervariable regions of the 18S ribosomal DNA of 193 named Cercozoa, we concluded that the V4 region is the most suitable for group-specific diversity analysis. We then designed a set of highly specific primers (encompassing ~270 bp) for 454 sequencing. The primers captured all major cercozoan groups; and >95% of the obtained sequences were from Cercozoa. From 443 350 high-quality short reads (>300 bp), we recovered 1585 operational taxonomic units defined by >95% V4 sequence similarity. Taxonomic annotation by phylogeny enabled us to assign >95% of our reads to order level and ~85% to genus level despite the presence of a large, hitherto unknown diversity. Over 40% of the annotated sequences were assigned to Glissomonad genera, whereas the most common individually named genus was the euglyphid Trinema. Cercozoan diversity was largely resilient to drought, although we observed a community composition shift towards fewer testate amoebae

Detection of Helicobacter ganmani-Like 16S rDNA in Pediatric Liver Tissue

Background. To determine the presence of Helicobacter species in the liver biopsy specimens from children with various chronic liver diseases as data in adult literature suggests a possible role of these bacteria in their pathogenesis.Materials and methods. Paraffin sections of 61 liver biopsies of pediatric patients with miscellaneous diseases and autopsy liver tissue from 10 control subjects with no evidence of preexisting liver disease were examined for the presence of Helicobacter species by a genus-specific seminested polymerase chain reaction (PCR) assay. PCRproducts of positive samples were further characterized by denaturing gradient gel electrophoresis (DGGE) and DNA-sequence analysis. Based on those results, a seminested PCR assay for H. ganmani was developed and applied to the samples.Results. On analysis, 40/61 patient samples were positive in the genus-specific Helicobacter PCR and 4/10 from the control group. The nucleotide sequences of 16S rDNA fragments were 99100 similar to mainly Helicobacter sp. liver and H. ganmani. PCR-products similar to H. canis and H. bilis were also found. The 16S rDNAs of control specimens showed similarity to Helicobacter sp. liver. In the H. ganmani-specific PCR analysis 19 patients, but none of the controls, were positive.Conclusions. Amplified Helicobacter 16S rDNAs were related to Helicobacter sp. liver or H. ganmani in liver biopsy specimens of pediatric patients. The possible significance of Helicobacter species in pediatric liver diseases needs to be evaluated further in prospective studies

High prevalence of Helicobacter Species detected in laboratory mouse strains by multiplex PCR-denaturing gradient gel electrophoresis and pyrosequencing.

Rodent models have been developed to study the pathogenesis of diseases caused by Helicobacter pylori, as well as by other gastric and intestinal Helicobacter spp., but some murine enteric Helicobacter spp. cause hepatobiliary and intestinal tract diseases in specific inbred strains of laboratory mice. To identify these murine Helicobacter spp., we developed an assay based on PCR-denaturing gradient gel electrophoresis and pyrosequencing. Nine strains of mice, maintained in four conventional laboratory animal houses, were assessed for Helicobacter sp. carriage. Tissue samples from the liver, stomach, and small intestine, as well as feces and blood, were collected; and all specimens (n = 210) were screened by a Helicobacter genus-specific PCR. Positive samples were identified to the species level by multiplex denaturing gradient gel electrophoresis, pyrosequencing, and a H. ganmani-specific PCR assay. Histologic examination of 30 tissue samples from 18 animals was performed. All mice of eight of the nine strains tested were Helicobacter genus positive; H. bilis, H. hepaticus, H. typhlonius, H. ganmani, H. rodentium, and a Helicobacter sp. flexispira-like organism were identified. Helicobacter DNA was common in fecal (86%) and gastric tissue (55%) specimens, whereas samples of liver tissue (21%), small intestine tissue (17%), and blood (14%) were less commonly positive. Several mouse strains were colonized with more than one Helicobacter spp. Most tissue specimens analyzed showed no signs of inflammation; however, in one strain of mice, hepatitis was diagnosed in livers positive for H. hepaticus, and in another strain, gastric colonization by H. typhlonius was associated with gastritis. The diagnostic setup developed was efficient at identifying most murine Helicobacter spp

Community structure of the metabolically active rumen bacterial and archaeal communities of dairy cows over the transition period

Dairy cows experience dramatic changes in host physiology from gestation to lactation period and dietary switch from high-forage prepartum diet to high-concentrate postpartum diet over the transition period (parturition +/- three weeks). Understanding the community structure and activity of the rumen microbiota and its associative patterns over the transition period may provide insight for e.g. improving animal health and production. In the present study, rumen samples from ten primiparous Holstein dairy cows were collected over seven weeks spanning the transition period. Total RNA was extracted from the rumen samples and cDNA thereof was subsequently used for characterizing the metabolically active bacterial (16S rRNA transcript amplicon sequencing) and archaeal (qPCR, T-RFLP and mcrA and 16S rRNA transcript amplicon sequencing) communities. The metabolically active bacterial community was dominated by three phyla, showing significant changes in relative abundance range over the transition period: Firmicutes (from prepartum 57% to postpartum 35%), Bacteroidetes (from prepartum 22% to postpartum 18%) and Proteobacteria (from prepartum 7% to postpartum 32%). For the archaea, qPCR analysis of 16S rRNA transcript number, revealed a significant prepartum to postpartum increase in Methanobacteriales, in accordance with an observed increase (from prepartum 80% to postpartum 89%) in relative abundance of 16S rRNA transcript amplicons allocated to this order. On the other hand, a significant prepartum to postpartum decrease (from 15% to 2%) was observed in relative abundance of Methanomassiliicoccales 16S rRNA transcripts. In contrast to qPCR analysis of the 16S rRNA transcripts, quantification of mcrA transcripts revealed no change in total abundance of metabolically active methanogens over the transition period. According to T-RFLP analysis of the mcrA transcripts, two Methanobacteriales genera, Methanobrevibacter and Methanosphaera (represented by the T-RFs 39 and 267 bp), represented more than 70% of the metabolically active methanogens, showing no significant changes over the transition period; minor T-RFs, likely to represent members of the order Methanomassiliicoccales and with a relative abundance below 5% in total, decreased significantly over the transition period. In accordance with the T-RFLP analysis, the mcrA transcript amplicon sequencing revealed Methanobacteriales to cover 99% of the total reads, dominated by the genera Methanobrevibacter (75%) and Methanosphaera (24%), whereas the Methanomassiliicoccales order covered only 0.2% of the total reads. In conclusion, the present study showed that the structure of the metabolically active bacterial and archaeal rumen communities changed over the transition period, likely in response to the dramatic changes in physiology and nutritional factors like dry matter intake and feed composition. It should be noted however that for the methanogens, the observed community changes were influenced by the analyzed gene (mcrA or 16S rRNA)

Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

BACKGROUND: There is an immense scientific interest in the human microbiome and its effects on human physiology, health, and disease. A common approach for examining bacterial communities is high-throughput sequencing of 16S rRNA gene hypervariable regions, aggregating sequence-similar amplicons into operational taxonomic units (OTUs). Strategies for detecting differential relative abundance of OTUs between sample conditions include classical statistical approaches as well as a plethora of newer methods, many borrowing from the related field of RNA-seq analysis. This effort is complicated by unique data characteristics, including sparsity, sequencing depth variation, and nonconformity of read counts to theoretical distributions, which is often exacerbated by exploratory and/or unbalanced study designs. Here, we assess the robustness of available methods for (1) inference in differential relative abundance analysis and (2) beta-diversity-based sample separation, using a rigorous benchmarking framework based on large clinical 16S microbiome datasets from different sources. RESULTS: Running more than 380,000 full differential relative abundance tests on real datasets with permuted case/control assignments and in silico-spiked OTUs, we identify large differences in method performance on a range of parameters, including false positive rates, sensitivity to sparsity and case/control balances, and spike-in retrieval rate. In large datasets, methods with the highest false positive rates also tend to have the best detection power. For beta-diversity-based sample separation, we show that library size normalization has very little effect and that the distance metric is the most important factor in terms of separation power. CONCLUSIONS: Our results, generalizable to datasets from different sequencing platforms, demonstrate how the choice of method considerably affects analysis outcome. Here, we give recommendations for tools that exhibit low false positive rates, have good retrieval power across effect sizes and case/control proportions, and have low sparsity bias. Result output from some commonly used methods should be interpreted with caution. We provide an easily extensible framework for benchmarking of new methods and future microbiome datasets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40168-016-0208-8) contains supplementary material, which is available to authorized users