Microbiome variation in corals with distinct depth distribution ranges across a shallow-mesophotic gradient (15-85 m)

Mesophotic coral ecosystems (MCEs) are generally poorly studied, and our knowledge of lower MCEs (below 60 m depth) is largely limited to visual surveys. Here, we provide a first detailed assessment of the prokaryotic community associated with scleractinian corals over a depth gradient to the lower mesophotic realm (15-85 m). Specimens of three Caribbean coral species exhibiting differences in their depth distribution ranges (Agaricia grahamae, Madracis pharensis and Stephanocoenia intersepta) were collected with a manned submersible on the island of Cura double dagger ao, and their prokaryotic communities assessed using 16S rRNA gene sequencing analysis. Corals with narrower depth distribution ranges (depth-specialists) were associated with a stable prokaryotic community, whereas corals with a broader niche range (depth-generalists) revealed a higher variability in their prokaryotic community. The observed depth effects match previously described patterns in Symbiodinium depth zonation. This highlights the contribution of structured microbial communities over depth to the coral's ability to colonize a broader depth range.Austrian Science Fund (FWF); Catlin Group Limited; Global Change Institute; Eddie Bauer Grant for Expeditions by The Explorers Club; Marie Curie Fellowship [FP7-299320]; Lise Meitner Program of the Austrian Science Fund (FWF) [M1363-B20]info:eu-repo/semantics/publishedVersio

Soil bacterial and fungal communities across a pH gradient in an arable soil

Soils collected across a long-term liming experiment (pH 4.0-8.3), in which variation in factors other than pH have been minimized, were used to investigate the direct influence of pH on the abundance and composition of the two major soil microbial taxa, fungi and bacteria. We hypothesized that bacterial communities would be more strongly influenced by pH than fungal communities. To determine the relative abundance of bacteria and fungi, we used quantitative PCR (qPCR), and to analyze the composition and diversity of the bacterial and fungal communities, we used a bar-coded pyrosequencing technique. Both the relative abundance and diversity of bacteria were positively related to pH, the latter nearly doubling between pH 4 and 8. In contrast, the relative abundance of fungi was unaffected by pH and fungal diversity was only weakly related with pH. The composition of the bacterial communities was closely defined by soil pH; there was as much variability in bacterial community composition across the 180-m distance of this liming experiment as across soils collected from a wide range of biomes in North and South America, emphasizing the dominance of pH in structuring bacterial communities. The apparent direct influence of pH on bacterial community composition is probably due to the narrow pH ranges for optimal growth of bacteria. Fungal community composition was less strongly affected by pH, which is consistent with pure culture studies, demonstrating that fungi generally exhibit wider pH ranges for optimal growth. The ISME Journal (2010) 4, 1340-1351; doi: 10.1038/ismej.2010.58; published online 6 May 2010&nbsp

Challenges for automatically extracting molecular interactions from full-text articles

<p>Abstract</p> <p>Background</p> <p>The increasing availability of full-text biomedical articles will allow more biomedical knowledge to be extracted automatically with greater reliability. However, most Information Retrieval (IR) and Extraction (IE) tools currently process only abstracts. The lack of corpora has limited the development of tools that are capable of exploiting the knowledge in full-text articles. As a result, there has been little investigation into the advantages of full-text document structure, and the challenges developers will face in processing full-text articles.</p> <p>Results</p> <p>We manually annotated passages from full-text articles that describe interactions summarised in a Molecular Interaction Map (MIM). Our corpus tracks the process of identifying facts to form the MIM summaries and captures any factual dependencies that must be resolved to extract the fact completely. For example, a fact in the results section may require a synonym defined in the introduction. The passages are also annotated with negated and coreference expressions that must be resolved.</p> <p>We describe the guidelines for identifying relevant passages and possible dependencies. The corpus includes 2162 sentences from 78 full-text articles. Our corpus analysis demonstrates the necessity of full-text processing; identifies the article sections where interactions are most commonly stated; and quantifies the proportion of interaction statements requiring coherent dependencies. Further, it allows us to report on the relative importance of identifying synonyms and resolving negated expressions. We also experiment with an oracle sentence retrieval system using the corpus as a gold-standard evaluation set.</p> <p>Conclusion</p> <p>We introduce the MIM corpus, a unique resource that maps interaction facts in a MIM to annotated passages within full-text articles. It is an invaluable case study providing guidance to developers of biomedical IR and IE systems, and can be used as a gold-standard evaluation set for full-text IR tasks.</p

A Platform-Independent Method for Detecting Errors in Metagenomic Sequencing Data: DRISEE

We provide a novel method, DRISEE (duplicate read inferred sequencing error estimation), to assess sequencing quality (alternatively referred to as “noise” or “error”) within and/or between sequencing samples. DRISEE provides positional error estimates that can be used to inform read trimming within a sample. It also provides global (whole sample) error estimates that can be used to identify samples with high or varying levels of sequencing error that may confound downstream analyses, particularly in the case of studies that utilize data from multiple sequencing samples. For shotgun metagenomic data, we believe that DRISEE provides estimates of sequencing error that are more accurate and less constrained by technical limitations than existing methods that rely on reference genomes or the use of scores (e.g. Phred). Here, DRISEE is applied to (non amplicon) data sets from both the 454 and Illumina platforms. The DRISEE error estimate is obtained by analyzing sets of artifactual duplicate reads (ADRs), a known by-product of both sequencing platforms. We present DRISEE as an open-source, platform-independent method to assess sequencing error in shotgun metagenomic data, and utilize it to discover previously uncharacterized error in de novo sequence data from the 454 and Illumina sequencing platforms

Nicotinic Acetylcholine Receptor Variants Are Related to Smoking Habits, but Not Directly to COPD

Genome-wide association studies identified single nucleotide polymorphisms (SNPs) in the nicotinic acetylcholine receptors (nAChRs) cluster as a risk factor for nicotine dependency and COPD. We investigated whether SNPs in the nAChR cluster are associated with smoking habits and lung function decline, and if these potential associations are independent of each other. The SNPs rs569207, rs1051730 and rs8034191 in the nAChR cluster were analyzed in the Vlagtwedde-Vlaardingen cohort (n = 1,390) that was followed for 25 years. We used GEE and LME models to analyze the associations of the SNPs with quitting or restarting smoking and with the annual FEV1 decline respectively. Individuals homozygote (CC) for rs569207 were more likely to quit smoking (OR (95%CI) = 1.58 (1.05–2.38)) compared to wild-type (TT) individuals. Individuals homozygote (TT) for rs1051730 were less likely to quit smoking (0.64 (0.42; 0.97)) compared to wild-type (CC) individuals. None of the SNPs was significantly associated with the annual FEV1 decline in smokers and ex-smokers. We show that SNPs in the nAChR region are associated with smoking habits such as quitting smoking, but have no significant effect on the annual FEV1 decline in smokers and ex-smokers, suggesting a potential role of these SNPs in COPD development via smoking habits rather than via direct effects on lung function

16S rRNA Gene Pyrosequencing Reveals Bacterial Dysbiosis in the Duodenum of Dogs with Idiopathic Inflammatory Bowel Disease

BACKGROUND: Canine idiopathic inflammatory bowel disease (IBD) is believed to be caused by a complex interaction of genetic, immunologic, and microbial factors. While mucosa-associated bacteria have been implicated in the pathogenesis of canine IBD, detailed studies investigating the enteric microbiota using deep sequencing techniques are lacking. The objective of this study was to evaluate mucosa-adherent microbiota in the duodenum of dogs with spontaneous idiopathic IBD using 16 S rRNA gene pyrosequencing. METHODOLOGY/PRINCIPAL FINDINGS: Biopsy samples of small intestinal mucosa were collected endoscopically from healthy dogs (n = 6) and dogs with moderate IBD (n = 7) or severe IBD (n = 7) as assessed by a clinical disease activity index. Total RNA was extracted from biopsy specimens and 454-pyrosequencing of the 16 S rRNA gene was performed on aliquots of cDNA from each dog. Intestinal inflammation was associated with significant differences in the composition of the intestinal microbiota when compared to healthy dogs. PCoA plots based on the unweighted UniFrac distance metric indicated clustering of samples between healthy dogs and dogs with IBD (ANOSIM, p<0.001). Proportions of Fusobacteria (p = 0.010), Bacteroidaceae (p = 0.015), Prevotellaceae (p = 0.022), and Clostridiales (p = 0.019) were significantly more abundant in healthy dogs. In contrast, specific bacterial genera within Proteobacteria, including Diaphorobacter (p = 0.044) and Acinetobacter (p = 0.040), were either more abundant or more frequently identified in IBD dogs. CONCLUSIONS/SIGNIFICANCE: In conclusion, dogs with spontaneous IBD exhibit alterations in microbial groups, which bear resemblance to dysbiosis reported in humans with chronic intestinal inflammation. These bacterial groups may serve as useful targets for monitoring intestinal inflammation

Coral-Bacterial Communities before and after a Coral Mass Spawning Event on Ningaloo Reef

Bacteria associated with three coral species, Acropora tenuis, Pocillopora damicornis and Tubastrea faulkneri, were assessed before and after coral mass spawning on Ningaloo Reef in Western Australia. Two colonies of each species were sampled before and after the mass spawning event and two additional samples were collected for P. damicornis after planulation. A variable 470 bp region of the 16 S rRNA gene was selected for pyrosequencing to provide an understanding of potential variations in coral-associated bacterial diversity and community structure. Bacterial diversity increased for all coral species after spawning as assessed by Chao1 diversity indicators. Minimal changes in community structure were observed at the class level and data at the taxonomical level of genus incorporated into a PCA analysis indicated that despite bacterial diversity increasing after spawning, coral-associated community structure did not shift greatly with samples grouped according to species. However, interesting changes could be detected from the dataset; for example, α-Proteobacteria increased in relative abundance after coral spawning and particularly the Roseobacter clade was found to be prominent in all coral species, indicating that this group may be important in coral reproduction