Metabarcoding of colonic cleansing fluid reveals unique bacterial members of mucosal microbiota associated with Inflammatory Bowel Disease.

BACKGROUND: Inflammatory Bowel Disease (IBD) is a group of chronic idiopathic inflammatory diseases of the gastrointestinal (GI) tract associated with the dysbiosis of gut microbiota. Metabarcoding-based profiling of the gut microbiota of IBD patients is generally based on the stool samples collected from individual patients which rarely represent the mucosa-associated microbiota. The ideal sampling strategy for routine monitoring of the mucosal component of IBD has yet to be determined. METHODS: We hereby compare the microbiota composition of the colonic cleansing fluid (CCF) collected during colonoscopy with stool samples from IBD patients. The relationship between IBD and gut microbiota was revealed through the application of the 16S rRNA amplicon sequencing-based metabarcoding approach. CCF and stool samples were collected from IBD patients with Crohn's disease and ulcerative colitis. RESULTS: The present study shows significant differences in the microbial composition of CCF samples, presumably indicating changes in the mucosal microbiota of IBD patients as compared to the control group. Short-chain fatty acid-producing bacteria under the family Lachnospiraceae, the actinobacterial genus Bifidobacterium, the proteobacterial Sutterella and Raoultella are found to contribute to the microbial dysbiosis of the mucosal flora in IBD patients. CONCLUSIONS: CCF microbiota has the capacity to distinguish IBD patients from healthy controls and, thus, may constitute an alternative analysis strategy for the early diagnosis and disease progression in IBD biomarker research.

Strategies to improve usability and preserve accuracy in biological sequence databases.

Biology is increasingly dependent on large-scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identification of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high-quality, informative annotation from less useful ones; (iv) to integrate automated quality-control software whenever such tools exist; and (v) to facilitate postsubmission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large-scale data.

FANTOM: Functional and taxonomic analysis of metagenomes.

BACKGROUND: Interpretation of quantitative metagenomics data is important for our understanding of ecosystem functioning and assessing differences between various environmental samples. There is a need for an easy to use tool to explore the often complex metagenomics data in taxonomic and functional context. RESULTS: Here we introduce FANTOM, a tool that allows for exploratory and comparative analysis of metagenomics abundance data integrated with metadata information and biological databases. Importantly, FANTOM can make use of any hierarchical database and it comes supplied with NCBI taxonomic hierarchies as well as KEGG Orthology, COG, PFAM and TIGRFAM databases. CONCLUSIONS: The software is implemented in Python, is platform independent, and is available at http://www.sysbio.se/Fantom.

Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia.

In tumor tissues, hypoxia is a commonly observed feature resulting from rapidly proliferating cancer cells outgrowing their surrounding vasculature network. Transformed cancer cells are known to exhibit phenotypic alterations, enabling continuous proliferation despite a limited oxygen supply. The four-step isogenic BJ cell model enables studies of defined steps of tumorigenesis: the normal, immortalized, transformed, and metastasizing stages. By transcriptome profiling under atmospheric and moderate hypoxic (3% O2) conditions, we observed that despite being highly similar, the four cell lines of the BJ model responded strikingly different to hypoxia. Besides corroborating many of the known responses to hypoxia, we demonstrate that the transcriptome adaptation to moderate hypoxia resembles the process of malignant transformation. The transformed cells displayed a distinct capability of metabolic switching, reflected in reversed gene expression patterns for several genes involved in oxidative phosphorylation and glycolytic pathways. By profiling the stage-specific responses to hypoxia, we identified ASS1 as a potential prognostic marker in hypoxic tumors. This study demonstrates the usefulness of the BJ cell model for highlighting the interconnection of pathways involved in malignant transformation and hypoxic response.

A pathology atlas of the human cancer transcriptome.

Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

Metagenomic sequencing of marine periphyton: taxonomic and functional insights into biofilm communities.

Periphyton communities are complex phototrophic, multispecies biofilms that develop on surfaces in aquatic environments. These communities harbor a large diversity of organisms comprising viruses, bacteria, algae, fungi, protozoans, and metazoans. However, thus far the total biodiversity of periphyton has not been described. In this study, we use metagenomics to characterize periphyton communities from the marine environment of the Swedish west coast. Although we found approximately ten times more eukaryotic rRNA marker gene sequences compared to prokaryotic, the whole metagenome-based similarity searches showed that bacteria constitute the most abundant phyla in these biofilms. We show that marine periphyton encompass a range of heterotrophic and phototrophic organisms. Heterotrophic bacteria, including the majority of proteobacterial clades and Bacteroidetes, and eukaryotic macro-invertebrates were found to dominate periphyton. The phototrophic groups comprise Cyanobacteria and the alpha-proteobacterial genus Roseobacter, followed by different micro- and macro-algae. We also assess the metabolic pathways that predispose these communities to an attached lifestyle. Functional indicators of the biofilm form of life in periphyton involve genes coding for enzymes that catalyze the production and degradation of extracellular polymeric substances, mainly in the form of complex sugars such as starch and glycogen-like meshes together with chitin. Genes for 278 different transporter proteins were detected in the metagenome, constituting the most abundant protein complexes. Finally, genes encoding enzymes that participate in anaerobic pathways, such as denitrification and methanogenesis, were detected suggesting the presence of anaerobic or low-oxygen micro-zones within the biofilms.

Long-term effects of the antibacterial agent triclosan on marine periphyton communities.

Triclosan is a widely used antibacterial agent that has become a ubiquitous contaminant in freshwater, estuary, and marine environments. Concerns about potential adverse effects of triclosan have been described in several recent risk assessments. Its effects on freshwater microbial communities have been well studied, but studies addressing effects on marine microbial communities are scarce. In the present study, the authors describe short- and long-term effects of triclosan on marine periphyton (microbial biofilm) communities. Short-term effects on photosynthesis were estimated after 60 min to 210 min of exposure. Long-term effects on photosynthesis, chlorophyll a fluorescence, pigment content, community tolerance, and bacterial carbon utilization were studied after exposing periphyton for 17 d in flow-through microcosms to 0.316 nM to 10,000 nM triclosan. Results from the short-term studies show that triclosan is toxic to periphyton photosynthesis. Half maximal effective concentration (EC50) values of 1080 nM and 3000 nM were estimated using (14)CO2-incorporation and pulse amplitude modulation (PAM) fluorescence measurements, respectively. After long-term triclosan exposure in flow-through microcosms, photosynthesis estimated using PAM fluorometry was not inhibited by triclosan concentrations up to 1000 nM but instead increased with increasing triclosan concentration. Similarly, at exposure concentrations of 31.6 nM and higher, triclosan caused an increase in photosynthetic pigments. At 316 nM triclosan, the pigment amounts were increased by a factor of 1.4 to 1.9 compared with the control level. Pollution-induced community tolerance was observed for algae and cyanobacteria at 100 nM triclosan and higher. Despite the widespread use of triclosan as an antibacterial agent, the compound did not have any effects on bacterial carbon utilization after long-term exposure.