Objective Omega-3 polyunsaturated fatty acids (PUFAs) have anticolorectal cancer (CRC) activity. The intestinal microbiota has been implicated in colorectal carcinogenesis. Dietary omega-3 PUFAs alter the mouse intestinal microbiome compatible with antineoplastic activity. Therefore, we investigated the effect of omega-3 PUFA supplements on the faecal microbiome in middle-aged, healthy volunteers (n=22). Design A randomised, open-label, cross-over trial of 8 weeks’ treatment with 4 g mixed eicosapentaenoic acid/docosahexaenoic acid in two formulations (soft-gel capsules and Smartfish drinks), separated by a 12-week ‘washout’ period. Faecal samples were collected at five time-points for microbiome analysis by 16S ribosomal RNA PCR and Illumina MiSeq sequencing. Red blood cell (RBC) fatty acid analysis was performed by liquid chromatography tandem mass spectrometry. Results Both omega-3 PUFA formulations induced similar changes in RBC fatty acid content, except that drinks were associated with a larger, and more prolonged, decrease in omega-6 PUFA arachidonic acid than the capsule intervention (p=0.02). There were no significant changes in α or β diversity, or phyla composition, associated with omega-3 PUFA supplementation. However, a reversible increased abundance of several genera, including Bifidobacterium , Roseburia and Lactobacillus was observed with one or both omega-3 PUFA interventions. Microbiome changes did not correlate with RBC omega-3 PUFA incorporation or development of omega-3 PUFA-induced diarrhoea. There were no treatment order effects. Conclusion Omega-3 PUFA supplementation induces a reversible increase in several short-chain fatty acid-producing bacteria, independently of the method of administration. There is no simple relationship between the intestinal microbiome and systemic omega-3 PUFA exposure. Trial registration number ISRCTN18662143.
Metagenomics is the study of microbial organisms using sequencing applied directly to environmental samples. Technological advances in next-generation sequencing methods are fueling a rapid increase in the number and scope of metagenome projects. While metagenomics provides information on the gene content, metatranscriptomics aims at understanding gene expression patterns in microbial communities. The initial computational analysis of a metagenome or metatranscriptome addresses three questions: (1) Who is out there? (2) What are they doing? and (3) How do different datasets compare? There is a need for new computational tools to answer these questions. In 2007, the program MEGAN (MEtaGenome ANalyzer) was released, as a standalone interactive tool for analyzing the taxonomic content of a single metagenome dataset. The program has subsequently been extended to support comparative analyses of multiple datasets. The focus of this paper is to report on new features of MEGAN that allow the functional analysis of multiple metagenomes (and metatranscriptomes) based on the SEED hierarchy and KEGG pathways. We have compared our results with the MG-RAST service for different datasets. The MEGAN program now allows the interactive analysis and comparison of the taxonomical and functional content of multiple datasets. As a stand-alone tool, MEGAN provides an alternative to web portals for scientists that have concerns about uploading their unpublished data to a website.
Abstract Objective Individuals with newly diagnosed RA have a distinct microbiome when compared with healthy controls. However, little is known as to when these microbiome perturbations begin. Using a prospective at-risk cohort of individuals positive for anti-citrullinated protein (anti-CCP) antibody with new onset musculoskeletal symptoms, but without clinical arthritis, we investigated for the presence of a gut dysbiosis before the onset of RA. Methods The gut microbiota of 25 anti-CCP positive individuals without clinical synovitis were sequenced targeting the V4 region of the 16S rRNA gene. Using a publicly available database, a control population of 44 individuals, approximately matched in age, gender, diet and ethnicity was selected for comparison, using the same sequencing methodology. Median interval between sample collection and progression to RA was 188 days. Taxonomic analysis was performed using QIIME and MEGAN, and statistical analysis using R software. Results There were significant differences (P =0.01) at family level in gut microbiomes of anti-CCP positive individuals vs controls. The anti-CCP positive population had an overabundance of Lachnospiraceae, Helicobacteraceae, Ruminococcaceae, Erysipelotrichaceae and Bifidobacteriaceae, among others. Five individuals progressed to RA between sample collection and analysis. Clustering of the progressor population was observed on a phylogenetic network created using a probabilistic similarity index (Goodall’s index). Conclusions Anti-CCP positive at-risk individuals without clinical synovitis appear to have a distinct gut microbiome compared with healthy controls. Phylogenetic clustering was observed in individuals who progressed to RA, suggesting that distinct taxa are associated with the development of RA many months before its onset.
This chapter contains sections titled: Introduction Getting Started Taxonomical Analysis Functional Analysis Comparing Datasets Summary and Outlook Internet Resources References
One important question in microbiome analysis is how to assess the homogeneity of the microbial composition in a given environment, with respect to a given analysis method. Do different microbial samples taken from the same environment follow the same taxonomic distribution of organisms, or the same distribution of functions? Here we provide a non-parametric statistical "triangulation test" to address this type of question. The test requires that multiple replicates are available for each of the biological samples, and it is based on three-way computational comparisons of samples. To illustrate the application of the test, we collected three biological samples taken from different locations in one piece of human stool, each represented by three replicates, and analyzed them using MEGAN. (Despite its name, the triangulation test does not require that the number of biological samples or replicates be three.) The triangulation test rejects the null hypothesis that the three biological samples exhibit the same distribution of taxa or function (error probability ≤0.05), indicating that the microbial composition of the investigated human stool is not homogenous on a macroscopic scale, suggesting that pooling material from multiple locations is a reasonable practice. We provide an implementation of the test in our open source program MEGAN Community Edition.
Clostridium difficile infection (CDI) is a major cause of nosocomial diarrhea associated with antimicrobial-mediated dysbiosis. Dysbiosis may be perpetuated by antibiotic (AB) CDI therapy, leading to recurrent CDI. Ridinilazole (RIDI) has very narrow activity against certain clostridia. We measured faecal microbiomes of Phase 2 subjects randomised to 10 days of RIDI or fidaxomicin (FDX) for CDI. Fecal samples (27 patients) were obtained at study entry (DM1-D1), Day 2 (D2-D3), Day 5 (D4–5), Day 7 (D5–7), Day 10 (D9–10), Day 12 (D11–14), Day 25 (D15–25), D30 (D25–30), Day 40 (D40+) and grouped according to blinded treatment (Drug A or B). DNA was extracted using the QIAamp DNA Stool Mini Kit (Qiagen), and 16S V4 PCR products sequenced on an Illumina MiSeq®. Samples were analyzed as a full dataset (n = 154) and as subgroups: no concomitant (con)AB or prior CDI AB (n = 67); no conAB but prior CDI AB (n = 44); total no conAB (n = 111). Data were quality controlled and annotated (QIIME, Usearch, Greengenes, PyNAST, RDP). OTU files were imported in MEGAN for further analyses. Comparable sustained clinical response rates to 30 days post end of therapy were seen with RIDI (50%) compared with FDX (46.2%); estimated treatment difference 2.9% (95% CI −30.8, 36.7). Following unblinding (drug A = RIDI, drug B=FDX), Simpsons diversity indices showed marked microbiome differences at family level for RIDI vs. FDX subjects in multiple analyses. This was most marked for no conAB or prior CD AB and total no conAB subjects, in whom RIDI microbiome diversity was markedly greater (approaching significance) during CDI treatment and significantly greater at D11-D14 and D4–5, D6–8, respectively (P < 0.05) (figure). In particular, Bifidobacteriaceae and Ruminococcaceae populations, previously linked to colonisation resistance, were more stable during RIDI treatment. RIDI preserved gut microbiome diversity to a greater extent than FDX during CDI treatment. Differences were most marked for, but not restricted to, patients receiving no conAB. Microbiome sparing by RIDI is consistent with low CDI recurrence rates. C. Chilton, Astellas: Investigator and Speaker’s Bureau, Research grant and Speaker honorarium. Da Volterra: Investigator, Research grant. Paratek pharmaceuticals: Investigator, Research grant. Actavis: Investigator, Research grant; J. Freeman, Astellas: Investigator, Research grant. Morphochem: Investigator and Research Contractor, Research support. Melinta: Research Contractor, Research support. R. Vickers, Summit plc: Employee, share options; M. Wilcox, Merck & Co., Inc.: Consultant, Consulting fee. Cubist: Consultant, Grant Investigator and Speaker’s Bureau, Consulting fee, Grant recipient and Speaker honorarium. Alere, Actelion Pharma, Astellas, Optimer, sanofi pasteur, Summit Pharma, bioMerieux, Da Volterra, Qiagen, Cerexa, Abbott, AstraZeneca, Pfizer, Durata Therapeutics, Seres Therapeutics, Valneva, Nabriva Therapeutics, Roche, The Medicines Company, Basilea P: Consultant, Consulting fee. Alere, Actelion Pharmaceuticals, Pharmaceuticals, Astellas, Optimer Pharmaceuticals, sanofi pasteur, Summit Pharmaceuticals, bioMerieux, Da Volterra, Qiagen, Cerexa, and Abbott: Grant Investigator, Grant recipient.
Mangroves are among the most diverse and productive coastal ecosystems in the tropical and subtropical regions. Environmental conditions particular to this biome make mangroves hotspots for microbial diversity, and the resident microbial communities play essential roles in maintenance of the ecosystem. Recently, there has been increasing interest to understand the composition and contribution of microorganisms in mangroves. In the present study, we have analyzed the diversity and distribution of archaea in the tropical mangrove sediments of Sundarbans using 16S rRNA gene amplicon sequencing. The extraction of DNA from sediment samples and the direct application of 16S rRNA gene amplicon sequencing resulted in approximately 142 Mb of data from three distinct mangrove areas (Godkhali, Bonnie camp, and Dhulibhashani). The taxonomic analysis revealed the dominance of phyla Euryarchaeota and Thaumarchaeota (Marine Group I) within our dataset. The distribution of different archaeal taxa and respective statistical analysis (SIMPER, NMDS) revealed a clear community shift along the sampling stations. The sampling stations (Godkhali and Bonnie camp) with history of higher hydrocarbon/oil pollution showed different archaeal community pattern (dominated by haloarchaea) compared to station (Dhulibhashani) with nearly pristine environment (dominated by methanogens). It is indicated that sediment archaeal community patterns were influenced by environmental conditions.
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