There is increasing interest in employing shotgun sequencing, rather than amplicon sequencing, to analyze microbiome samples. Typical projects may involve hundreds of samples and billions of sequencing reads. The comparison of such samples against a protein reference database generates billions of alignments and the analysis of such data is computationally challenging. To address this, we have substantially rewritten and extended our widely-used microbiome analysis tool MEGAN so as to facilitate the interactive analysis of the taxonomic and functional content of very large microbiome datasets. Other new features include a functional classifier called InterPro2GO, gene-centric read assembly, principal coordinate analysis of taxonomy and function, and support for metadata. The new program is called MEGAN Community Edition (CE) and is open source. By integrating MEGAN CE with our high-throughput DNA-to-protein alignment tool DIAMOND and by providing a new program MeganServer that allows access to metagenome analysis files hosted on a server, we provide a straightforward, yet powerful and complete pipeline for the analysis of metagenome shotgun sequences. We illustrate how to perform a full-scale computational analysis of a metagenomic sequencing project, involving 12 samples and 800 million reads, in less than three days on a single server. All source code is available here: https://github.com/danielhuson/megan-ce Microbiome sequencing projects continue to grow rapidly, both in the number of samples considered and sequencing reads collected. With MEGAN Community Edition (CE), we provide a highly efficient program for interactive analysis and comparison of such data, allowing one to explore hundreds of samples and billions of reads. While taxonomic profiling is performed based on the NCBI taxonomy, we provide a number of different functional profiling approaches such as SEED, eggNOG, KEGG, and a new InterPro2GO classification scheme. MEGAN CE also supports the use of metadata in the context of principal coordinate analysis and clustering analysis.

Evidence suggests a correlation between the gut microbiota composition and weight loss caused by caloric restriction. Laparoscopic sleeve gastrectomy (LSG), a surgical intervention for obesity, is classified as predominantly restrictive procedure. In this study we investigated functional weight loss mechanisms with regard to gut microbial changes and energy harvest induced by LSG and a very low calorie diet in ten obese subjects (n=5per group) demonstrating identical weight loss during a follow-up period of six months. For gut microbiome analysis next generation sequencing was performed and faeces were analyzed for targeted metabolomics. The energy-reabsorbing potential of the gut microbiota decreased following LSG, indicated by the Bacteroidetes/Firmicutes ratio, but increased during diet. Changes in butyrate-producing bacterial species were responsible for the Firmicutes changes in both groups. No alteration of faecal butyrate was observed, but the microbial capacity for butyrate fermentation decreased following LSG and increased following dietetic intervention. LSG resulted in enhanced faecal excretion of nonesterified fatty acids and bile acids. LSG, but not dietetic restriction, improved the obesity-associated gut microbiota composition towards a lean microbiome phenotype. Moreover, LSG increased malabsorption due to loss in energy-rich faecal substrates and impairment of bile acid circulation. This trial is registered with ClinicalTrials.govNCT01344525.

Supplementation with members of the early-life microbiota as “probiotics” is increasingly used in attempts to beneficially manipulate the preterm infant gut microbiota. We performed a large observational longitudinal study comprising two preterm groups: 101 infants orally supplemented with Bifidobacterium and Lactobacillus (Bif/Lacto) and 133 infants non-supplemented (control) matched by age, sex, and delivery method. 16S rRNA gene profiling on fecal samples (n = 592) showed a predominance of Bifidobacterium and a lower abundance of pathobionts in the Bif/Lacto group. Metabolomic analysis showed higher fecal acetate and lactate and a lower fecal pH in the Bif/Lacto group compared to the control group. Fecal acetate positively correlated with relative abundance of Bifidobacterium, consistent with the ability of the supplemented Bifidobacterium strain to metabolize human milk oligosaccharides into acetate. This study demonstrates that microbiota supplementation is associated with a Bifidobacterium-dominated preterm microbiota and gastrointestinal environment more closely resembling that of full-term infants.

Infants born prematurely, particularly extremely low birth weight infants (ELBW) have altered gut microbial communities. Factors such as maternal health, gut immaturity, delivery mode, and antibiotic treatments are associated with microbiota disturbances, and are linked to an increased risk of certain diseases such as necrotising enterocolitis. Therefore, there is a requirement to optimally characterise microbial profiles in this at-risk cohort, via standardisation of methods, particularly for studying the influence of microbiota therapies (e.g. probiotic supplementation) on community profiles and health outcomes. Profiling of faecal samples using the 16S rRNA gene is a cost-efficient method for large-scale clinical studies to gain insights into the gut microbiota and additionally allows characterisation of cohorts were sample quantities are compromised (e.g. ELBW infants). However, DNA extraction method, and the 16S rRNA region targeted can significantly change bacterial community profiles obtained, and so confound comparisons between studies. Thus, we sought to optimise a 16S rRNA profiling protocol to allow standardisation for studying ELBW infant faecal samples, with or without probiotic supplementation. Using ELBW faecal samples, we compared three different DNA extraction methods, and subsequently PCR amplified and sequenced three hypervariable regions of the 16S rRNA gene (V1 + V2 + V3), (V4 + V5) and (V6 + V7 + V8), and compared two bioinformatics approaches to analyse results (OTU and paired end). Paired shotgun metagenomics was used as a ‘gold-standard’. Results indicated a longer bead-beating step was required for optimal bacterial DNA extraction and that sequencing regions (V1 + V2 + V3) and (V6 + V7 + V8) provided the most representative taxonomic profiles, which was confirmed via shotgun analysis. Samples sequenced using the (V4 + V5) region were found to be underrepresented in specific taxa including Bifidobacterium, and had altered diversity profiles. Both bioinformatics 16S rRNA pipelines used in this study (OTU and paired end) presented similar taxonomic profiles at genus level. We determined that DNA extraction from ELBW faecal samples, particularly those infants receiving probiotic supplementation, should include a prolonged beat-beating step. Furthermore, use of the 16S rRNA (V1 + V2 + V3) and (V6 + V7 + V8) regions provides reliable representation of ELBW microbiota profiles, while inclusion of the (V4 + V5) region may not be appropriate for studies where Bifidobacterium constitutes a resident microbiota member.

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.