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Rapid advancements in sequencing technologies along with falling costs present widespread opportunities for microbiome studies across a vast and diverse array of environments. These impressive technological developments have been accompanied by a considerable growth in the number of methodological variables, including sampling, storage, DNA extraction, primer pairs, sequencing technology, chemistry version, read length, insert size, and analysis pipelines, amongst others. This increase in variability threatens to compromise both the reproducibility and the comparability of studies conducted. Here we perform the first reported study comparing both amplicon and shotgun sequencing for the three leading next-generation sequencing technologies. These were applied to six human stool samples using Illumina HiSeq, MiSeq and Ion PGM shotgun sequencing, as well as amplicon sequencing across two variable 16S rRNA gene regions. Notably, we found that the factor responsible for the greatest variance in microbiota composition was the chosen methodology rather than the natural inter-individual variance, which is commonly one of the most significant drivers in microbiome studies. Amplicon sequencing suffered from this to a large extent, and this issue was particularly apparent when the 16S rRNA V1-V2 region amplicons were sequenced with MiSeq. Somewhat surprisingly, the choice of taxonomic binning software for shotgun sequences proved to be of crucial importance with even greater discriminatory power than sequencing technology and choice of amplicon. Optimal N50 assembly values for the HiSeq was obtained for 10 million reads per sample, whereas the applied MiSeq and PGM sequencing depths proved less sufficient for shotgun sequencing of stool samples. The latter technologies, on the other hand, provide a better basis for functional gene categorisation, possibly due to their longer read lengths. Hence, in addition to highlighting methodological biases, this study demonstrates the risks associated with comparing data generated using different strategies. We also recommend that laboratories with particular interests in certain microbes should optimise their protocols to accurately detect these taxa using different techniques.

The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The ‘omics’ technologies used for microbiome analysis continuously evolve and, although much of the research is still at an early stage, large-scale datasets of ever increasing size and complexity are being produced. However, there are varying levels of difficulty in realizing the full potential of these new tools, which limit our ability to critically analyse much of the available data. In this Perspective, we provide a brief overview on the role of gut microbiome in cancer and focus on the need, role and limitations of a machine learning-driven approach to analyse large amounts of complex health-care information in the era of big data. We also discuss the potential application of microbiome-based big data aimed at promoting precision medicine in cancer.Large-scale datasets of increasing size and complexity are being produced in the microbiome and oncology field. This Perspective discusses the potential to harness gut microbiome analysis, big data and machine learning in cancer, and the potential and limitations with this approach.

The human gastrointestinal tract (GIT) represents one of the most densely populated microbial ecosystems studied to date. Although this microbial consortium has been recognized to have a crucial impact on human health, its precise composition is still subject to intense investigation. Among the GIT microbiota, bifidobacteria represent an important commensal group, being among the first microbial colonizers of the gut. However, the prevalence and diversity of members of the genus Bifidobacterium in the infant intestinal microbiota has not yet been fully characterized, while some inconsistencies exist in literature regarding the abundance of this genus. In the current report, we assessed the complexity of the infant intestinal bifidobacterial population by analysis of pyrosequencing data of PCR amplicons derived from two hypervariable regions of the 16 S rRNA gene. Eleven faecal samples were collected from healthy infants of different geographical origins (Italy, Spain or Ireland), feeding type (breast milk or formula) and mode of delivery (vaginal or caesarean delivery), while in four cases, faecal samples of corresponding mothers were also analyzed. In contrast to several previously published culture-independent studies, our analysis revealed a predominance of bifidobacteria in the infant gut as well as a profile of co-occurrence of bifidobacterial species in the infant's intestine.

Variations in the composition of the human intestinal microbiota are linked to diverse health conditions. High-throughput molecular technologies have recently elucidated microbial community structure at much higher resolution than was previously possible. Here we compare two such methods, pyrosequencing and a phylogenetic array, and evaluate classifications based on two variable 16S rRNA gene regions. Over 1.75 million amplicon sequences were generated from the V4 and V6 regions of 16S rRNA genes in bacterial DNA extracted from four fecal samples of elderly individuals. The phylotype richness, for individual samples, was 1,400-1,800 for V4 reads and 12,500 for V6 reads, and 5,200 unique phylotypes when combining V4 reads from all samples. The RDP-classifier was more efficient for the V4 than for the far less conserved and shorter V6 region, but differences in community structure also affected efficiency. Even when analyzing only 20% of the reads, the majority of the microbial diversity was captured in two samples tested. DNA from the four samples was hybridized against the Human Intestinal Tract (HIT) Chip, a phylogenetic microarray for community profiling. Comparison of clustering of genus counts from pyrosequencing and HITChip data revealed highly similar profiles. Furthermore, correlations of sequence abundance and hybridization signal intensities were very high for lower-order ranks, but lower at family-level, which was probably due to ambiguous taxonomic groupings. The RDP-classifier consistently assigned most V4 sequences from human intestinal samples down to genus-level with good accuracy and speed. This is the deepest sequencing of single gastrointestinal samples reported to date, but microbial richness levels have still not leveled out. A majority of these diversities can also be captured with five times lower sampling-depth. HITChip hybridizations and resulting community profiles correlate well with pyrosequencing-based compositions, especially for lower-order ranks, indicating high robustness of both approaches. However, incompatible grouping schemes make exact comparison difficult.

Background Next-generation sequencing platforms have revolutionised our ability to investigate the microbiota composition of complex environments, frequently through 16S rRNA gene sequencing of the bacterial component of the community. Numerous factors, including DNA extraction method, primer sequences and sequencing platform employed, can affect the accuracy of the results achieved. The aim of this study was to determine the impact of these three factors on 16S rRNA gene sequencing results, using mock communities and mock community DNA. Results The use of different primer sequences (V4-V5, V1-V2 and V1-V2 degenerate primers) resulted in differences in the genera and species detected. The V4-V5 primers gave the most comparable results across platforms. The three Ion PGM primer sets detected more of the 20 mock community species than the equivalent MiSeq primer sets. Data generated from DNA extracted using the 2 extraction methods were very similar. Conclusions Microbiota compositional data differed depending on the primers and sequencing platform that were used. The results demonstrate the risks in comparing data generated using different sequencing approaches and highlight the merits of choosing a standardised approach for sequencing in situations where a comparison across multiple sequencing runs is required.

Background Shotgun metagenomic sequencing reveals the potential in microbial communities. However, lower-cost 16S ribosomal RNA (rRNA) gene sequencing provides taxonomic, not functional, observations. To remedy this, we previously introduced Piphillin, a software package that predicts functional metagenomic content based on the frequency of detected 16S rRNA gene sequences corresponding to genomes in regularly updated, functionally annotated genome databases. Piphillin (and similar tools) have previously been evaluated on 16S rRNA data processed by the clustering of sequences into operational taxonomic units (OTUs). New techniques such as amplicon sequence variant error correction are in increased use, but it is unknown if these techniques perform better in metagenomic content prediction pipelines, or if they should be treated the same as OTU data in respect to optimal pipeline parameters. Results To evaluate the effect of 16S rRNA sequence analysis method (clustering sequences into OTUs vs amplicon sequence variant error correction into amplicon sequence variants (ASVs)) on the ability of Piphillin to predict functional metagenomic content, we evaluated Piphillin-predicted functional content from 16S rRNA sequence data processed through OTU clustering and error correction into ASVs compared to corresponding shotgun metagenomic data. We show a strong correlation between metagenomic data and Piphillin-predicted functional content resulting from both 16S rRNA sequence analysis methods. Differential abundance testing with Piphillin-predicted functional content exhibited a low false positive rate (< 0.05) while capturing a large fraction of the differentially abundant features resulting from corresponding metagenomic data. However, Piphillin prediction performance was optimal at different cutoff parameters depending on 16S rRNA sequence analysis method. Using data analyzed with amplicon sequence variant error correction, Piphillin outperformed comparable tools, for instance exhibiting 19% greater balanced accuracy and 54% greater precision compared to PICRUSt2. Conclusions Our results demonstrate that raw Illumina sequences should be processed for subsequent Piphillin analysis using amplicon sequence variant error correction (with DADA2 or similar methods) and run using a 99% ID cutoff for Piphillin, while sequences generated on platforms other than Illumina should be processed via OTU clustering (e.g., UPARSE) and run using a 96% ID cutoff for Piphillin. Piphillin is publicly available for academic users (Piphillin server. http://piphillin.secondgenome.com/ .)

Grouping the microbiota of individual subjects into compositional categories, or enterotypes, based on the dominance of certain genera may have oversimplified a complex situation. Grouping the microbiota of individual subjects into compositional categories, or enterotypes, based on the dominance of certain genera may have oversimplified a complex situation.

Alterations in the human intestinal microbiota are linked to conditions including inflammatory bowel disease, irritable bowel syndrome, and obesity. The microbiota also undergoes substantial changes at the extremes of life, in infants and older people, the ramifications of which are still being explored. We applied pyrosequencing of over 40,000 16S rRNA gene V4 region amplicons per subject to characterize the fecal microbiota in 161 subjects aged 65 y and older and 9 younger control subjects. The microbiota of each individual subject constituted a unique profile that was separable from all others. In 68% of the individuals, the microbiota was dominated by phylum Bacteroides, with an average proportion of 57% across all 161 baseline samples. Phylum Firmicutes had an average proportion of 40%. The proportions of some phyla and genera associated with disease or health also varied dramatically, including Proteobacteria, Actinobacteria, and FAECALIBACTERIA: The core microbiota of elderly subjects was distinct from that previously established for younger adults, with a greater proportion of Bacteroides spp. and distinct abundance patterns of Clostridium groups. Analyses of 26 fecal microbiota datasets from 3-month follow-up samples indicated that in 85% of the subjects, the microbiota composition was more like the corresponding time-0 sample than any other dataset. We conclude that the fecal microbiota of the elderly shows temporal stability over limited time in the majority of subjects but is characterized by unusual phylum proportions and extreme variability.

Stipitate kelp species such as Laminaria digitata dominate most cold-water subtidal rocky shores and form underwater forests which are among the most productive coastal systems worldwide. Laminaria also sustains rich bacterial communities which offer a variety of biotechnological applications. However, to date, in-depth studies on the diversity and uniqueness of bacterial communities associated with this macroalgal species, their ecological role and their interactions with the alga are under-represented. To address this, the epibacterial populations associated with different thallus regions (holdfast, stipe, meristem, blade) of this brown seaweed were investigated using high-throughput Illumina sequencing of the 16S rRNA genes. The results show that epibacterial communities of the brown seaweed are significantly different and specific to the thallus region, with the shared bacterial population comprising of only 1.1% of the total amplicon sequence variants. The diverse holdfast and blade tissues formed distinct clusters while the meristem and stipe regions are more closely related. The data obtained further supports the hypothesis that macroalgal bacterial communities are shaped by morphological niches and display specificity.

Fermented foods are regaining popularity worldwide due in part to a greater appreciation of the health benefits of these foods and the associated microorganisms. Here, we use state-of-the-art approaches to explore the microbiomes of 58 of these foods, identifying the factors that drive the microbial composition of these foods and potential functional benefits associated with these populations. Food type, i.e., dairy-, sugar-, or brine-type fermented foods, was the primary driver of microbial composition, with dairy foods found to have the lowest microbial diversity and, notably, potential health promoting attributes were more common in fermented foods than nonfermented equivalents. The information provided here will provide significant opportunities for the further optimization of fermented-food production and the harnessing of their health-promoting potential. Fermented foods have been the focus of ever greater interest as a consequence of purported health benefits. Indeed, it has been suggested that consumption of these foods helps to address the negative consequences of “industrialization” of the human gut microbiota in Western society. However, as the mechanisms via which the microbes in fermented foods improve health are not understood, it is necessary to develop an understanding of the composition and functionality of the fermented-food microbiota to better harness desirable traits. Here, we considerably expand the understanding of fermented-food microbiomes by employing shotgun metagenomic sequencing to provide a comprehensive insight into the microbial composition, diversity, and functional potential (including antimicrobial resistance and carbohydrate-degrading and health-associated gene content) of a diverse range of 58 fermented foods from artisanal producers from a number of countries. Food type, i.e., dairy-, sugar-, or brine-type fermented foods, was the primary driver of microbial composition, with dairy foods found to have the lowest microbial diversity. From the combined data set, 127 high-quality metagenome-assembled genomes (MAGs), including 10 MAGs representing putatively novel species of Acetobacter , Acidisphaera , Gluconobacter , Companilactobacillus , Leuconostoc , and Rouxiella , were generated. Potential health promoting attributes were more common in fermented foods than nonfermented equivalents, with water kefirs, sauerkrauts, and kvasses containing the greatest numbers of potentially health-associated gene clusters. Ultimately, this study provides the most comprehensive insight into the microbiomes of fermented foods to date and yields novel information regarding their relative health-promoting potential. IMPORTANCE Fermented foods are regaining popularity worldwide due in part to a greater appreciation of the health benefits of these foods and the associated microorganisms. Here, we use state-of-the-art approaches to explore the microbiomes of 58 of these foods, identifying the factors that drive the microbial composition of these foods and potential functional benefits associated with these populations. Food type, i.e., dairy-, sugar-, or brine-type fermented foods, was the primary driver of microbial composition, with dairy foods found to have the lowest microbial diversity and, notably, potential health promoting attributes were more common in fermented foods than nonfermented equivalents. The information provided here will provide significant opportunities for the further optimization of fermented-food production and the harnessing of their health-promoting potential.