Relating the metatranscriptome and metagenome of the human gut. Proc Natl Acad Sci U S A. 2014;111:E2329-E2338. [PMID: 24843156 DOI: 10.1073/pnas.1319284111]

Temporal Stability of the Human Skin Microbiome

Biogeography and individuality shape the structural and functional composition of the human skin microbiome. To explore these factors' contribution to skin microbial community stability, we generated metagenomic sequence data from longitudinal samples collected over months and years. Analyzing these samples using a multi-kingdom, reference-based approach, we found that despite the skin's exposure to the external environment, its bacterial, fungal, and viral communities were largely stable over time. Site, individuality, and phylogeny were all determinants of stability. Foot sites exhibited the most variability; individuals differed in stability; and transience was a particular characteristic of eukaryotic viruses, which showed little site-specificity in colonization. Strain and single-nucleotide variant-level analysis showed that individuals maintain, rather than reacquire, prevalent microbes from the environment. Longitudinal stability of skin microbial communities generates hypotheses about colonization resistance and empowers clinical studies exploring alterations observed in disease states.

IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses

Existing workflows for the analysis of multi-omic microbiome datasets are lab-specific and often result in sub-optimal data usage. Here we present IMP, a reproducible and modular pipeline for the integrated and reference-independent analysis of coupled metagenomic and metatranscriptomic data. IMP incorporates robust read preprocessing, iterative co-assembly, analyses of microbial community structure and function, automated binning, as well as genomic signature-based visualizations. The IMP-based data integration strategy enhances data usage, output volume, and output quality as demonstrated using relevant use-cases. Finally, IMP is encapsulated within a user-friendly implementation using Python and Docker. IMP is available at http://r3lab.uni.lu/web/imp/ (MIT license).

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

Emerging Concepts and Technologies for the Discovery of Microorganisms Involved in Human Disease

Established infectious agents continue to be a major cause of human morbidity and mortality worldwide. However, the causative agent remains unknown for a wide range of diseases; many of these are suspected to be attributable to yet undiscovered microorganisms. The advent of unbiased high-throughput sequencing and bioinformatics has enabled rapid identification and molecular characterization of known and novel infectious agents in human disease. An exciting era of microbe discovery, now under way, holds great promise for the improvement of global health via the development of antimicrobial therapies, vaccination strategies, targeted public health measures, and probiotic-based preventions and therapies. Here, we review the history of pathogen discovery, discuss improvements and clinical applications for the detection of microbially associated diseases, and explore the challenges and strategies for establishing causation in human disease.

Function and Phylogeny of Bacterial Butyryl Coenzyme A:Acetate Transferases and Their Diversity in the Proximal Colon of Swine

Studying the host-associated butyrate-producing bacterial community is important, because butyrate is essential for colonic homeostasis and gut health. Previous research has identified the butyryl coenzyme A (CoA):acetate-CoA transferase (EC 2.3.8.3) as a gene of primary importance for butyrate production in intestinal ecosystems; however, this gene family (but) remains poorly defined. We developed tools for the analysis of butyrate-producing bacteria based on 12 putative but genes identified in the genomes of nine butyrate-producing bacteria obtained from the swine intestinal tract. Functional analyses revealed that eight of these genes had strong But enzyme activity. When but paralogues were found within a genome, only one gene per genome encoded strong activity, with the exception of one strain in which no gene encoded strong But activity. Degenerate primers were designed to amplify the functional but genes and were tested by amplifying environmental but sequences from DNA and RNA extracted from swine colonic contents. The results show diverse but sequences from swine-associated butyrate-producing bacteria, most of which clustered near functionally confirmed sequences. Here, we describe tools and a framework that allow the bacterial butyrate-producing community to be profiled in the context of animal health and disease.

IMPORTANCE

Butyrate is a compound produced by the microbiota in the intestinal tracts of animals. This compound is of critical importance for intestinal health, and yet studying its production by diverse intestinal bacteria is technically challenging. Here, we present an additional way to study the butyrate-producing community of bacteria using one degenerate primer set that selectively targets genes experimentally demonstrated to encode butyrate production. This work will enable researchers to more easily study this very important bacterial function that has implications for host health and resistance to disease.

An introduction to microbiome analysis for human biology applications

Research examining the gut microbiota is currently exploding, and results are providing new perspectives on human biology. Factors such as host diet and physiology influence the composition and function of the gut microbiota, which in turn affects human nutrition, health, and behavior via interactions with metabolism, the immune system, and the brain. These findings represent an exciting new twist on familiar topics, and as a result, gut microbiome research is likely to provide insight into unresolved biological mechanisms driving human health. However, much remains to be learned about the broader ecological and evolutionary contexts within which gut microbes and humans are affecting each other. Here, I outline the procedures for generating data describing the gut microbiota with the goal of facilitating the wider integration of microbiome analyses into studies of human biology. I describe the steps involved in sample collection, DNA extraction, PCR amplification, high-throughput sequencing, and bioinformatics. While this review serves only as an introduction to these topics, it provides sufficient resources for researchers interested in launching new microbiome initiatives. As knowledge of these methods spreads, microbiome analysis should become a standard tool in the arsenal of human biology research.

Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes

The gastrointestinal microbiome is a complex ecosystem with functions that shape human health. Studying the relationship between taxonomic alterations and functional repercussions linked to disease remains challenging. Here, we present an integrative approach to resolve the taxonomic and functional attributes of gastrointestinal microbiota at the metagenomic, metatranscriptomic and metaproteomic levels. We apply our methods to samples from four families with multiple cases of type 1 diabetes mellitus (T1DM). Analysis of intra- and inter-individual variation demonstrates that family membership has a pronounced effect on the structural and functional composition of the gastrointestinal microbiome. In the context of T1DM, consistent taxonomic differences were absent across families, but certain human exocrine pancreatic proteins were found at lower levels. The associated microbial functional signatures were linked to metabolic traits in distinct taxa. The methodologies and results provide a foundation for future large-scale integrated multi-omic analyses of the gastrointestinal microbiome in the context of host-microbe interactions in human health and disease.

SAMSA: a comprehensive metatranscriptome analysis pipeline

BACKGROUND

Although metatranscriptomics-the study of diverse microbial population activity based on RNA-seq data-is rapidly growing in popularity, there are limited options for biologists to analyze this type of data. Current approaches for processing metatranscriptomes rely on restricted databases and a dedicated computing cluster, or metagenome-based approaches that have not been fully evaluated for processing metatranscriptomic datasets. We created a new bioinformatics pipeline, designed specifically for metatranscriptome dataset analysis, which runs in conjunction with Metagenome-RAST (MG-RAST) servers. Designed for use by researchers with relatively little bioinformatics experience, SAMSA offers a breakdown of metatranscriptome transcription activity levels by organism or transcript function, and is fully open source. We used this new tool to evaluate best practices for sequencing stool metatranscriptomes.

RESULTS

Working with the MG-RAST annotation server, we constructed the Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) software package, a complete pipeline for the analysis of gut microbiome data. SAMSA can summarize and evaluate raw annotation results, identifying abundant species and significant functional differences between metatranscriptomes. Using pilot data and simulated subsets, we determined experimental requirements for fecal gut metatranscriptomes. Sequences need to be either long reads (longer than 100 bp) or joined paired-end reads. Each sample needs 40-50 million raw sequences, which can be expected to yield the 5-10 million annotated reads necessary for accurate abundance measures. We also demonstrated that ribosomal RNA depletion does not equally deplete ribosomes from all species within a sample, and remaining rRNA sequences should be discarded. Using publicly available metatranscriptome data in which rRNA was not depleted, we were able to demonstrate that overall organism transcriptional activity can be measured using mRNA counts. We were also able to detect significant differences between control and experimental groups in both organism transcriptional activity and specific cellular functions.

CONCLUSIONS

By making this new pipeline publicly available, we have created a powerful new tool for metatranscriptomics research, offering a new method for greater insight into the activity of diverse microbial communities. We further recommend that stool metatranscriptomes be ribodepleted and sequenced in a 100 bp paired end format with a minimum of 40 million reads per sample.

Periodontal and inflammatory bowel diseases: Is there evidence of complex pathogenic interactions?

Periodontal disease and inflammatory bowel disease (IBD) are both chronic inflammatory diseases. Their pathogenesis is mediated by a complex interplay between a dysbiotic microbiota and the host immune-inflammatory response, and both are influenced by genetic and environmental factors. This review aimed to provide an overview of the evidence dealing with a possible pathogenic interaction between periodontal disease and IBD. There seems to be an increased prevalence of periodontal disease in patients with IBD when compared to healthy controls, probably due to changes in the oral microbiota and a higher inflammatory response. Moreover, the induction of periodontitis seems to result in gut dysbiosis and altered gut epithelial cell barrier function, which might contribute to the pathogenesis of IBD. Considering the complexity of both periodontal disease and IBD, it is very challenging to understand the possible pathways involved in their coexistence. In conclusion, this review points to a complex pathogenic interaction between periodontal disease and IBD, in which one disease might alter the composition of the microbiota and increase the inflammatory response related to the other. However, we still need more data derived from human studies to confirm results from murine models. Thus, mechanistic studies are definitely warranted to clarify this possible bidirectional association.

Metabolic modeling with Big Data and the gut microbiome

The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework - or metabolic model - on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.

Studying the Human Microbiota

There are a range of methodologies available to study the human microbiota, ranging from traditional approaches such as culturing through to state-of-the-art developments in next generation DNA sequencing technologies. The advent of molecular techniques in particular has opened up tremendous new avenues for research, and has galvanised interest in the study of our microbial inhabitants. Given the dazzling array of available options, however, it is important to understand the inherent advantages and limitations of each technique so that the best approach can be employed to address the particular research objective. In this chapter we cover some of the most widely used current techniques in human microbiota research and highlight the particular strengths and caveats associated with each approach.

Transcriptional interactions suggest niche segregation among microorganisms in the human gut

The human gastrointestinal (GI) tract is the habitat for hundreds of microbial species, of which many cannot be cultivated readily, presumably because of the dependencies between species¹. Studies of microbial co-occurrence in the gut have indicated community substructures that may reflect functional and metabolic interactions between cohabiting species^2,3. To move beyond species co-occurrence networks, we systematically identified transcriptional interactions between pairs of coexisting gut microbes using metagenomics and microarray-based metatranscriptomics data from 233 stool samples from Europeans. In 102 significantly interacting species pairs, the transcriptional changes led to a reduced expression of orthologous functions between the coexisting species. Specific species-species transcriptional interactions were enriched for functions important for H₂ and CO₂ homeostasis, butyrate biosynthesis, ATP-binding cassette (ABC) transporters, flagella assembly and bacterial chemotaxis, as well as for the metabolism of carbohydrates, amino acids and cofactors. The analysis gives the first insight into the microbial community-wide transcriptional interactions, and suggests that the regulation of gene expression plays an important role in species adaptation to coexistence and that niche segregation takes place at the transcriptional level.

MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities

Culture independent techniques, such as shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. Recently, changes in microbial community structure and dynamics have been associated with a growing list of human diseases. The identification and comparison of bacteria driving those changes requires the development of sound statistical tools, especially if microbial biomarkers are to be used in a clinical setting. We present mixMC, a novel multivariate data analysis framework for metagenomic biomarker discovery. mixMC accounts for the compositional nature of 16S data and enables detection of subtle differences when high inter-subject variability is present due to microbial sampling performed repeatedly on the same subjects, but in multiple habitats. Through data dimension reduction the multivariate methods provide insightful graphical visualisations to characterise each type of environment in a detailed manner. We applied mixMC to 16S microbiome studies focusing on multiple body sites in healthy individuals, compared our results with existing statistical tools and illustrated added value of using multivariate methodologies to fully characterise and compare microbial communities.

Microbial bioinformatics 2020

Microbial bioinformatics in 2020 will remain a vibrant, creative discipline, adding value to the ever‐growing flood of new sequence data, while embracing novel technologies and fresh approaches. Databases and search strategies will struggle to cope and manual curation will not be sustainable during the scale‐up to the million‐microbial‐genome era. Microbial taxonomy will have to adapt to a situation in which most microorganisms are discovered and characterised through the analysis of sequences. Genome sequencing will become a routine approach in clinical and research laboratories, with fresh demands for interpretable user‐friendly outputs. The “internet of things” will penetrate healthcare systems, so that even a piece of hospital plumbing might have its own IP address that can be integrated with pathogen genome sequences. Microbiome mania will continue, but the tide will turn from molecular barcoding towards metagenomics. Crowd‐sourced analyses will collide with cloud computing, but eternal vigilance will be the price of preventing the misinterpretation and overselling of microbial sequence data. Output from hand‐held sequencers will be analysed on mobile devices. Open‐source training materials will address the need for the development of a skilled labour force. As we boldly go into the third decade of the twenty‐first century, microbial sequence space will remain the final frontier!

Do Vertebrate Gut Metagenomes Confer Rapid Ecological Adaptation?

During times of rapid environmental change, survival of most vertebrate populations depends on their phenomic plasticity. Although differential gene-expression and post-transcriptional processes of the host genome receive focus as the main molecular mechanisms, growing evidence points to the gut microbiota as a key driver defining hosts' phenotypes. We propose that the plasticity of the gut microbiota might be an essential factor determining phenomic plasticity of vertebrates, and that it might play a pivotal role when vertebrates acclimate and adapt to fast environmental variation. We contemplate some key questions and suggest methodological approaches and experimental designs that can be used to evaluate whether gut microorganisms provide a boost of plasticity to vertebrates' phenomes, thereby increasing their acclimation and adaptation capacity.

Emerging Technologies for Gut Microbiome Research

Understanding the importance of the gut microbiome on modulation of host health has become a subject of great interest for researchers across disciplines. As an intrinsically multidisciplinary field, microbiome research has been able to reap the benefits of technological advancements in systems and synthetic biology, biomaterials engineering, and traditional microbiology. Gut microbiome research has been revolutionized by high-throughput sequencing technology, permitting compositional and functional analyses that were previously an unrealistic undertaking. Emerging technologies, including engineered organoids derived from human stem cells, high-throughput culturing, and microfluidics assays allowing for the introduction of novel approaches, will improve the efficiency and quality of microbiome research. Here, we discuss emerging technologies and their potential impact on gut microbiome studies.

Analysis of the mouse gut microbiome using full-length 16S rRNA amplicon sequencing

Demands for faster and more accurate methods to analyze microbial communities from natural and clinical samples have been increasing in the medical and healthcare industry. Recent advances in next-generation sequencing technologies have facilitated the elucidation of the microbial community composition with higher accuracy and greater throughput than was previously achievable; however, the short sequencing reads often limit the microbial composition analysis at the species level due to the high similarity of 16S rRNA amplicon sequences. To overcome this limitation, we used the nanopore sequencing platform to sequence full-length 16S rRNA amplicon libraries prepared from the mouse gut microbiota. A comparison of the nanopore and short-read sequencing data showed that there were no significant differences in major taxonomic units (89%) except one phylotype and three taxonomic units. Moreover, both sequencing data were highly similar at all taxonomic resolutions except the species level. At the species level, nanopore sequencing allowed identification of more species than short-read sequencing, facilitating the accurate classification of the bacterial community composition. Therefore, this method of full-length 16S rRNA amplicon sequencing will be useful for rapid, accurate and efficient detection of microbial diversity in various biological and clinical samples.

Characterization of CRISPR RNA transcription by exploiting stranded metatranscriptomic data

CRISPR-Cas systems are bacterial adaptive immune systems, each typically composed of a locus of cas genes and a CRISPR array of spacers flanked by repeats. Processed transcripts of CRISPR arrays (crRNAs) play important roles in the interference process mediated by these systems, guiding targeted immunity. Here we developed computational approaches that allow us to characterize the expression of many CRISPRs in their natural environments, using community RNA-seq (metatranscriptomic) data. By exploiting public human gut metatranscriptomic data sets, we studied the expression of 56 repeat-sequence types of CRISPRs, revealing that most CRISPRs are transcribed in one direction (producing crRNAs). In rarer cases, including a type II system associated with Bacteroides fragilis, CRISPRs are transcribed in both directions. Type III CRISPR-Cas systems were found in the microbiomes, but metatranscriptomic reads were barely found for their CRISPRs. We observed individual-level variation of the crRNA transcription, and an even greater transcription of a CRISPR from the antisense strand than the crRNA strand in one sample. The orientations of CRISPR expression implicated by metatranscriptomic data are largely in agreement with prior predictions for CRISPRs, with exceptions. Our study shows the promise of exploiting community RNA-seq data for investigating the transcription of CRISPR-Cas systems.

Simultaneous purification of DNA and RNA from microbiota in a single colonic mucosal biopsy

BACKGROUND

Nucleic acid purification methods are of importance when performing microbiota studies and especially when analysing the intestinal microbiota as we here find a wide range of different microbes. Various considerations must be taken to lyse the microbial cell wall of each microbe. In the present article, we compare several tissue lysis steps and commercial purification kits, to achieve a joint RNA and DNA purification protocol for the purpose of investigating the microbiota and the microbiota-host interactions in a single colonic mucosal tissue sample.

RESULTS

A further optimised tissue homogenisation and lysis protocol comprising mechanical bead beating, lysis buffer replacement and enzymatic treatment, in combination with the AllPrep DNA/RNA Mini Kit (Qiagen, Hilden, Germany) resulted in efficient and simultaneous purification of microbial and human RNA and DNA from a single mucosal colonic tissue sample.

CONCLUSIONS

The present work provides a unique possibility to study RNA and DNA from the same mucosal biopsy sample, making a direct comparison between metabolically active microbes and total microbial DNA. The protocol also offers an opportunity to investigate other members of a microbiota such as viruses, fungi and micro-eukaryotes, and moreover the possibility to extract data on microbiota and host interactions from one single mucosal biopsy.

Analyses of Intestinal Microbiota: Culture versus Sequencing

Analyzing human as well as animal microbiota composition has gained growing interest because structural components and metabolites of microorganisms fundamentally influence all aspects of host physiology. Originally dominated by culture-dependent methods for exploring these ecosystems, the development of molecular techniques such as high throughput sequencing has dramatically increased our knowledge. Because many studies of the microbiota are based on the bacterial 16S ribosomal RNA (rRNA) gene targets, they can, at least in principle, be compared to determine the role of the microbiome composition for developmental processes, host metabolism, and physiology as well as different diseases. In our review, we will summarize differences and pitfalls in current experimental protocols, including all steps from nucleic acid extraction to bioinformatical analysis which may produce variation that outweighs subtle biological differences. Future developments, such as integration of metabolomic, transcriptomic, and metagenomic data sets and standardization of the procedures, will be discussed.

MetaPro-IQ: a universal metaproteomic approach to studying human and mouse gut microbiota

BACKGROUND

The gut microbiota has been shown to be closely associated with human health and disease. While next-generation sequencing can be readily used to profile the microbiota taxonomy and metabolic potential, metaproteomics is better suited for deciphering microbial biological activities. However, the application of gut metaproteomics has largely been limited due to the low efficiency of protein identification. Thus, a high-performance and easy-to-implement gut metaproteomic approach is required.

RESULTS

In this study, we developed a high-performance and universal workflow for gut metaproteome identification and quantification (named MetaPro-IQ) by using the close-to-complete human or mouse gut microbial gene catalog as database and an iterative database search strategy. An average of 38 and 33 % of the acquired tandem mass spectrometry (MS) spectra was confidently identified for the studied mouse stool and human mucosal-luminal interface samples, respectively. In total, we accurately quantified 30,749 protein groups for the mouse metaproteome and 19,011 protein groups for the human metaproteome. Moreover, the MetaPro-IQ approach enabled comparable identifications with the matched metagenome database search strategy that is widely used but needs prior metagenomic sequencing. The response of gut microbiota to high-fat diet in mice was then assessed, which showed distinct metaproteome patterns for high-fat-fed mice and identified 849 proteins as significant responders to high-fat feeding in comparison to low-fat feeding.

CONCLUSIONS

We present MetaPro-IQ, a metaproteomic approach for highly efficient intestinal microbial protein identification and quantification, which functions as a universal workflow for metaproteomic studies, and will thus facilitate the application of metaproteomics for better understanding the functions of gut microbiota in health and disease.

Taxonomic Assessment of Rumen Microbiota Using Total RNA and Targeted Amplicon Sequencing Approaches

Taxonomic characterization of active gastrointestinal microbiota is essential to detect shifts in microbial communities and functions under various conditions. This study aimed to identify and quantify potentially active rumen microbiota using total RNA sequencing and to compare the outcomes of this approach with the widely used targeted RNA/DNA amplicon sequencing technique. Total RNA isolated from rumen digesta samples from five beef steers was subjected to Illumina paired-end sequencing (RNA-seq), and bacterial and archaeal amplicons of partial 16S rRNA/rDNA were subjected to 454 pyrosequencing (RNA/DNA Amplicon-seq). Taxonomic assessments of the RNA-seq, RNA Amplicon-seq, and DNA Amplicon-seq datasets were performed using a pipeline developed in house. The detected major microbial phylotypes were common among the three datasets, with seven bacterial phyla, fifteen bacterial families, and five archaeal taxa commonly identified across all datasets. There were also unique microbial taxa detected in each dataset. Elusimicrobia and Verrucomicrobia phyla; Desulfovibrionaceae, Elusimicrobiaceae, and Sphaerochaetaceae families; and Methanobrevibacter woesei were only detected in the RNA-Seq and RNA Amplicon-seq datasets, whereas Streptococcaceae was only detected in the DNA Amplicon-seq dataset. In addition, the relative abundances of four bacterial phyla, eight bacterial families and one archaeal taxon were different among the three datasets. This is the first study to compare the outcomes of rumen microbiota profiling between RNA-seq and RNA/DNA Amplicon-seq datasets. Our results illustrate the differences between these methods in characterizing microbiota both qualitatively and quantitatively for the same sample, and so caution must be exercised when comparing data.

Interplay between diet, gut microbiota, epigenetic events, and colorectal cancer

Despite the success of colonoscopy screening, colorectal cancer (CRC) remains one of the most common and deadly cancers, and CRC incidence is rising in some countries where screening is not routine and populations have recently switched from traditional diets to western diets. Diet and energy balance influence CRC by multiple mechanisms. They modulate the composition and function of gut microbiota, which have a prodigious metabolic capacity and can produce oncometabolites or tumor-suppressive metabolites depending, in part, on which dietary factors and digestive components are present in the GI tract. Gut microbiota also have a profound effect on immune cells in the lamina propria, which influences inflammation and subsequently CRC. Nutrient availability, which is an outcome of diet and energy balance, determines the abundance of certain energy metabolites that are essential co-factors for epigenetic enzymes and therefore impinges upon epigenetic regulation of gene expression. Aberrant epigenetic marks accumulate during CRC, and epimutations that are selected for drive tumorigenesis by causing transcriptome profiles to diverge from the cell of origin. In some instances, the above mechanisms are intertwined as exemplified by dietary fiber being metabolized by colonic bacteria into butyrate, which is both a short-chain fatty acid (SCFA) and a histone deacetylase (HDAC) inhibitor that epigenetically upregulates tumor-suppressor genes in CRC cells and anti-inflammatory genes in immune cells.

Uncovering oral Neisseria tropism and persistence using metagenomic sequencing

Microbial epidemiology and population genomics have previously been carried out near-exclusively for organisms grown in vitro. Metagenomics helps to overcome this limitation, but it is still challenging to achieve strain-level characterization of microorganisms from culture-independent data with sufficient resolution for epidemiological modelling. Here, we have developed multiple complementary approaches that can be combined to profile and track individual microbial strains. To specifically profile highly recombinant neisseriae from oral metagenomes, we integrated four metagenomic analysis techniques: single nucleotide polymorphisms in the clade's core genome, DNA uptake sequence signatures, metagenomic multilocus sequence typing and strain-specific marker genes. We applied these tools to 520 oral metagenomes from the Human Microbiome Project, finding evidence of site tropism and temporal intra-subject strain retention. Although the opportunistic pathogen Neisseria meningitidis is enriched for colonization in the throat, N. flavescens and N. subflava populate the tongue dorsum, and N. sicca, N. mucosa and N. elongata the gingival plaque. The buccal mucosa appeared as an intermediate ecological niche between the plaque and the tongue. The resulting approaches to metagenomic strain profiling are generalizable and can be extended to other organisms and microbiomes across environments.

Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis

Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from multiple omics have been pursued. Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment. Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture. We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes.

Colorectal Cancer and the Human Gut Microbiome: Reproducibility with Whole-Genome Shotgun Sequencing

Accumulating evidence indicates that the gut microbiota affects colorectal cancer development, but previous studies have varied in population, technical methods, and associations with cancer. Understanding these variations is needed for comparisons and for potential pooling across studies. Therefore, we performed whole-genome shotgun sequencing on fecal samples from 52 pre-treatment colorectal cancer cases and 52 matched controls from Washington, DC. We compared findings from a previously published 16S rRNA study to the metagenomics-derived taxonomy within the same population. In addition, metagenome-predicted genes, modules, and pathways in the Washington, DC cases and controls were compared to cases and controls recruited in France whose specimens were processed using the same platform. Associations between the presence of fecal Fusobacteria, Fusobacterium, and Porphyromonas with colorectal cancer detected by 16S rRNA were reproduced by metagenomics, whereas higher relative abundance of Clostridia in cancer cases based on 16S rRNA was merely borderline based on metagenomics. This demonstrated that within the same sample set, most, but not all taxonomic associations were seen with both methods. Considering significant cancer associations with the relative abundance of genes, modules, and pathways in a recently published French metagenomics dataset, statistically significant associations in the Washington, DC population were detected for four out of 10 genes, three out of nine modules, and seven out of 17 pathways. In total, colorectal cancer status in the Washington, DC study was associated with 39% of the metagenome-predicted genes, modules, and pathways identified in the French study. More within and between population comparisons are needed to identify sources of variation and disease associations that can be reproduced despite these variations. Future studies should have larger sample sizes or pool data across studies to have sufficient power to detect associations that are reproducible and significant after correction for multiple testing.

Effect of room temperature transport vials on DNA quality and phylogenetic composition of faecal microbiota of elderly adults and infants

BACKGROUND

Alterations in intestinal microbiota have been correlated with a growing number of diseases. Investigating the faecal microbiota is widely used as a non-invasive and ethically simple proxy for intestinal biopsies. There is an urgent need for collection and transport media that would allow faecal sampling at distance from the processing laboratory, obviating the need for same-day DNA extraction recommended by previous studies of freezing and processing methods for stool. We compared the faecal bacterial DNA quality and apparent phylogenetic composition derived using a commercial kit for stool storage and transport (DNA Genotek OMNIgene GUT) with that of freshly extracted samples, 22 from infants and 20 from older adults.

RESULTS

Use of the storage vials increased the quality of extracted bacterial DNA by reduction of DNA shearing. When infant and elderly datasets were examined separately, no differences in microbiota composition were observed due to storage. When the two datasets were combined, there was a difference according to a Wilcoxon test in the relative proportions of Faecalibacterium, Sporobacter, Clostridium XVIII, and Clostridium XlVa after 1 week's storage compared to immediately extracted samples. After 2 weeks' storage, Bacteroides abundance was also significantly different, showing an apparent increase from week 1 to week 2. The microbiota composition of infant samples was more affected than that of elderly samples by storage, with significantly higher Spearman distances between paired freshly extracted and stored samples (p < 0.001). When the microbiota profiles were analysed at the operational taxonomic unit (OTU) level, three infant datasets in the study did not cluster together, while only one elderly dataset did not. The lower microbiota diversity of the infant gut microbiota compared to the elderly gut microbiota (p < 0.001) means that any alteration in the infant datasets has a proportionally larger effect.

CONCLUSIONS

The commercial storage vials appear to be suitable for high diversity microbiota samples, but may be less appropriate for lower diversity samples. Differences between fresh and stored samples mean that where storage is unavoidable, a consistent storage regime should be used. We would recommend extraction ideally within the first week of storage.

Preservation Methods Differ in Fecal Microbiome Stability, Affecting Suitability for Field Studies

Our study, spanning 15 individuals and over 1,200 samples, provides our most comprehensive view to date of storage and stabilization effects on stool. We tested five methods for preserving human and dog fecal specimens for periods of up to 8 weeks, including the types of variation often encountered under field conditions, such as freeze-thaw cycles and high temperature fluctuations. We show that several cost-effective methods provide excellent microbiome stability out to 8 weeks, opening up a range of field studies with humans and wildlife that would otherwise be cost-prohibitive.

Immediate freezing at −20°C or below has been considered the gold standard for microbiome preservation, yet this approach is not feasible for many field studies, ranging from anthropology to wildlife conservation. Here we tested five methods for preserving human and dog fecal specimens for periods of up to 8 weeks, including such types of variation as freeze-thaw cycles and the high temperature fluctuations often encountered under field conditions. We found that three of the methods—95% ethanol, FTA cards, and the OMNIgene Gut kit—can preserve samples sufficiently well at ambient temperatures such that differences at 8 weeks are comparable to differences among technical replicates. However, even the worst methods, including those with no fixative, were able to reveal microbiome differences between species at 8 weeks and between individuals after a week, allowing meta-analyses of samples collected using various methods when the effect of interest is expected to be larger than interindividual variation (although use of a single method within a study is strongly recommended to reduce batch effects). Encouragingly for FTA cards, the differences caused by this method are systematic and can be detrended. As in other studies, we strongly caution against the use of 70% ethanol. The results, spanning 15 individuals and over 1,200 samples, provide our most comprehensive view to date of storage effects on stool and provide a paradigm for the future studies of other sample types that will be required to provide a global view of microbial diversity and its interaction among humans, animals, and the environment.

IMPORTANCE Our study, spanning 15 individuals and over 1,200 samples, provides our most comprehensive view to date of storage and stabilization effects on stool. We tested five methods for preserving human and dog fecal specimens for periods of up to 8 weeks, including the types of variation often encountered under field conditions, such as freeze-thaw cycles and high temperature fluctuations. We show that several cost-effective methods provide excellent microbiome stability out to 8 weeks, opening up a range of field studies with humans and wildlife that would otherwise be cost-prohibitive.

Intricacies of assessing the human microbiome in epidemiologic studies

PURPOSE

In the past decade, remarkable relationships have been documented between dysbiosis of the human microbiota and adverse health outcomes. This review seeks to highlight some of the challenges and pitfalls that may be encountered during all stages of microbiota research, from study design and sample collection, to nucleic acid extraction and sequencing, and bioinformatic and statistical analysis.

METHODS

Literature focused on human microbiota research was reviewed and summarized.

RESULTS

Although most studies have focused on surveying the composition of the microbiota, fewer have explored the causal roles of these bacteria, archaea, viruses, and fungi in affecting disease states. Microbiome research is in its relatively early years and many aspects remain challenging, including the complexity and personalized aspects of microbial communities, the influence of exogenous and often confounding factors, the need to apply fundamental principles of ecology and epidemiology, the necessity for new software tools, and the rapidly evolving genomic, technological, and analytical landscapes.

CONCLUSIONS

Incorporating human microbiome research in large epidemiologic studies will soon help us unravel the intricate relationships that we have with our microbial partners and provide interventional opportunities to improve human health.

Characterization of the gut microbiome in epidemiologic studies: the multiethnic cohort experience

PURPOSE

The development of next-generation sequencing and accompanying bioinformatics tools has revolutionized characterization of microbial communities. As interest grows in the role of the human microbiome in health and disease, so does the need for well-powered, robustly designed epidemiologic studies. Here, we discuss sources of bias that can arise in gut microbiome research.

METHODS

Research comparing methods of specimen collection, preservation, processing, and analysis of gut microbiome samples is reviewed. Although selected studies are primarily based on the gut, many of the same principles are applicable to samples derived from other anatomical sites. Methods for participant recruitment and sampling of the gut microbiome implemented in an ongoing population-based study, the Multiethnic Cohort (MEC), are also described.

RESULTS

Variation in methodologies can influence the results of human microbiome studies. To help minimize bias, techniques such as sample homogenization, addition of internal standards, and quality filtering should be adopted in protocols. Within the MEC, participant response rates to stool sample collection were comparable to other studies, and in-home stool sample collection yields sufficient high-quality DNA for gut microbiome analysis.

CONCLUSIONS

Application of standardized and quality controlled methods in human microbiome studies is necessary to ensure data quality and comparability among studies.

Bifidobacteria Abundance-Featured Gut Microbiota Compositional Change in Patients with Behcet's Disease

Gut microbiota compositional alteration may have an association with immune dysfunction in patients with Behcet’s disease (BD). We conducted a fecal metagenomic analysis of BD patients. We analyzed fecal microbiota obtained from 12 patients with BD and 12 normal individuals by sequencing of 16S ribosomal RNA gene. We compared the relative abundance of bacterial taxa. Direct comparison of the relative abundance of bacterial taxa demonstrated that the genera Bifidobacterium and Eggerthella increased significantly and the genera Megamonas and Prevotella decreased significantly in BD patients compared with normal individuals. A linear discriminant analysis of bacterial taxa showed that the phylum Actinobacteria, including Bifidobacterium, and the family Lactobacillaceae exhibited larger positive effect sizes than other bacteria in patients with BD. The phylum Firmicutes and the class Clostridia had large effect sizes in normal individuals. There was no significant difference in annotated species numbers (as numbers of operational taxonomic unit; OTU) and bacterial diversity of each sample (alpha diversity) between BD patients and normal individuals. We next assigned each sample to a position using three axes by principal coordinates analysis of the OTU table. The two groups had a significant distance as beta diversity in the 3-axis space. Fecal sIgA concentrations increased significantly in BD patients but did not correlate with any bacterial taxonomic abundance. These data suggest that the compositional changes of gut microbes may be one type of dysbiosis (unfavorable microbiota alteration) in patients with BD. The dysbiosis may have an association with the pathophysiology of BD.

Use of Metatranscriptomics in Microbiome Research

The human intestinal microbiome is a microbial ecosystem that expresses as many as 100 times more genes than the human host, thereby constituting an important component of the human holobiome, which contributes to multiple health and disease processes. As most commensal species are difficult or impossible to culture, genomic characterization of microbiome composition and function, under various environmental conditions, comprises a central tool in understanding its roles in health and disease. The first decade of microbiome research was mainly characterized by usage of DNA sequencing-based 16S rDNA and shotgun metagenome sequencing, allowing for the elucidation of microbial composition and genome structure. Technological advances in RNA-seq have recently provided us with an ability to gain insight into the genes that are actively expressed in complex bacterial communities, enabling the elucidation of the functional changes that dictate the microbiome functions at given contexts, its interactions with the host, and functional alterations that accompany the conversion of a healthy microbiome toward a disease-driving configuration. Here, we highlight some of the key metatranscriptomics strategies that are implemented to determine microbiota gene expression and its regulation and discuss the advantages and potential challenges associated with these approaches.

Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics

The advent of next generation sequencing (NGS) has enabled investigations of the gut microbiome with unprecedented resolution and throughput. This has stimulated the development of sophisticated bioinformatics tools to analyze the massive amounts of data generated. Researchers therefore need a clear understanding of the key concepts required for the design, execution and interpretation of NGS experiments on microbiomes. We conducted a literature review and used our own data to determine which approaches work best. The two main approaches for analyzing the microbiome, 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics, are illustrated with analyses of libraries designed to highlight their strengths and weaknesses. Several methods for taxonomic classification of bacterial sequences are discussed. We present simulations to assess the number of sequences that are required to perform reliable appraisals of bacterial community structure. To the extent that fluctuations in the diversity of gut bacterial populations correlate with health and disease, we emphasize various techniques for the analysis of bacterial communities within samples (α-diversity) and between samples (β-diversity). Finally, we demonstrate techniques to infer the metabolic capabilities of a bacteria community from these 16S and shotgun data.

Faecal Metaproteomic Analysis Reveals a Personalized and Stable Functional Microbiome and Limited Effects of a Probiotic Intervention in Adults

Recent metagenomic studies have demonstrated that the overall functional potential of the intestinal microbiome is rather conserved between healthy individuals. Here we assessed the biological processes undertaken in-vivo by microbes and the host in the intestinal tract by conducting a metaproteome analysis from a total of 48 faecal samples of 16 healthy adults participating in a placebo-controlled probiotic intervention trial. Half of the subjects received placebo and the other half consumed Lactobacillus rhamnosus GG for three weeks (10¹⁰ cfu per day). Faecal samples were collected just before and at the end of the consumption phase as well as after a three-week follow-up period, and were processed for microbial composition and metaproteome analysis. A common core of shared microbial protein functions could be identified in all subjects. Furthermore, we observed marked differences in expressed proteins between subjects that resulted in the definition of a stable and personalized microbiome both at the mass-spectrometry-based proteome level and the functional level based on the KEGG pathway analysis. No significant changes in the metaproteome were attributable to the probiotic intervention. A detailed taxonomic assignment of peptides and comparison to phylogenetic microarray data made it possible to evaluate the activity of the main phyla as well as key species, including Faecalibacterium prausnitzii. Several correlations were identified between human and bacterial proteins. Proteins of the human host accounted for approximately 14% of the identified metaproteome and displayed variations both between and within individuals. The individually different human intestinal proteomes point to personalized host-microbiota interactions. Our findings indicate that analysis of the intestinal metaproteome can complement gene-based analysis and contributes to a thorough understanding of the activities of the microbiome and the relevant pathways in health and disease.

How to Predict Molecular Interactions between Species?

Organisms constantly interact with other species through physical contact which leads to changes on the molecular level, for example the transcriptome. These changes can be monitored for all genes, with the help of high-throughput experiments such as RNA-seq or microarrays. The adaptation of the gene expression to environmental changes within cells is mediated through complex gene regulatory networks. Often, our knowledge of these networks is incomplete. Network inference predicts gene regulatory interactions based on transcriptome data. An emerging application of high-throughput transcriptome studies are dual transcriptomics experiments. Here, the transcriptome of two or more interacting species is measured simultaneously. Based on a dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candida albicans, the software tool NetGenerator was applied to predict an inter-species gene regulatory network. To promote further investigations of molecular inter-species interactions, we recently discussed dual RNA-seq experiments for host-pathogen interactions and extended the applied tool NetGenerator (Schulze et al., 2015). The updated version of NetGenerator makes use of measurement variances in the algorithmic procedure and accepts gene expression time series data with missing values. Additionally, we tested multiple modeling scenarios regarding the stimuli functions of the gene regulatory network. Here, we summarize the work by Schulze et al. (2015) and put it into a broader context. We review various studies making use of the dual transcriptomics approach to investigate the molecular basis of interacting species. Besides the application to host-pathogen interactions, dual transcriptomics data are also utilized to study mutualistic and commensalistic interactions. Furthermore, we give a short introduction into additional approaches for the prediction of gene regulatory networks and discuss their application to dual transcriptomics data. We conclude that the application of network inference on dual-transcriptomics data is a promising approach to predict molecular inter-species interactions.

Defining the microbial transcriptional response to colitis through integrated host and microbiome profiling

The gut microbiome is significantly altered in inflammatory bowel diseases, but the basis of these changes is not well understood. We have combined metagenomic and metatranscriptomic profiling of the gut microbiome to assess modifications to both bacterial community structure and transcriptional activity in a mouse model of colitis. By using transcriptomic analysis of colonic tissue and luminal RNA derived from the host, we have also characterised how host transcription relates to the microbial transcriptional response in inflammation. In colitis, increased abundance and transcription of diverse microbial gene families involved in responses to nutrient deprivation, antimicrobial peptide production and oxidative stress support an adaptation of multiple commensal genera to withstand a diverse set of environmental stressors in the inflammatory environment. These data are supported by a transcriptional signature of activated macrophages and granulocytes in the gut lumen during colitis, a signature that includes the transcription of the key antimicrobial genes S100a8 and S100a9 (calprotectin). Genes involved in microbial resistance to oxidative stress, including Dps/ferritin, Fe-dependent peroxidase and glutathione S-transferase were identified as changing to a greater extent at the level of transcription than would be predicted by DNA abundance changes, implicating a role for increased oxygen tension and/or host-derived reactive oxygen species in driving transcriptional changes in commensal microbes.

Brain-gut-microbiota axis: challenges for translation in psychiatry

PURPOSE

The accruing data linking the gut microbiome to the development and function of the central nervous system has been proposed as a paradigm shift in neuroscience. The gut microbiota can communicate with the brain via neuroimmune, neuroendocrine, and neural pathways comprising the brain-gut-microbiota axis. Dysfunctional neuroimmune pathways are implicated in stress-related psychiatric disorders.

METHODS

Using depression as our primary example, we review both the preclinical and clinical evidence supporting the possible role played by the gut microbiota in stress-related psychiatric disorders. We consider how this can inform future treatment strategies and outline the challenges and necessary studies for moving the field forward.

RESULTS

The role played by the gut microbiota has not been fully elucidated in psychiatric populations. Although tempting to speculate that psychiatric patients may benefit from therapeutic modulation of the brain-gut-microbiota axis, the translational applications of the results obtained in rodent studies have yet to be demonstrated.

CONCLUSIONS

Evidence of altered gut microbiota composition and function in psychiatric patients is limited and cannot be regarded as proven. Moreover the efficacy of targeting the gut microbiota has not yet been established, and needs further investigation.

Oesophageal squamous cell carcinoma (ESCC): Advances through omics technologies, towards ESCC salivaomics

Oesophageal Squamous Cell Carcinoma (ESCC) is one of the two main subtypes of oesophageal cancer, affecting mainly populations in Asia. Though there have been great efforts to develop methods for a better prognosis, there is still a limitation in the staging of this affection. As a result, ESCC is detected at advances stages, when the interventions on the patient do not have such a positive outcome, leading in many cases to recurrence and to a very low 5-year survival rate, causing high mortality. A way to decrease the number of deaths is the use of biomarkers that can trace the advance of the disease at early stages, when surgical or chemotherapeutic methodologies would have a greater effect on the evolution of the subject. The new high throughput omics technologies offer an unprecedented chance to screen for thousands of molecules at the same time, from which a new set of biomarkers could be developed. One of the most convenient types of samples is saliva, an accessible body fluid that has the advantage of being non-invasive for the patient, being easy to store or to process. This review will focus on the current status of the new omics technologies regarding salivaomics in ESCC, or when not evaluated yet, the achievements in related diseases.

The human gut microbiome and its dysfunctions through the meta-omics prism

The microorganisms inhabiting the human gut are abundant (10(14) cells) and diverse (approximately 500 species per individual). It is now acknowledged that the microbiota has coevolved with its host to achieve a symbiotic relationship, leading to physiological homeostasis. The gut microbiota ensures vital functions, such as food digestibility, maturation of the host immune system, and protection against pathogens. Over the last few decades, the gut microbiota has also been associated with numerous diseases, such as inflammatory bowel disease, irritable bowel syndrome, obesity, and metabolic diseases. In most of these pathologies, a microbial dysbiosis has been found, indicating shifts in the taxonomic composition of the gut microbiota and changes in its functionality. Our understanding of the influence of the gut microbiota on human health is still growing. Working with microorganisms residing in the gut is challenging since most of them are anaerobic and a vast majority (approximately 75%) are uncultivable to date. Recently, a wide range of new approaches (meta-omics) has been developed to bypass the uncultivability and reveal the intricate mechanisms that sustain gut microbial homeostasis. After a brief description of these approaches (metagenomics, metatranscriptomics, metaproteomics, and metabolomics), this review will discuss the importance of considering the gut microbiome as a structured ecosystem and the use of meta-omics to decipher dysfunctions of the gut microbiome in diseases.

Characterization of Intestinal Microbiota in Ulcerative Colitis Patients with and without Primary Sclerosing Cholangitis

BACKGROUND AND AIMS

There is an unexplained association between ulcerative colitis [UC] and primary sclerosing cholangitis [PSC], with the intestinal microbiota implicated as an important factor. The study aim was to compare the structure of the intestinal microbiota of patients with UC with and without PSC.

METHODS

UC patients with PSC [PSC-UC] and without PSC [UC] were identified from biobanks at Oslo University Hospital, Foothills Hospital Calgary and Mount Sinai Hospital Toronto. Microbial DNA was extracted from colonic tissue and sequencing performed of the V4 region of the 16S rRNA gene on Illumina MiSeq. Sequences were assigned to operational taxonomic units [OTUs] using Quantitative Insights Into Microbial Ecology [QIIME]. Microbial alpha diversity, beta diversity, and relative abundance were compared between PSC-UC and UC phenotypes.

RESULTS

In all, 31 PSC-UC patients and 56 UC patients were included. Principal coordinate analysis [PCoA] demonstrated that city of sample collection was the strongest determinant of taxonomic profile. In the Oslo cohort, Chao 1 index was modestly decreased in PSC-UC compared with UC [p = 0.04] but did not differ significantly in the Calgary cohort. No clustering by PSC phenotype was observed using beta diversity measures. For multiple microbial genera there were nominally significant differences between UC and PSC-UC, but results were not robust to false-discovery rate correction.

CONCLUSIONS

No strong PSC-specific microbial associations in UC patients consistent across different cohorts were identified. Recruitment centre had a strong effect on microbial composition. Future studies should include larger cohorts to increase power and the ability to control for confounding factors.

The role of the intestinal microbiota in type 1 diabetes mellitus

Type 1 diabetes mellitus (T1DM) is a chronic immune-mediated disease with a subclinical prodromal period, characterized by selective loss of insulin-producing-β cells in the pancreatic islets of genetically susceptible individuals. The incidence of T1DM has increased several fold in most developed countries since World War II, in conjunction with other immune-mediated diseases. Rapid environmental changes and modern lifestyles are probably the driving factors that underlie this increase. These effects might be mediated by changes in the human microbiota, particularly the intestinal microbiota. Research on the gut microbiome of individuals at risk of developing T1DM and in patients with established disease is still in its infancy, but initial findings indicate that the intestinal microbiome of individuals with prediabetes or diabetes mellitus is different to that of healthy individuals. The gut microbiota in individuals with preclinical T1DM is characterized by Bacteroidetes dominating at the phylum level, a dearth of butyrate-producing bacteria, reduced bacterial and functional diversity and low community stability. However, these changes seem to emerge after the appearance of autoantibodies that are predictive of T1DM, which suggests that the intestinal microbiota might be involved in the progression from β-cell autoimmunity to clinical disease rather than in the initiation of the disease process.

Sub-clinical detection of gut microbial biomarkers of obesity and type 2 diabetes

Background

Obesity and type 2 diabetes (T2D) are linked both with host genetics and with environmental factors, including dysbioses of the gut microbiota. However, it is unclear whether these microbial changes precede disease onset. Twin cohorts present a unique genetically-controlled opportunity to study the relationships between lifestyle factors and the microbiome. In particular, we hypothesized that family-independent changes in microbial composition and metabolic function during the sub-clinical state of T2D could be either causal or early biomarkers of progression.

Methods

We collected fecal samples and clinical metadata from 20 monozygotic Korean twins at up to two time points, resulting in 36 stool shotgun metagenomes. While the participants were neither obese nor diabetic, they spanned the entire range of healthy to near-clinical values and thus enabled the study of microbial associations during sub-clinical disease while accounting for genetic background.

Results

We found changes both in composition and in function of the sub-clinical gut microbiome, including a decrease in Akkermansia muciniphila suggesting a role prior to the onset of disease, and functional changes reflecting a response to oxidative stress comparable to that previously observed in chronic T2D and inflammatory bowel diseases. Finally, our unique study design allowed us to examine the strain similarity between twins, and we found that twins demonstrate strain-level differences in composition despite species-level similarities.

Conclusions

These changes in the microbiome might be used for the early diagnosis of an inflamed gut and T2D prior to clinical onset of the disease and will help to advance toward microbial interventions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0271-6) contains supplementary material, which is available to authorized users.

Increasing Metagenomic Resolution of Microbiome Interactions Through Functional Phylogenomics and Bacterial Sub-Communities

The genomic composition of the microbiome and its relationship with the environment is an exciting open question in biology. Metagenomics is a useful tool in the discovery of previously unknown taxa, but its use to understand the functional and ecological capacities of the microbiome is limited until taxonomy and function are understood in the context of the community. We suggest that this can be achieved using a combined functional phylogenomics and co-culture-based experimental strategy that can increase our capacity to measure sub-community interactions. Functional phylogenomics can identify and partition the genome such that hidden gene functions and gene clusters with unique evolutionary signals are revealed. We can test these phylogenomic predictions using an experimental model based on sub-community populations that represent a subset of the diversity directly obtained from environmental samples. These populations increase the detection of mechanisms that drive functional forces in the assembly of the microbiome, in particular the role of metabolites from key taxa in community interactions. Our combined approach leverages the potential of metagenomics to address biological questions from ecological systems.

The Metagenome of Utricularia gibba's Traps: Into the Microbial Input to a Carnivorous Plant

The genome and transcriptome sequences of the aquatic, rootless, and carnivorous plant Utricularia gibba L. (Lentibulariaceae), were recently determined. Traps are necessary for U. gibba because they help the plant to survive in nutrient-deprived environments. The U. gibba's traps (Ugt) are specialized structures that have been proposed to selectively filter microbial inhabitants. To determine whether the traps indeed have a microbiome that differs, in composition or abundance, from the microbiome in the surrounding environment, we used whole-genome shotgun (WGS) metagenomics to describe both the taxonomic and functional diversity of the Ugt microbiome. We collected U. gibba plants from their natural habitat and directly sequenced the metagenome of the Ugt microbiome and its surrounding water. The total predicted number of species in the Ugt was more than 1,100. Using pan-genome fragment recruitment analysis, we were able to identify to the species level of some key Ugt players, such as Pseudomonas monteilii. Functional analysis of the Ugt metagenome suggests that the trap microbiome plays an important role in nutrient scavenging and assimilation while complementing the hydrolytic functions of the plant.

An evaluation of the accuracy and speed of metagenome analysis tools

Metagenome studies are becoming increasingly widespread, yielding important insights into microbial communities covering diverse environments from terrestrial and aquatic ecosystems to human skin and gut. With the advent of high-throughput sequencing platforms, the use of large scale shotgun sequencing approaches is now commonplace. However, a thorough independent benchmark comparing state-of-the-art metagenome analysis tools is lacking. Here, we present a benchmark where the most widely used tools are tested on complex, realistic data sets. Our results clearly show that the most widely used tools are not necessarily the most accurate, that the most accurate tool is not necessarily the most time consuming, and that there is a high degree of variability between available tools. These findings are important as the conclusions of any metagenomics study are affected by errors in the predicted community composition and functional capacity. Data sets and results are freely available from http://www.ucbioinformatics.org/metabenchmark.html

The bovine milk microbiota: insights and perspectives from -omics studies

Recent findings and future perspectives of -omics studies on the bovine milk microbiota, focusing on its impact on animal health.