Melanie Schirmer

Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity.

“Normal” for the gut microbiota For the benefit of future clinical studies, it is critical to establish what constitutes a “normal” gut microbiome, if it exists at all. Through fecal samples and questionnaires, Falony et al. and Zhernakova et al. targeted general populations in Belgium and the Netherlands, respectively. Gut microbiota composition correlated with a range of factors including diet, use of medication, red blood cell counts, fecal chromogranin A, and stool consistency. The data give some hints for possible biomarkers of normal gut communities. Science, this issue pp. 560 and 565 Two large-scale studies in Western Europe establish environment-diet-microbe-host interactions. Deep sequencing of the gut microbiomes of 1135 participants from a Dutch population-based cohort shows relations between the microbiome and 126 exogenous and intrinsic host factors, including 31 intrinsic factors, 12 diseases, 19 drug groups, 4 smoking categories, and 60 dietary factors. These factors collectively explain 18.7% of the variation seen in the interindividual distance of microbial composition. We could associate 110 factors to 125 species and observed that fecal chromogranin A (CgA), a protein secreted by enteroendocrine cells, was exclusively associated with 61 microbial species whose abundance collectively accounted for 53% of microbial composition. Low CgA concentrations were seen in individuals with a more diverse microbiome. These results are an important step toward a better understanding of environment-diet-microbe-host interactions.

Binning metagenomic contigs by coverage and composition

Shotgun sequencing enables the reconstruction of genomes from complex microbial communities, but because assembly does not reconstruct entire genomes, it is necessary to bin genome fragments. Here we present CONCOCT, a new algorithm that combines sequence composition and coverage across multiple samples, to automatically cluster contigs into genomes. We demonstrate high recall and precision on artificial as well as real human gut metagenome data sets.

Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform

With read lengths of currently up to 2 × 300 bp, high throughput and low sequencing costs Illuminas MiSeq is becoming one of the most utilized sequencing platforms worldwide. The platform is manageable and affordable even for smaller labs. This enables quick turnaround on a broad range of applications such as targeted gene sequencing, metagenomics, small genome sequencing and clinical molecular diagnostics. However, Illumina error profiles are still poorly understood and programs are therefore not designed for the idiosyncrasies of Illumina data. A better knowledge of the error patterns is essential for sequence analysis and vital if we are to draw valid conclusions. Studying true genetic variation in a population sample is fundamental for understanding diseases, evolution and origin. We conducted a large study on the error patterns for the MiSeq based on 16S rRNA amplicon sequencing data. We tested state-of-the-art library preparation methods for amplicon sequencing and showed that the library preparation method and the choice of primers are the most significant sources of bias and cause distinct error patterns. Furthermore we tested the efficiency of various error correction strategies and identified quality trimming (Sickle) combined with error correction (BayesHammer) followed by read overlapping (PANDAseq) as the most successful approach, reducing substitution error rates on average by 93%.

The effect of host genetics on the gut microbiome

The gut microbiome is affected by multiple factors, including genetics. In this study, we assessed the influence of host genetics on microbial species, pathways and gene ontology categories, on the basis of metagenomic sequencing in 1,514 subjects. In a genome-wide analysis, we identified associations of 9 loci with microbial taxonomies and 33 loci with microbial pathways and gene ontology terms at P < 5 × 10−8. Additionally, in a targeted analysis of regions involved in complex diseases, innate and adaptive immunity, or food preferences, 32 loci were identified at the suggestive level of P < 5 × 10−6. Most of our reported associations are new, including genome-wide significance for the C-type lectin molecules CLEC4F–CD207 at 2p13.3 and CLEC4A–FAM90A1 at 12p13. We also identified association of a functional LCT SNP with the Bifidobacterium genus (P = 3.45 × 10−8) and provide evidence of a gene–diet interaction in the regulation of Bifidobacterium abundance. Our results demonstrate the importance of understanding host–microbe interactions to gain better insight into human health.

Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity

Gut microbial dysbioses are linked to aberrant immune responses, which are often accompanied by abnormal production of inflammatory cytokines. As part of the Human Functional Genomics Project (HFGP), we investigate how differences in composition and function of gut microbial communities may contribute to inter-individual variation in cytokine responses to microbial stimulations in healthy humans. We observe microbiome-cytokine interaction patterns that are stimulus specific, cytokine specific, and cytokine and stimulus specific. Validation of two predicted host-microbial interactions reveal that TNFα and IFNγ production are associated with specific microbial metabolic pathways: palmitoleic acid metabolism and tryptophan degradation to tryptophol. Besides providing a resource of predicted microbially derived mediators that influence immune phenotypes in response to common microorganisms, these data can help to define principles for understanding disease susceptibility. The three HFGP studies presented in this issue lay the groundwork for further studies aimed at understanding the interplay between microbial, genetic, and environmental factors in the regulation of the immune response in humans. PAPERCLIP.

A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling

BackgroundIn the last 5 years, the rapid pace of innovations and improvements in sequencing technologies has completely changed the landscape of metagenomic and metagenetic experiments. Therefore, it is critical to benchmark the various methodologies for interrogating the composition of microbial communities, so that we can assess their strengths and limitations. The most common phylogenetic marker for microbial community diversity studies is the 16S ribosomal RNA gene and in the last 10 years the field has moved from sequencing a small number of amplicons and samples to more complex studies where thousands of samples and multiple different gene regions are interrogated.ResultsWe assembled 2 synthetic communities with an even (EM) and uneven (UM) distribution of archaeal and bacterial strains and species, as metagenomic control material, to assess performance of different experimental strategies. The 2 synthetic communities were used in this study, to highlight the limitations and the advantages of the leading sequencing platforms: MiSeq (Illumina), The Pacific Biosciences RSII, 454 GS-FLX/+ (Roche), and IonTorrent (Life Technologies). We describe an extensive survey based on synthetic communities using 3 experimental designs (fusion primers, universal tailed tag, ligated adaptors) across the 9 hypervariable 16S rDNA regions. We demonstrate that library preparation methodology can affect data interpretation due to different error and chimera rates generated during the procedure. The observed community composition was always biased, to a degree that depended on the platform, sequenced region and primer choice. However, crucially, our analysis suggests that 16S rRNA sequencing is still quantitative, in that relative changes in abundance of taxa between samples can be recovered, despite these biases.ConclusionWe have assessed a range of experimental conditions across several next generation sequencing platforms using the most up-to-date configurations. We propose that the choice of sequencing platform and experimental design needs to be taken into consideration in the early stage of a project by running a small trial consisting of several hypervariable regions to quantify the discriminatory power of each region. We also suggest that the use of a synthetic community as a positive control would be beneficial to identify the potential biases and procedural drawbacks that may lead to data misinterpretation. The results of this study will serve as a guideline for making decisions on which experimental condition and sequencing platform to consider to achieve the best microbial profiling.

Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data

BackgroundIllumina’s sequencing platforms are currently the most utilised sequencing systems worldwide. The technology has rapidly evolved over recent years and provides high throughput at low costs with increasing read-lengths and true paired-end reads. However, data from any sequencing technology contains noise and our understanding of the peculiarities and sequencing errors encountered in Illumina data has lagged behind this rapid development.ResultsWe conducted a systematic investigation of errors and biases in Illumina data based on the largest collection of in vitro metagenomic data sets to date. We evaluated the Genome Analyzer II, HiSeq and MiSeq and tested state-of-the-art low input library preparation methods. Analysing in vitro metagenomic sequencing data allowed us to determine biases directly associated with the actual sequencing process. The position- and nucleotide-specific analysis revealed a substantial bias related to motifs (3mers preceding errors) ending in “GG”. On average the top three motifs were linked to 16 % of all substitution errors. Furthermore, a preferential incorporation of ddGTPs was recorded. We hypothesise that all of these biases are related to the engineered polymerase and ddNTPs which are intrinsic to any sequencing-by-synthesis method. We show that quality-score-based error removal strategies can on average remove 69 % of the substitution errors - however, the motif-bias remains.ConclusionSingle-nucleotide polymorphism changes in bacterial genomes can cause significant changes in phenotype, including antibiotic resistance and virulence, detecting them within metagenomes is therefore vital. Current error removal techniques are not designed to target the peculiarities encountered in Illumina sequencing data and other sequencing-by-synthesis methods, causing biases to persist and potentially affect any conclusions drawn from the data. In order to develop effective diagnostic and therapeutic approaches we need to be able to identify systematic sequencing errors and distinguish these errors from true genetic variation.

Prediction of complicated disease course for children newly diagnosed with Crohn's disease: a multicentre inception cohort study

BACKGROUND Stricturing and penetrating complications account for substantial morbidity and health-care costs in paediatric and adult onset Crohns disease. Validated models to predict risk for complications are not available, and the effect of treatment on risk is unknown. METHODS We did a prospective inception cohort study of paediatric patients with newly diagnosed Crohns disease at 28 sites in the USA and Canada. Genotypes, antimicrobial serologies, ileal gene expression, and ileal, rectal, and faecal microbiota were assessed. A competing-risk model for disease complications was derived and validated in independent groups. Propensity-score matching tested the effect of anti-tumour necrosis factor α (TNFα) therapy exposure within 90 days of diagnosis on complication risk. FINDINGS Between Nov 1, 2008, and June 30, 2012, we enrolled 913 patients, 78 (9%) of whom experienced Crohns disease complications. The validated competing-risk model included age, race, disease location, and antimicrobial serologies and provided a sensitivity of 66% (95% CI 51-82) and specificity of 63% (55-71), with a negative predictive value of 95% (94-97). Patients who received early anti-TNFα therapy were less likely to have penetrating complications (hazard ratio [HR] 0·30, 95% CI 0·10-0·89; p=0·0296) but not stricturing complication (1·13, 0·51-2·51; 0·76) than were those who did not receive early anti-TNFα therapy. Ruminococcus was implicated in stricturing complications and Veillonella in penetrating complications. Ileal genes controlling extracellular matrix production were upregulated at diagnosis, and this gene signature was associated with stricturing in the risk model (HR 1·70, 95% CI 1·12-2·57; p=0·0120). When this gene signature was included, the models specificity improved to 71%. INTERPRETATION Our findings support the usefulness of risk stratification of paediatric patients with Crohns disease at diagnosis, and selection of anti-TNFα therapy. FUNDING Crohns and Colitis Foundation of America, Cincinnati Childrens Hospital Research Foundation Digestive Health Center.

Benchmarking of viral haplotype reconstruction programmes: an overview of the capacities and limitations of currently available programmes

Viral haplotype reconstruction from a set of observed reads is one of the most challenging problems in bioinformatics today. Next-generation sequencing technologies enable us to detect single-nucleotide polymorphisms (SNPs) of haplotypes-even if the haplotypes appear at low frequencies. However, there are two major problems. First, we need to distinguish real SNPs from sequencing errors. Second, we need to determine which SNPs occur on the same haplotype, which cannot be inferred from the reads if the distance between SNPs on a haplotype exceeds the read length. We conducted an independent benchmarking study that directly compares the currently available viral haplotype reconstruction programmes. We also present nine in silico data sets that we generated to reflect biologically plausible populations. For these data sets, we simulated 454 and Illumina reads and applied the programmes to test their capacity to reconstruct whole genomes and individual genes. We developed a novel statistical framework to demonstrate the strengths and limitations of the programmes. Our benchmarking demonstrated that all the programmes we tested performed poorly when sequence divergence was low and failed to recover haplotype populations with rare haplotypes.

The kinase DYRK1A reciprocally regulates the differentiation of Th17 and regulatory T cells

The balance between Th17 and T regulatory (Treg) cells critically modulates immune homeostasis, with an inadequate Treg response contributing to inflammatory disease. Using an unbiased chemical biology approach, we identified a novel role for the dual specificity tyrosine-phosphorylation-regulated kinase DYRK1A in regulating this balance. Inhibition of DYRK1A enhances Treg differentiation and impairs Th17 differentiation without affecting known pathways of Treg/Th17 differentiation. Thus, DYRK1A represents a novel mechanistic node at the branch point between commitment to either Treg or Th17 lineages. Importantly, both Treg cells generated using the DYRK1A inhibitor harmine and direct administration of harmine itself potently attenuate inflammation in multiple experimental models of systemic autoimmunity and mucosal inflammation. Our results identify DYRK1A as a physiologically relevant regulator of Treg cell differentiation and suggest a broader role for other DYRK family members in immune homeostasis. These results are discussed in the context of human diseases associated with dysregulated DYRK activity. DOI: http://dx.doi.org/10.7554/eLife.05920.001