A physicians' wish list for the clinical application of intestinal metagenomics

Plasmid DNA contaminant in molecular reagents

Background noise in metagenomic studies is often of high importance and its removal requires extensive post-analytic, bioinformatics filtering. This is relevant as significant signals may be lost due to a low signal-to-noise ratio. The presence of plasmid residues, that are frequently present in reagents as contaminants, has not been investigated so far, but may pose a substantial bias. Here we show that plasmid sequences from different sources are omnipresent in molecular biology reagents. Using a metagenomic approach, we identified the presence of the (pol) of equine infectious anemia virus in human samples and traced it back to the expression plasmid used for generation of a commercial reverse transcriptase. We found fragments of multiple other expression plasmids in human samples as well as commercial polymerase preparations. Plasmid contamination sources included production chain of molecular biology reagents as well as contamination of reagents from environment or human handling of samples and reagents. Retrospective analyses of published metagenomic studies revealed an inaccurate signal-to-noise differentiation. Hence, the plasmid sequences that seem to be omnipresent in molecular biology reagents may misguide conclusions derived from genomic/metagenomics datasets and thus also clinical interpretations. Critical appraisal of metagenomic data sets for the possibility of plasmid background noise is required to identify reliable and significant signals.

16S Based Microbiome Analysis from Healthy Subjects' Skin Swabs Stored for Different Storage Periods Reveal Phylum to Genus Level Changes

Microbiome research and improvements in high throughput sequencing technologies revolutionize our current scientific viewpoint. The human associated microbiome is a prominent focus of clinical research. Large cohort studies are often required to investigate the human microbiome composition and its changes in a multitude of human diseases. Reproducible analyses of large cohort samples require standardized protocols in study design, sampling, storage, processing, and data analysis. In particular, the effect of sample storage on actual results is critical for reproducibility. So far, the effect of storage conditions on the results of microbial analysis has been examined for only a few human biological materials (e.g., stool samples). There is a lack of data and information on appropriate storage conditions on other human derived samples, such as skin. Here, we analyzed skin swab samples collected from three different body locations (forearm, V of the chest and back) of eight healthy volunteers. The skin swabs were soaked in sterile buffer and total DNA was isolated after freezing at -80°C for 24 h, 90 or 365 days. Hypervariable regions V1-2 were amplified from total DNA and libraries were sequenced on an Illumina MiSeq desktop sequencer in paired end mode. Data were analyzed using Qiime 1.9.1. Summarizing all body locations per time point, we found no significant differences in alpha diversity and multivariate community analysis among the three time points. Considering body locations separately significant differences in the richness of forearm samples were found between d0 vs. d90 and d90 vs. d365. Significant differences in the relative abundance of major skin genera (Propionibacterium, Streptococcus, Bacteroides, Corynebacterium, and Staphylococcus) were detected in our samples in Bacteroides only among all time points in forearm samples and between d0 vs. d90 and d90 vs. d365 in V of the chest and back samples. Accordingly, significant differences were detected in the ratios of the main phyla Actinobacteria, Firmicutes, and Bacteroidetes: Actinobacteria vs. Bacteroidetes at d0 vs. d90 (p-value = 0.0234), at d0 vs. d365 (p-value = 0.0234) and d90 vs. d365 (p-value = 0.0234) in forearm samples and at d90 vs. d365 in V of the chest (p-value = 0.0234) and back samples (p-value = 0.0234). The ratios of Firmicutes vs. Bacteroidetes showed no significant changes in any of the body locations as well as the ratios of Actinobacteria vs. Firmicutes at any time point. Studies with larger sample sizes are required to verify our results and determine long term storage effects with regard to specific biological questions.

Other side of the coin for personalised medicine and healthcare: content analysis of 'personalised' practices in the literature

OBJECTIVES

Various terms and definitions are used to describe personalised approaches to medicine and healthcare, but in ambiguous and inconsistent ways. They mostly have been defined in a top-down manner. However, actual practices might take different paths. Here, we aimed to provide a 'practice-based' perspective on the debate by analysing the content of 'personalised' practices published in the literature.

METHODS

The search in PubMed and EMBASE (April 2014) using the terms frequently used for personalised approaches resulted in 5333 records. 2 independent researchers used different strategies for screening, resulting in 157 articles describing 88 'personalised' practices that were implemented/presented on at least 1 individual/patient case. The content analysis was grounded on these data and did not have a priori analytical frameworks.

RESULTS

'Personalised medicine/healthcare' can be a commodity in the healthcare market, a way how health services are provided, or a keyword for emerging applications. It can help individuals/patients to gain control of their health, health professionals to provide better services, healthcare organisations to increase effectiveness and efficiency, or national health systems to increase performance. Country examples indicated that for integration of practices into health services, attitude towards innovations and health system and policy context is important. Categorisation based on the terms or the technologies used, if any, was not possible.

CONCLUSIONS

This study is the first to provide a comprehensive content analysis of the 'personalised' practices in the literature. Unlike the top-down definitions, our findings highlighted not the technologies but real-life issues faced by the practices. 'Personalised medicine' and 'personalised healthcare' can be differentiated by using the former for specific tools available and the latter for health services with a holistic approach, implemented in certain contexts. To realise integration of 'personalised medicine/healthcare' into real life, science, technology, health policy and practice, and society domains must work together.

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.

DUDes: a top-down taxonomic profiler for metagenomics

MOTIVATION

Species identification and quantification are common tasks in metagenomics and pathogen detection studies. The most recent techniques are built on mapping the sequenced reads against a reference database (e.g. whole genomes, marker genes, proteins) followed by application-dependent analysis steps. Although these methods have been proven to be useful in many scenarios, there is still room for improvement in species and strain level detection, mainly for low abundant organisms.

RESULTS

We propose a new method: DUDes, a reference-based taxonomic profiler that introduces a novel top-down approach to analyze metagenomic Next-generation sequencing (NGS) samples. Rather than predicting an organism presence in the sample based only on relative abundances, DUDes first identifies possible candidates by comparing the strength of the read mapping in each node of the taxonomic tree in an iterative manner. Instead of using the lowest common ancestor we propose a new approach: the deepest uncommon descendent. We showed in experiments that DUDes works for single and multiple organisms and can identify low abundant taxonomic groups with high precision.

AVAILABILITY AND IMPLEMENTATION

DUDes is open source and it is available at http://sf.net/p/dudes

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CONTACT

renardB@rki.de.

Gut Microbiota Dysbiosis as Risk and Premorbid Factors of IBD and IBS Along the Childhood-Adulthood Transition

Gastrointestinal disorders, although clinically heterogeneous, share pathogenic mechanisms, including genetic susceptibility, impaired gut barrier function, altered microbiota, and environmental triggers (infections, social and behavioral factors, epigenetic control, and diet). Gut microbiota has been studied for inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) in either children or adults, while modifiable gut microbiota features, acting as risk and premorbid factors along the childhood-adulthood transition, have not been thoroughly investigated so far. Indeed, the relationship between variations of the entire host/microbiota/environmental scenario and clinical phenotypes is still not fully understood. In this respect, tracking gut dysbiosis grading may help deciphering host phenotype-genotype associations and microbiota shifts in an integrated top-down omics-based approach within large-scale pediatric and adult case-control cohorts. Large-scale gut microbiota signatures and host inflammation patterns may be integrated with dietary habits, under genetic and epigenetic constraints, providing gut dysbiosis profiles acting as risk predictors of IBD or IBS in preclinical cases. Tracking dysbiosis supports new personalized/stratified IBD and IBS prevention programmes, generating Decision Support System tools. They include (1) high risk or flare-up recurrence -omics-based dysbiosis profiles; (2) microbial and molecular biomarkers of health and disease; (3) -omics-based pipelines for laboratory medicine diagnostics; (4) health apps for self-management of score-based dietary profiles, which can be shared with clinicians for nutritional habit and lifestyle amendment; (5) -omics profiling data warehousing and public repositories for IBD and IBS profile consultation. Dysbiosis-related indexes can represent novel laboratory and clinical medicine tools preventing or postponing the disease, finally interfering with its natural history.

Using populations of human and microbial genomes for organism detection in metagenomes

Identifying causative disease agents in human patients from shotgun metagenomic sequencing (SMS) presents a powerful tool to apply when other targeted diagnostics fail. Numerous technical challenges remain, however, before SMS can move beyond the role of research tool. Accurately separating the known and unknown organism content remains difficult, particularly when SMS is applied as a last resort. The true amount of human DNA that remains in a sample after screening against the human reference genome and filtering nonbiological components left from library preparation has previously been underreported. In this study, we create the most comprehensive collection of microbial and reference-free human genetic variation available in a database optimized for efficient metagenomic search by extracting sequences from GenBank and the 1000 Genomes Project. The results reveal new human sequences found in individual Human Microbiome Project (HMP) samples. Individual samples contain up to 95% human sequence, and 4% of the individual HMP samples contain 10% or more human reads. Left unidentified, human reads can complicate and slow down further analysis and lead to inaccurately labeled microbial taxa and ultimately lead to privacy concerns as more human genome data is collected.