Assessment of quality control approaches for metagenomic data analysis

Effects of marine sediment as agricultural substrate on soil microbial diversity: an amplicon sequencing study

BACKGROUND

The soil microbiota has a direct impact on plant development and other metabolic systems, such as the degradation of organic matter and the availability of microelements and metabolites. In the context of agricultural soils, microbial activity is crucial for maintaining soil health and productivity. Thus, the present study aimed to identify, characterize, and quantify the microbial communities of four types of substrates with varying proportions of marine port sediment used for cultivating lemons. By investigating microbial diversity and relative abundance, the work aimed to highlight the importance of soil microbial communities in agriculture when alternative culture media was used.

RESULTS

The composition and structure of the sampled microbial communities were assessed through the amplification and sequencing of the V3-V4 variable regions of the 16 S rRNA gene The results revealed a diverse microbial community composition in all substrate samples, with a total of 41 phyla, 113 classes, 266 orders, 405 families, 715 genera, and 1513 species identified. Among these, Proteobacteria, Bacteroidota, Planctomycetota, Patescibacteria, Chloroflexi, Actinobacteriota, Acidobacteriota, Verrucomicrobiota, and Gemmatimonadota accounted for over 90% of the bacterial reads, indicating their dominance in the substrates.

CONCLUSIONS

The impact of the substrate origin on the diversity and relative abundace of the microbiota was confirmed. The higher content of beneficial bacterial communities for plant development identified in peat could explain why is considered an ideal agricultural substrate. Development of "beneficial for plants" bacterial communities in alternative agricultural substrates, regardless of the edaphic characteristics, opens the possibility of studying the forced and specific inoculation of these culture media aiming to be agriculturally ideals.

An efficient method for high molecular weight bacterial DNA extraction suitable for shotgun metagenomics from skin swabs

The human skin microbiome represents a variety of complex microbial ecosystems that play a key role in host health. Molecular methods to study these communities have been developed but have been largely limited to low-throughput quantification and short amplicon-based sequencing, providing limited functional information about the communities present. Shotgun metagenomic sequencing has emerged as a preferred method for microbiome studies as it provides more comprehensive information about the species/strains present in a niche and the genes they encode. However, the relatively low bacterial biomass of skin, in comparison to other areas such as the gut microbiome, makes obtaining sufficient DNA for shotgun metagenomic sequencing challenging. Here we describe an optimised high-throughput method for extraction of high molecular weight DNA suitable for shotgun metagenomic sequencing. We validated the performance of the extraction method, and analysis pipeline on skin swabs collected from both adults and babies. The pipeline effectively characterised the bacterial skin microbiota with a cost and throughput suitable for larger longitudinal sets of samples. Application of this method will allow greater insights into community compositions and functional capabilities of the skin microbiome.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

Metataxonomic sequencing to assess microbial safety of Turkish white cheeses

High-throughput sequencing has provided a way to monitor the large diversity of microorganisms in fermented foods that have complex microbiota. Up to date, many kinds of cheese have been characterized with the metataxonomic approach, but the safety of unpacked Turkish white cheeses, which are widely consumed in Turkey, has not been assessed. In this study, fifteen unpacked white cheeses sold in public bazaars in Ankara province have been collected and subjected to microbial enumeration as well as physicochemical analysis. Five white cheeses, which have relatively the highest foodborne pathogens, out of fifteen white cheeses, have been analyzed by next-generation sequencing and metataxonomic analysis. According to the results, abundant families were Lactobacillaceae, Oceanospirillaceae, Enterococcaceae, Pseudomonadaceae, and Vibrionaceae. Staphylococcus aureus, E. coli, and Salmonella, which are indicators of bad hygiene and sanitation conditions, were found in cheeses. In conclusion, culture-independent methods such as metataxonomic can be important to evaluate the safety of foods.

Metagenomics Approaches for Improving Food Safety: A Review

ABSTRACT

Advancements in next-generation sequencing technology have dramatically reduced the cost and increased the ease of microbial whole genome sequencing. This approach is revolutionizing the identification and analysis of foodborne microbial pathogens, facilitating expedited detection and mitigation of foodborne outbreaks, improving public health outcomes, and limiting costly recalls. However, next-generation sequencing is still anchored in the traditional laboratory practice of the selection and culture of a single isolate. Metagenomic-based approaches, including metabarcoding and shotgun and long-read metagenomics, are part of the next disruptive revolution in food safety diagnostics and offer the potential to directly identify entire microbial communities in a single food, ingredient, or environmental sample. In this review, metagenomic-based approaches are introduced and placed within the context of conventional detection and diagnostic techniques, and essential considerations for undertaking metagenomic assays and data analysis are described. Recent applications of the use of metagenomics for food safety are discussed alongside current limitations and knowledge gaps and new opportunities arising from the use of this technology.

HIGHLIGHTS

Comparative study of microbial structure and functional profile of sunflower rhizosphere grown in two fields

Background

Microbial communities inhabiting the rhizosphere play pivotal roles in determining plant health and yield. Manipulation of the rhizosphere microbial community is a promising means to enhance the productivity of economically viable and important agricultural crops such as sunflower (Helianthus annuus). This study was designed to gain insights into the taxonomic and functional structures of sunflower rhizosphere and bulk soil microbiome at two different locations (Sheila and Itsoseng) in South Africa.

Results

Microbial DNA extracted from the sunflower rhizosphere and bulk soils was subjected to next-generation sequencing using 16S amplicon sequencing technique. Firmicutes, Actnobacteria and Proteobacteria predominated sunflower rhizosphere soils. Firmicutes, Cyanobacteria, Deinococcus-Thermus and Fibrobacteres were positively influenced by Na⁺ and clay content, while Actinobacteria, Thaumarchaeota, Bacteroidetes, Planctomycetes, Aquificae and Chloroflexi were positively influenced by soil resistivity (Res) and Mg²⁺. The community-level physiological profiling (CLPP) analysis showed that the microbial communities in SHR and ITR used the amino acids tryptophan and malic acid efficiently. The metabolisms of these carbon substrates may be due to the dominant nature of some of the organisms, such as Actinobacteria in the soils.

Conclusion

The CLPP measurements of soil from sunflower rhizosphere were different from those of the bulk soil and the degree of the variations were based on the type of carbon substrates and the soil microbial composition. This study has shown the presence of certain taxa of rhizobacteria in sunflower rhizosphere which were positively influenced by Na⁺ and Mg²⁺, and taxa obtained from SHR and ITR were able to effectively utilized tryptophan and malic acid. Many unclassified microbial groups were also discovered and it is therefore recommended that efforts should further be made to isolate, characterize and identify these unclassified microbial species, as it might be plausible to discover new microbial candidates that can further be harnessed for biotechnological purpose.

The endophytome (plant-associated microbiome): methodological approaches, biological aspects, and biotech applications

Similar to other organisms, plants establish interactions with a variety of microorganisms in their natural environment. The plant microbiome occupies the host plant's tissues, either internally or on its surfaces, showing interactions that can assist in its growth, development, and adaptation to face environmental stresses. The advance of metagenomics and metatranscriptomics approaches has strongly driven the study and recognition of plant microbiome impacts. Research in this regard provides comprehensive information about the taxonomic and functional aspects of microbial plant communities, contributing to a better understanding of their dynamics. Evidence of the plant microbiome's functional potential has boosted its exploitation to develop more ecological and sustainable agricultural practices that impact human health. Although microbial inoculants' development and use are promising to revolutionize crop production, interdisciplinary studies are needed to identify new candidates and promote effective practical applications. On the other hand, there are challenges in understanding and analyzing complex data generated within a plant microbiome project's scope. This review presents aspects about the complex structuring and assembly of the microbiome in the host plant's tissues, metagenomics, and metatranscriptomics approaches for its understanding, covering descriptions of recent studies concerning metagenomics to characterize the microbiome of non-model plants under different aspects. Studies involving bio-inoculants, isolated from plant microbial communities, capable of assisting in crops' productivity, are also reviewed.

An Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies

The gut microbiome is a microbial ecosystem which expresses 100 times more genes than the human host and plays an essential role in human health and disease pathogenesis. Since most intestinal microbial species are difficult to culture, next generation sequencing technologies have been widely applied to study the gut microbiome, including 16S rRNA, 18S rRNA, internal transcribed spacer (ITS) sequencing, shotgun metagenomic sequencing, metatranscriptomic sequencing and viromic sequencing. Various software tools were developed to analyze different sequencing data. In this review, we summarize commonly used computational tools for gut microbiome data analysis, which extended our understanding of the gut microbiome in health and diseases.

Metagenomics survey unravels diversity of biogas microbiomes with potential to enhance productivity in Kenya

The obstacle to optimal utilization of biogas technology is poor understanding of biogas microbiomes diversities over a wide geographical coverage. We performed random shotgun sequencing on twelve environmental samples. Randomized complete block design was utilized to assign the twelve treatments to four blocks, within eastern and central regions of Kenya. We obtained 42 million paired-end reads that were annotated against sixteen reference databases using two ENVO ontologies, prior to β-diversity studies. We identified 37 phyla, 65 classes and 132 orders. Bacteria dominated and comprised 28 phyla, 42 classes and 92 orders, conveying substrate's versatility in the treatments. Though, Fungi and Archaea comprised 5 phyla, the Fungi were richer; suggesting the importance of hydrolysis and fermentation in biogas production. High β-diversity within the taxa was largely linked to communities' metabolic capabilities. Clostridiales and Bacteroidales, the most prevalent guilds, metabolize organic macromolecules. The identified Cytophagales, Alteromonadales, Flavobacteriales, Fusobacteriales, Deferribacterales, Elusimicrobiales, Chlamydiales, Synergistales to mention but few, also catabolize macromolecules into smaller substrates to conserve energy. Furthermore, δ-Proteobacteria, Gloeobacteria and Clostridia affiliates syntrophically regulate PH2 and reduce metal to provide reducing equivalents. Methanomicrobiales and other Methanomicrobia species were the most prevalence Archaea, converting formate, CO2(g), acetate and methylated substrates into CH4(g). Thermococci, Thermoplasmata and Thermoprotei were among the sulfur and other metal reducing Archaea that contributed to redox balancing and other metabolism within treatments. Eukaryotes, mainly fungi were the least abundant guild, comprising largely Ascomycota and Basidiomycota species. Chytridiomycetes, Blastocladiomycetes and Mortierellomycetes were among the rare species, suggesting their metabolic and substrates limitations. Generally, we observed that environmental and treatment perturbations influenced communities' abundance, β-diversity and reactor performance largely through stochastic effect. Understanding diversity of biogas microbiomes over wide environmental variables and its' productivity provided insights into better management strategies that ameliorate biochemical limitations to effective biogas production.

Method development for cross-study microbiome data mining: Challenges and opportunities

During the past decade, tremendous amount of microbiome sequencing data has been generated to study on the dynamic associations between microbial profiles and environments. How to precisely and efficiently decipher large-scale of microbiome data and furtherly take advantages from it has become one of the most essential bottlenecks for microbiome research at present. In this mini-review, we focus on the three key steps of analyzing cross-study microbiome datasets, including microbiome profiling, data integrating and data mining. By introducing the current bioinformatics approaches and discussing their limitations, we prospect the opportunities in development of computational methods for the three steps, and propose the promising solutions to multi-omics data analysis for comprehensive understanding and rapid investigation of microbiome from different angles, which could potentially promote the data-driven research by providing a broader view of the "microbiome data space".

Elimination of "kitome" and "splashome" contamination results in lack of detection of a unique placental microbiome

Background

A placental microbiome, which may be altered in gestational diabetes mellitus (GDM), has been described. However, publications raising doubts about the existence of a placental microbiome that is different than contaminants in DNA extraction kits and reagents (“kitomes”) have emerged. The aims of this study were to confirm the existence of a placental microbiome distinct from contaminants and determine if it is altered in GDM mothers.

Results

We first enrolled normal weight, obese and GDM mothers (N = 17) at term elective cesarean section delivery in a pilot case control study. Bacterial DNA was extracted from placental parenchyma, maternal and cord blood, maternal vaginal-rectal swabs, and positive and negative controls with the standard Qiagen/MoBio Power Soil kit. Placentas had significantly higher copies of bacterial 16S rRNA genes than negative controls, but the placental microbiome was similar in all three groups and could not be distinguished from contaminants in blank controls. To determine the source and composition of the putative placental bacterial community identified in the pilot study, we expanded the study to 10 subjects per group (N = 30) and increased the number and variety of negative controls (N = 53). We modified our protocol to use an ultraclean DNA extraction kit (Qiagen QIAamp UCP with Pathogen Lysis Tube S), which reduced the “kitome” contamination, but we were still unable to distinguish a placental microbiome from contaminants in negative controls. We noted microbial DNA from the high biomass vaginal-rectal swabs and positive controls in placental and negative control samples and determined that this resulted from close proximity well-to-well cross contamination or “splashome”. We eliminated this source of contamination by repeating the sequencing run with a minimum of four wells separating high biomass from low biomass samples. This reduced the reads of bacterial 16S rRNA genes in placental samples to insignificant numbers.

Conclusions

We identified the problem of well-to-well contamination (“splashome”) as an additional source of error in microbiome studies of low biomass samples and found a method of eliminating it. Once “kitome” and “splashome” contaminants were eliminated, we were unable to identify a unique placental microbiome.

YAMP: a containerized workflow enabling reproducibility in metagenomics research

YAMP ("Yet Another Metagenomics Pipeline") is a user-friendly workflow that enables the analysis of whole shotgun metagenomic data while using containerization to ensure computational reproducibility and facilitate collaborative research. YAMP can be executed on any UNIX-like system and offers seamless support for multiple job schedulers as well as for the Amazon AWS cloud. Although YAMP was developed to be ready to use by nonexperts, bioinformaticians will appreciate its flexibility, modularization, and simple customization.

Potential testing of reprocessing procedures by real-time polymerase chain reaction: A multicenter study of colonoscopy devices

BACKGROUND

Reprocessing of endoscopes is key to preventing cross-infection after colonoscopy. Culture-based methods are recommended for monitoring, but alternative and rapid approaches are needed to improve surveillance and reduce turnover times. A molecular strategy based on detection of residual traces from gut microbiota was developed and tested using a multicenter survey.

METHODS

A simplified sampling and DNA extraction protocol using nylon-tipped flocked swabs was optimized. A multiplex real-time polymerase chain reaction (PCR) test was developed that targeted 6 bacteria genes that were amplified in 3 mixes. The method was validated by interlaboratory tests involving 5 reference laboratories. Colonoscopy devices (n = 111) were sampled in 10 Italian hospitals. Culture-based microbiology and metagenomic tests were performed to verify PCR data.

RESULTS

The sampling method was easily applied in all 10 endoscopy units and the optimized DNA extraction and amplification protocol was successfully performed by all of the involved laboratories. This PCR-based method allowed identification of both contaminated (n = 59) and fully reprocessed endoscopes (n = 52) with high sensibility (98%) and specificity (98%), within 3-4 hours, in contrast to the 24-72 hours needed for a classic microbiology test. Results were confirmed by next-generation sequencing and classic microbiology.

CONCLUSIONS

A novel approach for monitoring reprocessing of colonoscopy devices was developed and successfully applied in a multicenter survey. The general principle of tracing biological fluids through microflora DNA amplification was successfully applied and may represent a promising approach for hospital hygiene.

Bioinformatics for NGS-based metagenomics and the application to biogas research

Metagenomics has proven to be one of the most important research fields for microbial ecology during the last decade. Starting from 16S rRNA marker gene analysis for the characterization of community compositions to whole metagenome shotgun sequencing which additionally allows for functional analysis, metagenomics has been applied in a wide spectrum of research areas. The cost reduction paired with the increase in the amount of data due to the advent of next-generation sequencing led to a rapidly growing demand for bioinformatic software in metagenomics. By now, a large number of tools that can be used to analyze metagenomic datasets has been developed. The Bielefeld-Gießen center for microbial bioinformatics as part of the German Network for Bioinformatics Infrastructure bundles and imparts expert knowledge in the analysis of metagenomic datasets, especially in research on microbial communities involved in anaerobic digestion residing in biogas reactors. In this review, we give an overview of the field of metagenomics, introduce into important bioinformatic tools and possible workflows, accompanied by application examples of biogas surveys successfully conducted at the Center for Biotechnology of Bielefeld University.

Feed-additive probiotics accelerate yet antibiotics delay intestinal microbiota maturation in broiler chicken

BACKGROUND

Reducing antibiotics overuse in animal agriculture is one key in combat against the spread of antibiotic resistance. Probiotics are a potential replacement of antibiotics in animal feed; however, it is not clear whether and how probiotics and antibiotics differ in impact on physiology and microbial ecology of host animals.

RESULTS

Host phenotype and fecal microbiota of broilers with either antibiotics or probiotics as feed additive were simultaneously sampled at four time points from birth to slaughter and then compared. Probiotic feeding resulted in a lower feed conversion ratio (FCR) and induced the highest level of immunity response, suggesting greater economic benefits in broiler farming. Probiotic use but not antibiotic use recapitulated the characteristics of age-dependent development of gut microbiota in the control group. The maturation of intestinal microbiota was greatly accelerated by probiotic feeding, yet significantly retarded and eventually delayed by antibiotic feeding. LP-8 stimulated the growth of many intestinal Lactobacillus spp. and led to an altered bacterial correlation network where Lactobacillus spp. are negatively correlated with 14 genera and positively linked with none, yet from the start antibiotic feeding featured a less-organized network where such inter-genera interactions were fewer and weaker. Consistently, microbiota-encoded functions as revealed by metagenome sequencing were highly distinct between the two groups. Thus, "intestinal microbiota maturation index" was proposed to quantitatively compare impact of feed additives on animal microecology.

CONCLUSIONS

Our results reveal a tremendous potential of probiotics as antibiotics' substitute in poultry farming.

Culturomics: A New Kid on the Block of OMICS to Enable Personalized Medicine

This innovation analysis highlights the underestimated and versatile potential of the new field of culturomics and examines its relation to other OMICS system sciences such as infectiomics, metabolomics, phenomics, and pharmacomicrobiomics. The advent of molecular biology, followed by the emergence of various disciplines of the genomics, and most importantly metagenomics, brought about the sharp decline of conventional microbiology methods. Emergence of culturomics has a natural synergy with therapeutic and clinical genomic approaches so as to realize personalized medicine. Notably, the concept of culturomics expands on that of phenomics and allows a reintroduction of the culture-based phenotypic characterization into the 21st century research repertoire, bolstered by robust technology for automated and massive execution, but its potential is largely unappreciated at present; the few available references show unenthusiastic pursuit and in narrow applications. This has not to be so: depending on the specific brand of culturomics, the scope of applications may extend to medicine, agriculture, environmental sciences, pharmacomicrobiomics, and biotechnology innovation. Moreover, culturomics may produce Big Data. This calls for a new generation of data scientists and innovative ways of harnessing and valorizing Big Data beyond classical genomics. Much more detailed and objective classification and identification of microbiota may soon be at hand through culturomics, thus enabling precision diagnosis toward truly personalized medicine. Culturomics may both widen the scope of microbiology and improve its contributions to diagnostics and personalized medicine, characterizing microbes and determining their associations with health and disease dynamics.

Multi-OMICS: a critical technical perspective on integrative lipidomics approaches

During the past decades, high-throughput approaches for analyzing different molecular classes such as nucleic acids, proteins, metabolites, and lipids have grown rapidly. These approaches became powerful tools for getting a fundamental understanding of biological systems. Considering each approach and its results separately, relations and causal connections between these classes have no chance to be revealed, since only separate molecular snapshots are provided. Only a combined approach, not fully established yet, with the integration of the corresponding data, might yield a comprehensive and complete understanding of biological processes, such as crosstalk and interactions in signaling pathways. Taking two or more omics-methods into consideration for analysis is referred to as a multi-omics approach, which is gradually evolving. In this critical note, we briefly discuss the relevance, challenges, current state, and potential of data integration from multi-omics approaches, with a special focus on lipidomics analysis, listing the advantages and gaps in this field. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.

Characterization of Sinus Microbiota by 16S Sequencing from Swabs

New culture-independent microbiology methods are leading to a paradigm shift in our understanding of how the microbial community at the mucosal surface impacts sinonasal health and disease. Whereas traditional culture-based protocols were designed to identify specific pathogens in order to direct antibiotic therapies and eradicate bacteria, newer molecular techniques allow for the identification of both culturable and nonculturable bacteria in diverse communities. As a result of the recent explosion in the use of molecular techniques, we are gaining an understanding of how commensal bacteria may help modulate the host immune response and promote homeostasis. Here, we describe the general workflow of microbiome sequencing including the detailed methods for extracting mixed-community genomic DNA from sinonasal swabs, amplifying bacterial 16S rRNA genes using quantitative PCR, and preparing the samples for next-generation sequencing on the most commonly used sequencing platforms.

Informativeness of NGS Analysis for Vaginal Fluid Identification

The identification of vaginal fluids in forensic examinations plays an important role in crime scene reconstruction. Molecular detection of vaginal bacterial communities can lead to the correct discrimination of body fluids. These kinds of studies can be performed through multiplex real-time PCR using primers for a specific selection of bacteria. The availability of next-generation sequencing (NGS) protocols provided for the extension of the analysis to evaluate the prokaryotes present in specimens. In this study, DNA was extracted from 18 samples (vaginal, oral, fecal, yoghurt) and analyzed by real-time PCR and NGS. The comparison between the two approaches has demonstrated that the information developed through NGS can augment the more conventional real-time PCR detection of a few key bacterial species to provide a more probative result and the correct identification of vaginal fluid from samples that are more forensically challenged.

Computational prediction shines light on type III secretion origins

Type III secretion system is a key bacterial symbiosis and pathogenicity mechanism responsible for a variety of infectious diseases, ranging from food-borne illnesses to the bubonic plague. In many Gram-negative bacteria, the type III secretion system transports effector proteins into host cells, converting resources to bacterial advantage. Here we introduce a computational method that identifies type III effectors by combining homology-based inference with de novo predictions, reaching up to 3-fold higher performance than existing tools. Our work reveals that signals for recognition and transport of effectors are distributed over the entire protein sequence instead of being confined to the N-terminus, as was previously thought. Our scan of hundreds of prokaryotic genomes identified previously unknown effectors, suggesting that type III secretion may have evolved prior to the archaea/bacteria split. Crucially, our method performs well for short sequence fragments, facilitating evaluation of microbial communities and rapid identification of bacterial pathogenicity – no genome assembly required. pEffect and its data sets are available at http://services.bromberglab.org/peffect.

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.

Challenges of the Unknown: Clinical Application of Microbial Metagenomics

Availability of fast, high throughput and low cost whole genome sequencing holds great promise within public health microbiology, with applications ranging from outbreak detection and tracking transmission events to understanding the role played by microbial communities in health and disease. Within clinical metagenomics, identifying microorganisms from a complex and host enriched background remains a central computational challenge. As proof of principle, we sequenced two metagenomic samples, a known viral mixture of 25 human pathogens and an unknown complex biological model using benchtop technology. The datasets were then analysed using a bioinformatic pipeline developed around recent fast classification methods. A targeted approach was able to detect 20 of the viruses against a background of host contamination from multiple sources and bacterial contamination. An alternative untargeted identification method was highly correlated with these classifications, and over 1,600 species were identified when applied to the complex biological model, including several species captured at over 50% genome coverage. In summary, this study demonstrates the great potential of applying metagenomics within the clinical laboratory setting and that this can be achieved using infrastructure available to nondedicated sequencing centres.

Fecal microbiota analysis: an overview of sample collection methods and sequencing strategies

Despite huge interest, there are still no universally accepted standards to conduct clinical studies in the field of gut microbiota analysis. Stool material is frequently used as a proxy of gut microbiota, but many different protocols can be used for collection and DNA extraction. Whereas 16S rRNA encoding gene amplification and sequencing has been widely used to study the composition of bacterial populations, it is now being challenged by the random, shotgun approach that brings far more information, although at a higher cost. In this review we give an overview of existing methods and important points to consider when conducting gut microbiota studies, with the objective to provide recommendations to those who would like to conduct such research.

Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data

BACKGROUND

The rapid expansion of 16S rRNA gene sequencing in challenging clinical contexts has resulted in a growing body of literature of variable quality. To a large extent, this is due to a failure to address spurious signal that is characteristic of samples with low levels of bacteria and high levels of non-bacterial DNA. We have developed a workflow based on the paired-end read Illumina MiSeq-based approach, which enables significant improvement in data quality, post-sequencing. We demonstrate the efficacy of this methodology through its application to paediatric upper-respiratory samples from several anatomical sites.

RESULTS

A workflow for processing sequence data was developed based on commonly available tools. Data generated from different sample types showed a marked variation in levels of non-bacterial signal and 'contaminant' bacterial reads. Significant differences in the ability of reference databases to accurately assign identity to operational taxonomic units (OTU) were observed. Three OTU-picking strategies were trialled as follows: de novo, open-reference and closed-reference, with open-reference performing substantially better. Relative abundance of OTUs identified as potential reagent contamination showed a strong inverse correlation with amplicon concentration allowing their objective removal. The removal of the spurious signal showed the greatest improvement in sample types typically containing low levels of bacteria and high levels of human DNA. A substantial impact of pre-filtering data and spurious signal removal was demonstrated by principal coordinate and co-occurrence analysis. For example, analysis of taxon co-occurrence in adenoid swab and middle ear fluid samples indicated that failure to remove the spurious signal resulted in the inclusion of six out of eleven bacterial genera that accounted for 80% of similarity between the sample types.

CONCLUSIONS

The application of the presented workflow to a set of challenging clinical samples demonstrates its utility in removing the spurious signal from the dataset, allowing clinical insight to be derived from what would otherwise be highly misleading output. While other approaches could potentially achieve similar improvements, the methodology employed here represents an accessible means to exclude the signal from contamination and other artefacts.