Next-generation sequencing in the analysis of human microbiota: essential considerations for clinical application

Evaluation of the effects of Lake Hévíz sulfur thermal water on skin microbiome in plaque psoriasis: An open label, pilot study

Psoriasis is a chronic inflammatory skin disease. It is associated with changes in skin microbiome. The aim of this study was to evaluate how Lake Hévíz sulfur thermal water influences the composition of microbial communities that colonizes skin in patients with psoriasis. Our secondary objective was to investigate the effects of balneotherapy on disease activity. In this open label study, participants with plaque psoriasis underwent 30-min therapy sessions in Lake Hévíz, at a temperature of 36 °C, five times a week for 3 weeks. The skin microbiome samples were collected by swabbing method from two different areas (lesional skin-psoriatic plaque and non-lesional skin). From 16 patients, 64 samples were processed for a 16S rRNA sequence-based microbiome analysis. Outcome measures were alpha-diversity (Shannon, Simpson, and Chao1 indexes), beta-diversity (Bray-Curtis metric), differences in genus level abundances, and Psoriasis Area and Severity Index (PASI). Skin microbiome samples were collected at baseline, and immediately after treatment. Based on the visual examination of the employed alpha- and beta-diversity measures, no systematic difference based on sampling timepoint or sample location could be revealed in these regards. Balneotherapy in the unaffected area significantly increased the level of Leptolyngbya genus, and significantly decreased the level of Flavobacterium genus. A similar trend was revealed by the results of the psoriasis samples, but the differences were not statistically significant. In patients with mild psoriasis, a significant improvement was observed in PASI scores.

A novel metabarcoded deep amplicon sequencing tool for disease surveillance and determining the species composition of Trypanosoma in cattle and other farm animals

The World Health Organization (WHO) and the Food and Agriculture Organization (FAO) have developed strategies to control trypanosomiasis in humans and livestock in endemic areas. These require a better understanding of the distribution of different Trypanosoma species and improved predictions of where they might appear in the future, based on accurate diagnosis and robust surveillance systems. Here, we describe a metabarcoding deep amplicon sequencing method to identify and determine the Trypanosoma species in co-infecting communities. First, four morphological verified Trypanosoma species (T. brucei, T. congolense, T. vivax and T. theileri) were used to prepare test DNA pools derived from different numbers of parasites to evaluate the method's detection threshold for each of the four species and to assess the accuracy of their proportional quantification. Having demonstrated the accurate determination of species composition in Trypanosoma communities, the method was applied to determine its detection threshold using blood samples collected from cattle with confirmed Trypanosoma infections based on a PCR assay. Each sample showed a different Trypanosoma species composition based on the proportion of MiSeq reads. Finally, we applied the assay to field samples to develop new insight into the species composition of Trypanosoma communities in cattle, camels, buffalo, horses, sheep, and goat in endemically infected regions of Pakistan. We confirmed that Trypanosoma evansi is the major species in Pakistan and for the first time showed the presence of Trypanosoma theileri. The metabarcoding deep amplicon sequencing method and bioinformatics pathway have several potential applications in animal and human research, including evaluation of drug treatment responses, understanding of the emergence and spread of drug resistance, and description of species interactions during co-infections and determination of host and geographic distribution of trypanosomiasis in humans and livestock.

Composition of nasal bacterial community and its seasonal variation in health care workers stationed in a clinical research laboratory

The microorganisms at the workplace contribute towards a large portion of the biodiversity a person encounters in his or her life. Health care professionals are often at risk due to their frontline nature of work. Competition and cooperation between nasal bacterial communities of individuals working in a health care setting have been shown to mediate pathogenic microbes. Therefore, we investigated the nasal bacterial community of 47 healthy individuals working in a clinical research laboratory in Kuwait. The taxonomic profiling and core microbiome analysis identified three pre-dominant genera as Corynebacterium (15.0%), Staphylococcus (10.3%) and, Moraxella (10.0%). All the bacterial genera exhibited seasonal variations in summer, winter, autumn and spring. SparCC correlation network analysis revealed positive and negative correlations among the classified genera. A rich set of 16 genera (q < 0.05) were significantly differentially abundant (LEfSe) across the four seasons. The highest species counts, richness and evenness (P < 0.005) were recorded in autumn. Community structure profiling indicated that the entire bacterial population followed a seasonal distribution (R2-0.371; P < 0.001). Other demographic factors such as age, gender and, ethnicity contributed minimally towards community clustering in a closed indoor laboratory setting. Intra-personal diversity also witnessed rich species variety (maximum 6.8 folds). Seasonal changes in the indoor working place in conjunction with the outdoor atmosphere seems to be important for the variations in the nasal bacterial communities of professionals working in a health care setting.

The Vaginal Microbiome as a Tool to Predict rASRM Stage of Disease in Endometriosis: a Pilot Study

Endometriosis remains a challenge to understand and to diagnose. This is an observational cross-sectional pilot study to characterize the gut and vaginal microbiome profiles among endometriosis patients and control subjects without the disease and to explore their potential use as a less-invasive diagnostic tool for endometriosis. Overall, 59 women were included, n = 35 with endometriosis and n = 24 controls. Rectal and vaginal samples were collected in two different periods of the menstrual cycle from all subjects. Gut and vaginal microbiomes from patients with different rASRM (revised American Society for Reproductive Medicine) endometriosis stages and controls were analyzed. Illumina sequencing libraries were constructed using a two-step 16S rRNA gene PCR amplicon approach. Correlations of 16S rRNA gene amplicon data with clinical metadata were conducted using a random forest-based machine-learning classification analysis. Distribution of vaginal CSTs (community state types) significantly differed between follicular and menstrual phases of the menstrual cycle (p = 0.021, Fisher's exact test). Vaginal and rectal microbiome profiles and their association to severity of endometriosis (according to rASRM stages) were evaluated. Classification models built with machine-learning methods on the microbiota composition during follicular and menstrual phases of the cycle were built, and it was possible to accurately predict rASRM stages 1-2 verses rASRM stages 3-4 endometriosis. The feature contributing the most to this prediction was an OTU (operational taxonomic unit) from the genus Anaerococcus. Gut and vaginal microbiomes of women with endometriosis have been investigated. Our findings suggest for the first time that vaginal microbiome may predict stage of disease when endometriosis is present.

Development of a deep amplicon sequencing method to determine the species composition of piroplasm haemoprotozoa

Piroplasmosis is caused by tick-borne haemoprotozoa of the genera Theileria and Babesia. These parasitic infections can seriously impact on the health of livestock and production. Piroplasms of multiple species can be present in a single host, but reliable molecular diagnostic tools are needed in order to understand the composition of these complex parasite communities. Theileria and Babesia vary in their epidemiology, drug sensitivity, pathogenicity and interaction with co-infecting species, but are similar in that infected animals become persistent carriers after recovery from primary infection, acting as reservoir hosts. Here, we describe for the first time the use of a deep amplicon sequencing platform to identify proportions of piroplasm species in co-infecting communities and develop the concept of a "haemoprotobiome". First, four phenotypically-verified species of Theileria and Babesia were used to prepare mock DNA pools with random numbers of the parasites amplified by four different numbers of PCR cycles to assess sequence representation for each species. Second, we evaluated the detection threshold of the deep amplicon sequencing assay for each of the four species and to assess the accuracy of proportional quantification of all four species. Finally, we applied the assay to the field samples to afford insight of the species composition of piroplasm communities in small and large ruminants in the Punjab province of Pakistan. The "haemoprotobiome" concept has several potential applications in veterinary and human research, including understanding of responses to drug treatment; parasite epidemiology and ecology; species interactions during mixed infections; and parasite control strategies.

The contribution of respiratory microbiome analysis to a treatable traits model of care

The composition of the airway microbiome in patients with chronic airway diseases, such as severe asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis and cystic fibrosis (CF), has the potential to inform a precision model of clinical care. Patients with these conditions share overlapping disease characteristics, including airway inflammation and airflow limitation. The clinical management of chronic respiratory conditions is increasingly moving away from a one-size-fits-all model based on primary diagnosis, towards care targeting individual disease traits, and is particularly useful for subgroups of patients who respond poorly to conventional therapies. Respiratory microbiome analysis is an important potential contributor to such a 'treatable traits' approach, providing insight into both microbial drivers of airways disease, and the selective characteristics of the changing lower airway environment. We explore the potential to integrate respiratory microbiome analysis into a treatable traits model of clinical care and provide a practical guide to the application and clinical interpretation of respiratory microbiome analysis.

Fungal-Grade Reagents and Materials for Molecular Analysis

Fungal DNA is present at very low loads in clinical specimens. Molecular detection by amplification assays generally is a challenge because of a potentially multiple input of contaminating DNA from exogenous sources. Besides airborne, handling and cross-contamination, materials and reagents used in the molecular laboratory can contain microbial DNA which is a long underestimated potential source of false positive results. In this contribution decontamination procedures of materials and reagents and the selection of certified microbial DNA-free components for sample collection, DNA extraction, and PCR amplification are discussed with respect to the aim of building up a reliable molecular system for the diagnosis of fungal organisms at the limit of detection.

New Insights into the Microbiota of Moth Pests

In recent years, next generation sequencing (NGS) technologies have helped to improve our understanding of the bacterial communities associated with insects, shedding light on their wide taxonomic and functional diversity. To date, little is known about the microbiota of lepidopterans, which includes some of the most damaging agricultural and forest pests worldwide. Studying their microbiota could help us better understand their ecology and offer insights into developing new pest control strategies. In this paper, we review the literature pertaining to the microbiota of lepidopterans with a focus on pests, and highlight potential recurrent patterns regarding microbiota structure and composition.

Comparison of DNA-, PMA-, and RNA-based 16S rRNA Illumina sequencing for detection of live bacteria in water

The limitation of 16S rRNA gene sequencing (DNA-based) for microbial community analyses in water is the inability to differentiate live (dormant cells as well as growing or non-growing metabolically active cells) and dead cells, which can lead to false positive results in the absence of live microbes. Propidium-monoazide (PMA) has been used to selectively remove DNA from dead cells during downstream sequencing process. In comparison, 16S rRNA sequencing (RNA-based) can target live microbial cells in water as both dormant and metabolically active cells produce rRNA. The objective of this study was to compare the efficiency and sensitivity of DNA-based, PMA-based and RNA-based 16S rRNA Illumina sequencing methodologies for live bacteria detection in water samples experimentally spiked with different combination of bacteria (2 gram-negative and 2 gram-positive/acid fast species either all live, all dead, or combinations of live and dead species) or obtained from different sources (First Nation community drinking water; city of Winnipeg tap water; water from Red River, Manitoba, Canada). The RNA-based method, while was superior for detection of live bacterial cells still identified a number of 16S rRNA targets in samples spiked with dead cells. In environmental water samples, the DNA- and PMA-based approaches perhaps overestimated the richness of microbial community compared to RNA-based method. Our results suggest that the RNA-based sequencing was superior to DNA- and PMA-based methods in detecting live bacterial cells in water.

Global Microbial Identifier

Human and animal populations are increasingly confronted with emerging and re-emerging infections and often such infections are exchanged between these populations, e.g. through food. A more effective and uniform approach to the prevention of these microbial threats is essential. The technological advances in the next generation sequencing field and decreasing costs of these tests provide novel opportunities in understanding the dynamics of infection—even in real time—through the analysis of microbial genome diversity. The projected significant increase in whole (microbial) genome sequencing (WGS) will likely also enable a much better understanding of the pathogenesis of the infection and the molecular basis of the host response to infection. But the full potential of these advances will only transpire if the data in this area become transferable and thereby comparable, preferably in open-source systems. There is therefore an obvious need to develop a global system of whole microbial genome databases to aggregate, share, mine and use microbiological genomic data, to address global public health and clinical challenges, and most importantly to identify and diagnose infectious diseases. The global microbial identifier (GMI) initiative, aims to build a database of whole microbial genome sequencing data linked to relevant metadata, which can be used to identify microorganisms, their communities and the diseases they cause. It would be a platform for storing whole genome sequencing (WGS) data of microorganisms, for the identification of relevant genes and for the comparison of genomes to detect outbreaks and emerging pathogens. To harness the full potential of WGS, a shared global database of genomes linked to relevant metadata and the necessary software tools needs to be generated, hence the global microbial identifier (GMI) initiative. This tool will ideally be used in amongst others in the diagnosis of infectious diseases in humans and animals, in the identification of microorganisms in food and environment, and to track and trace microbial agents in all arenas globally. This will require standardization and extensive investments in computational analytical tools. In addition, the wider introduction of WGS in clinical diagnostics can accelerate developments in health care in many poor countries. This overview describes the growing network of stakeholders behind GMI, the contours of the database, and the IT structures needed to serve the GMI user community. It discusses what essentially can be done by a global GMI tool and how the GMI organization could help achieve these goals.

Exploring the Gastrointestinal "Nemabiome": Deep Amplicon Sequencing to Quantify the Species Composition of Parasitic Nematode Communities

Parasitic helminth infections have a considerable impact on global human health as well as animal welfare and production. Although co-infection with multiple parasite species within a host is common, there is a dearth of tools with which to study the composition of these complex parasite communities. Helminth species vary in their pathogenicity, epidemiology and drug sensitivity and the interactions that occur between co-infecting species and their hosts are poorly understood. We describe the first application of deep amplicon sequencing to study parasitic nematode communities as well as introduce the concept of the gastro-intestinal “nemabiome”. The approach is analogous to 16S rDNA deep sequencing used to explore microbial communities, but utilizes the nematode ITS-2 rDNA locus instead. Gastro-intestinal parasites of cattle were used to develop the concept, as this host has many well-defined gastro-intestinal nematode species that commonly occur as complex co-infections. Further, the availability of pure mono-parasite populations from experimentally infected cattle allowed us to prepare mock parasite communities to determine, and correct for, species representation biases in the sequence data. We demonstrate that, once these biases have been corrected, accurate relative quantitation of gastro-intestinal parasitic nematode communities in cattle fecal samples can be achieved. We have validated the accuracy of the method applied to field-samples by comparing the results of detailed morphological examination of L3 larvae populations with those of the sequencing assay. The results illustrate the insights that can be gained into the species composition of parasite communities, using grazing cattle in the mid-west USA as an example. However, both the technical approach and the concept of the ‘nemabiome’ have a wide range of potential applications in human and veterinary medicine. These include investigations of host-parasite and parasite-parasite interactions during co-infection, parasite epidemiology, parasite ecology and the response of parasite populations to both drug treatments and control programs.

Next generation sequencing in cardiomyopathy: towards personalized genomics and medicine

Next generation sequencing (NGS) is perhaps one of the most exciting advances in the field of life sciences and biomedical research in the last decade. With the availability of massive parallel sequencing, human DNA blueprint can be decoded to explore the hidden information with reduced time and cost. This technology has been used to understand the genetic aspects of various diseases including cardiomyopathies. Mutations for different cardiomyopathies have been identified and cataloging mutations on phenotypic basis are underway and are expected to lead to new discoveries that may translate to novel diagnostic, prognostic and therapeutic targets. With ease in handling NGS, cost effectiveness and fast data output, NGS is now considered as a diagnostic tool for cardiomyopathy by providing targeted gene sequencing. In addition to the number of genetic variants that are identified in cardiomyopathies, there is a need of quicker and easy way to screen multiple genes associated with the disease. In this review, an attempt has been made to explain the NGS technology, methods and applications in cardiomyopathies and their perspective in clinical practice and challenges which are to be addressed.

Analysis of microbiota in stable patients with chronic obstructive pulmonary disease

To identify the bacterial diversity (microbiota) in expectorated sputum, a pyrosequencing method that investigates complex microbial communities of expectorated sputum was done in 19 stable chronic obstructive pulmonary disease patients (mean (SD) FEV1: 47 (18%) of predicted value). Using conventional culture, 3 phyla and 20 bacterial genera were identified, whereas the pyrosequencing approach detected 9 phyla and 43 genera (p < 0.001). In sputum the prevalent genera with pyrosequencing approach were Streptococcus, Actinomyces, Neisseria, Haemophilus, Rothia, Fusobacterium, Gemella, Granulicatella, Porphyromonas, Prevotella and Veillonella. Enterobacteriaceae, detected frequently in conventional culture, were not significantly detected with pyrosequencing methods. In addition, we found that important pathogens such as Haemophilus and Moraxella were detected more frequently with the new genetic procedures. The presence of Enterobacteriaceae is probably overestimated with conventional culture, whereas other difficult cultivable pathogens are underestimated. These studies open a new perspective for evaluating the role of bacterial colonization in chronic obstructive pulmonary disease pathogenesis and progression.

The cystic fibrosis airway microbiome

PURPOSE OF REVIEW

Culture and molecular approaches have established that lower airway infections are polymicrobial. We consider how this new perspective in cystic fibrosis (CF) may affect treatment choices.

RECENT FINDINGS

Standard clinical microbiology of CF infection exacerbations often fails to provide indications of microbial causes that may drive the onset of exacerbation and the anticipated bacteriologic responses to the usual parenteral antibiotics prescribed as treatment. Antimicrobial responses by nonclassical members of the CF airway microbiome may explain why most patients clinically improve. These other organisms contribute to disease either directly as pathogens missed by conventional microbiology or through synergy with conventional pathogens. With these considerations, therapy may best be guided by directed antibiotic therapy to numerically significant isolates. An example is the Streptococcus milleri group, which we now believe to represent new pathogens that profile the exacerbations of infection in the CF lung and that necessitate specific antibiotic therapy to prevent loss of lung function and reduce frequency of exacerbations.

SUMMARY

A comprehensive understanding of airway infections offers the potential for improved disease management in CF patients. Accurate quantitative microbiology will be a prerequisite for routine intervention based on the polymicrobial perspective of CF infection exacerbations.

Molecular detection of CF lung pathogens: current status and future potential

Molecular diagnostic tests, based on the detection and identification of nucleic acids in human biological samples, are increasingly employed in the diagnosis of infectious diseases and may be of future benefit to CF microbiology services. Our growing understanding of the complex polymicrobial nature of CF airway infection has highlighted current and likely future shortcomings in standard diagnostic practices. Failure to detect fastidious or slow growing microbes and misidentification of newly emerging pathogens could potentially be addressed using culture-independent molecular technologies with high target specificity. This review considers existing molecular diagnostic tests in the context of the key requirements for an envisaged CF microbiology focussed assay. The issues of assay speed, throughput, detection of multiple pathogens, data interpretation and antimicrobial susceptibility testing are discussed.

Next-generation sequencing approaches to understanding the oral microbiome

Until recently, the focus in dental research has been on studying a small fraction of the oral microbiome-so-called opportunistic pathogens. With the advent of next-generation sequencing (NGS) technologies, researchers now have the tools that allow for profiling of the microbiomes and metagenomes at unprecedented depths. The major advantages of NGS are the high throughput and the fact that specific taxa do not need to be targeted. The relatively low cost and the availability of sequencing facilities have contributed to nearly exponential growth of NGS datasets. The quality and interpretation of the NGS data could be undermined at numerous steps-from sample collection, storage, and DNA extraction to PCR bias, sequencing errors, choice of algorithms for data processing, and statistical analyses. Making sense out of this data deluge is and will be the major challenge. The community analyses based on systems ecology principles will bring us closer to an understanding of the underlying forces that facilitate the stability (or imbalance) of the microbiome. The next logical step will take us beyond the microbiome. The integration of bacterial, viral, fungal "meta-omes" such as the meta-transcriptome, meta-proteome, and meta-metabolome, together with the host as a major co-factor, should be the ultimate goal in unraveling the complexity of the oral interactome.

Molecular methods for pathogen and microbial community detection and characterization: current and potential application in diagnostic microbiology

Clinical microbiology laboratories worldwide have historically relied on phenotypic methods (i.e., culture and biochemical tests) for detection, identification and characterization of virulence traits (e.g., antibiotic resistance genes, toxins) of human pathogens. However, limitations to implementation of molecular methods for human infectious diseases testing are being rapidly overcome allowing for the clinical evaluation and implementation of diverse technologies with expanding diagnostic capabilities. The advantages and limitation of molecular techniques including real-time polymerase chain reaction, partial or whole genome sequencing, molecular typing, microarrays, broad-range PCR and multiplexing will be discussed. Finally, terminal restriction fragment length polymorphism (T-RFLP) and deep sequencing are introduced as technologies at the clinical interface with the potential to dramatically enhance our ability to diagnose infectious diseases and better define the epidemiology and microbial ecology of a wide range of complex infections.

Next generation sequencing for profiling expression of miRNAs: technical progress and applications in drug development

miRNAs are non-coding RNAs that play a regulatory role in expression of genes and are associated with diseases. Quantitatively measuring expression levels of miRNAs can help in understanding the mechanisms of human diseases and discovering new drug targets. There are three major methods that have been used to measure the expression levels of miRNAs: real-time reverse transcription PCR (qRT-PCR), microarray, and the newly introduced next-generation sequencing (NGS). NGS is not only suitable for profiling of known miRNAs as qRT-PCR and microarray can do too but it also is able to detect unknown miRNAs which the other two methods are incapable of doing. Profiling of miRNAs by NGS has progressed rapidly and is a promising field for applications in drug development. This paper reviews the technical advancement of NGS for profiling miRNAs, including comparative analyses between different platforms and software packages for analyzing NGS data. Examples and future perspectives of applications of NGS profiling miRNAs in drug development will be discussed.