Deciphering the human microbiome using next-generation sequencing data and bioinformatics approaches

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

The human "contaminome": bacterial, viral, and computational contamination in whole genome sequences from 1000 families

The unmapped readspace of whole genome sequencing data tends to be large but is often ignored. We posit that it contains valuable signals of both human infection and contamination. Using unmapped and poorly aligned reads from whole genome sequences (WGS) of over 1000 families and nearly 5000 individuals, we present insights into common viral, bacterial, and computational contamination that plague whole genome sequencing studies. We present several notable results: (1) In addition to known contaminants such as Epstein-Barr virus and phiX, sequences from whole blood and lymphocyte cell lines contain many other contaminants, likely originating from storage, prep, and sequencing pipelines. (2) Sequencing plate and biological sample source of a sample strongly influence contamination profile. And, (3) Y-chromosome fragments not on the human reference genome commonly mismap to bacterial reference genomes. Both experiment-derived and computational contamination is prominent in next-generation sequencing data. Such contamination can compromise results from WGS as well as metagenomics studies, and standard protocols for identifying and removing contamination should be developed to ensure the fidelity of sequencing-based studies.

Leveraging big data bioinformatics approaches to extract knowledge from Staphylococcus aureus public omics data

Staphylococcus aureus is a notorious pathogen posing challenges in the medical industry due to drug resistance and biofilm formation. The horizon of knowledge on S. aureus pathogenesis has expanded with the advancement of data-driven bioinformatics techniques. Mining information from sequenced genomes and their expression data is an economic approach that alleviates wastage of resources and redundancy in experiments. The current review covers how big data bioinformatics has been used in the analysis of S. aureus from publicly available -omics data to uncover mechanisms of infection and inhibition. Particularly, advances in the past two decades in biomarker discovery, host responses, phenotype identification, consolidation of information, and drug development are discussed highlighting the challenges and shortcomings. Overall, the review summarizes the diverse aspects of scrupulous re-analysis of S. aureus proteomic and transcriptomic expression datasets retrieved from public repositories in terms of the efforts taken, benefits offered, and follow-up actions. The detailed review thus serves as a reference and aid for (i) Computational biologists by briefing the approaches utilized for bacterial omics re-analysis concerning S. aureus and (ii) Experimental biologists by elucidating the potential of bioinformatics in biological research to generate reliable postulates in a prompt and economical manner.

Application of artificial intelligence in microbiome study promotes precision medicine for gastric cancer

The microbiome has been identified as a causing factor for many cancers. Helicobacter pylori contributes to the development of gastric cancer (GC) and impacts disease treatments. The rapid development of sequencing technology is increasingly producing large-scale and complex big data. However, there are many obstacles in the analysis of these data by humans, which limit clinicians from making rapid decisions. Recently, the emergence of artificial intelligence (AI), including machine learning and deep learning, has greatly assisted clinicians in processing and interpreting large microbiome data. This paper reviews the application of AI in the study of the microbiome and discusses its potential in the diagnosis and therapy of GC. We also exemplify strategies for implementing microbiome-based precision medicines for patients with GC.

Colorectal Cancer Stage-Specific Fecal Bacterial Community Fingerprinting of the Taiwanese Population and Underpinning of Potential Taxonomic Biomarkers

Despite advances in the characterization of colorectal cancer (CRC), it still faces a poor prognosis. There is growing evidence that gut microbiota and their metabolites potentially contribute to the development of CRC. Thus, microbial dysbiosis and their metabolites associated with CRC, based on stool samples, may be used to advantage to provide an excellent opportunity to find possible biomarkers for the screening, early detection, prevention, and treatment of CRC. Using 16S rRNA amplicon sequencing coupled with statistical analysis, this study analyzed the cause–effect shift of the microbial taxa and their metabolites that was associated with the fecal gut microbiota of 17 healthy controls, 21 polyps patients, and 21 cancer patients. The microbial taxonomic shift analysis revealed striking differences among the healthy control, polyps and cancer groups. At the phylum level, Synergistetes was reduced significantly in the polyps group compared to the healthy control and cancer group. Additionally, at the genus level and in association with the cancer group, a total of 12 genera were highly enriched in abundance. In contrast, only Oscillosprira was significantly higher in abundance in the healthy control group. Comparisons of the polyps and cancer groups showed a total of 18 significantly enriched genera. Among them, 78% of the genera associated with the cancer group were in higher abundance, whereas the remaining genera showed a higher abundance in the polyps group. Additionally, the comparison of healthy control and polyp groups showed six significantly abundant genera. More than 66% of these genera showed a reduced abundance in the polyps group than in healthy controls, whereas the remaining genera were highly abundant in the polyps group. Based on tumor presence and absence, the abundance of Olsenella and Lactobacillus at the genus level was significantly reduced in the patient group compared to healthy controls. The significant microbial function prediction revealed an increase in the abundance of metabolites in the polyps and cancer groups compared to healthy controls. A correlation analysis revealed a higher contribution of Dorea in the predicted functions. This study showed dysbiosis of gut microbiota at the taxonomic level and their metabolic functions among healthy subjects and in two stages of colorectal cancer, including adenoma and adenocarcinoma, which might serve as potential biomarkers for the early diagnosis and treatment of CRC.

Possible role of microbiome in the pathogenesis of endometriosis

INTRODUCTION

There is an urgent necessity to explore the complex pathophysiological nature of endometriosis, which may enable the rationale for new diagnostic and therapeutic strategies to be discovered. This systematic review aimed to clarify the bidirectional relationship between endometriosis and the microbiome and evaluate if the microbiome may be involved in endometriosis's pathogenesis, establishing a potential connection between the different studies.

EVIDENCE ACQUISITION

Studies were identified through a systematic literature search of papers that evaluated the microbiomes of human or other animal species with endometriosis and of those without in the electronic database PubMed/Medline, and Embase without a date restriction. We included all cohort studies focusing on the interaction between endometriosis and the microbiomes of humans or other mammals, evaluating if the microbiome may be involved in endometriosis's pathogenesis.

EVIDENCE SYNTHESIS

Endometriosis appears to be associated with elevated levels of different microorganisms across various microbiome sites. An ineffective immune response seems to play a key role in endometriosis pathogenesis, and there is some scientific proof to state that the immune response may be modulated by the microbiome. Interestingly, nine studies of our review detected species belonging to the phyla Proteobacteria, Bacteroidetes, and Negativicutes characterized by Gram-negative staining, that were significantly increased in endometriosis cohorts.

CONCLUSIONS

Laboratory and clinical investigations indicate that hosts' microbiome profiles with and without endometriosis can be significantly different. To further our understanding of the relationships between endometriosis and the host microbiome, more studies are necessary.

Increasing Reproducibility in Oral Microbiome Research

Evidence on the role of the oral microbiome in health and disease is changing the way we understand, diagnose, and treat ailments. Numerous studies on diseases affecting the oral cavity have revealed a large amount of data that is invaluable for the advancements in diagnosing and treating these diseases. However, the clinical translation of most of these exploratory data is stalled by variable methodology between studies and non-uniform reporting of the data.Understanding the key areas that are gateways to bias in microbiome studies is imperative to overcome this challenge faced by oral microbiome research. Bias can be multifactorial and may be introduced in a microbiome research study during the formulation of the study design, sample collection and storage, or the sample processing protocols before sequencing. This chapter summarizes the recommendations from literature to eliminate bias in the microbiome research studies and to ensure the reproducibility of the microbiome research data.

Microbiome dysbiosis in lung cancer: from composition to therapy

The correlations between microbiota dysbiosis and cancer have gained extensive attention and been widely explored. As a leading cancer diagnosis worldwide, lung cancer poses a great threat to human health. The healthy human lungs are consistently exposed to external environment and harbor a specific pattern of microbiota, sharing many key pathological and physiological characteristics with the intestinal tract. Although previous findings uncovered the critical roles of microbiota in tumorigenesis and response to anticancer therapy, most of them were focused on the intestinal microbiota rather than lung microbiota. Notably, the considerable functions of microbiota in maintaining lung homeostasis should not be neglected as the microbiome dysbiosis may promote tumor development and progression through production of cytokines and toxins and multiple other pathways. Despite the fact that increasing studies have revealed the effect of microbiome on the induction of lung cancer and different disease status, the underlying mechanisms and potential therapeutic strategies remained unclear. Herein, we summarized the recent progresses about microbiome in lung cancer and further discussed the role of microbial communities in promoting lung cancer progression and the current status of therapeutic approaches targeting microbiome to alleviate and even cure lung cancer.

The Nidobiome: A Framework for Understanding Microbiome Assembly in Neonates

The importance of microbial associations to animals' development, physiology, and fitness is widely recognized. In most animals, these microbial associations must be developed anew with every generation, making microbiome assembly a critical ecological and evolutionary process. To fully understand neonate microbial colonization, we need to study the interacting effects of neonate, parents, nest, and external environment. We propose an integrative approach based on the concept of the 'nidobiome', a new unit of microbiome-host interactions, which brings together these key elements. We discuss the contribution of each element on microbial colonization at different stages of host development, and we provide a framework based on key developmental events to compare microbiome assembly across animal species.

The importance and future of biochemical engineering

Today's Biochemical Engineer may contribute to advances in a wide range of technical areas. The recent Biochemical and Molecular Engineering XXI conference focused on "The Next Generation of Biochemical and Molecular Engineering: The role of emerging technologies in tomorrow's products and processes". On the basis of topical discussions at this conference, this perspective synthesizes one vision on where investment in research areas is needed for biotechnology to continue contributing to some of the world's grand challenges.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

The Evolving Role of Gut Microbiota in the Management of Irritable Bowel Syndrome: An Overview of the Current Knowledge

The intestinal microbiota is one of the most rapidly evolving areas in biology and medicine. Extensive research in the last decade has escalated our understanding of the role of the microbiota in the pathogenesis of several intestinal and extra-intestinal disorders. Marked by high prevalence, substantial morbidity, and enormous costs, irritable bowel syndrome (IBS) is an important chronic gastrointestinal disorder that is widely encountered by gastroenterologists. Despite advances in our understanding of its pathophysiology, curative interventions have yet to be discovered, and therapeutic approaches remain symptom-driven. Recently, accumulating evidence has enlightened the possible impact of an imbalanced gut microbiome in the pathogenesis of IBS. In fact, several studies have documented altered microbiota in patients, while others have shown that IBS severity was associated with a distinct microbiota signature. These findings may pave the way for the use of microbiota manipulation strategies as an attractive option for IBS management, and may have an essential role in efforts to reduce the societal and economic effects of this ever-growing disorder. In this review, we have outlined the results of the latest research on the association between microbiota and IBS and their implications for the clinical management of affected patients.

Human Endometrial Microbiota at Term of Normal Pregnancies

The endometrium is a challenging site for metagenomic analysis due to difficulties in obtaining uncontaminated samples and the limited abundance of the bacterial population. Indeed, solid correlations between endometrial physio-pathologic conditions and bacteria compositions have not yet been firmly established. Nevertheless, the study of the endometrial microbiota is of great interest due to the close correlations between microbiota profiles, women’s health, and successful pregnancies. In this study, we decided to tackle the study of the endometrial microbiota through analysis of bacterial population in women subjected to elective caesarean delivery. As a pilot study, a cohort of 19 Caucasian women at full term of normal pregnancy and with a prospection of elective caesarean delivery was enrolled for endometrium sampling at the time of caesarean section. Sampling was carried out by endometrial biopsy soon after the delivery of the newborn and the discharge of the placenta and fetal membranes from the uterus. Bacterial composition was established by a deep metabarcoding next generation sequencing (NGS) procedure addressing the V5–V6 hypervariable region of the 16S rRNA gene. Amplicon sequences were analysed by bioinformatic procedures for denoising and taxonomic classification. The RDP database was used as 16S rRNA reference collection. Metabarcoding analysis showed the presence of a common bacterial composition, including six genera classifiable within the human microbiota (Cutibacterium, Escherichia, Staphylococcus, Acinetobacter, Streptococcus, Corynebacterium), that could be part of the core endometrial microbiota under the specific conditions examined. These results can provide useful information for future studies on the correlations between bacteria and successful pregnancies.

Scales of persistence: transmission and the microbiome

Historically microbiomes have been studied on the scale of the individual host, giving little consideration for the role of extra-host microbial populations in microbiome assembly. However, work in recent years has brought to light the importance of inter-host transmission and its influence on microbiome composition and dynamics. We now appreciate that microbiomes do not exist in isolation, but exchange constituents with the microbial communities of other hosts and the environment. Moving forward, fully understanding the role of transmission in microbiome assembly and dynamics will require a high-resolution view of the colonization and persistence patterns of particular microbial lineages (i.e. strains) across individuals and the environment. Yet, accomplishing this level of resolution will be an immense challenge, requiring improved sampling and bioinformatics approaches as well as employment of tractable experimental models. Insight gained from these investigations will contribute to our understanding of microbiome composition and variation, and lead to improved strategies for modulating microbiomes to improve human health.

Evaluation of 16S next-generation sequencing of hypervariable region 4 in wastewater samples: An unsuitable approach for bacterial enteric pathogen identification

Recycled wastewater can carry human-infectious microbial pathogens and therefore wastewater treatment strategies must effectively eliminate pathogens before recycled wastewater is used to supplement drinking and agricultural water supplies. This study characterised the bacterial composition of four wastewater treatment plants (WWTPs) (three waste stabilisation ponds and one oxidation ditch WWTP using activated sludge treatment) in Western Australia. The hypervariable region 4 (V4) of the bacterial 16S rRNA (16S) gene was sequenced using next-generation sequencing (NGS) on the Illumina MiSeq platform. Sequences were pre-processed in USEARCH v10.0 and denoised into zero-radius taxonomic units (ZOTUs) with UNOISE3. Taxonomy was assigned to the ZOTUs using QIIME 2 and the Greengenes database and cross-checked with the NCBI nr/nt database. Bacterial composition of all WWTPs and treatment stages (influent, intermediate and effluent) were dominated by Proteobacteria (29.0-87.4%), particularly Betaproteobacteria (9.0-53.5%) and Gammaproteobacteria (8.6-34.6%). Nitrifying bacteria (Nitrospira spp.) were found only in the intermediate and effluent of the oxidation ditch WWTP, and denitrifying and floc-forming bacteria were detected in all WWTPs, particularly from the families Comamonadaceae and Rhodocyclales. Twelve pathogens were assigned taxonomy by the Greengenes database, but comparison of sequences from genera and families known to contain pathogens to the NCBI nr/nt database showed that only three pathogens (Arcobacter venerupis, Laribacter hongkongensis and Neisseria canis) could be identified in the dataset at the V4 region. Importantly, Enterobacteriaceae genera could not be differentiated. Family level taxa assigned by Greengenes database agreed with NCBI nr/nt in most cases, however, BLAST analyses revealed erroneous taxa in Greengenes database. This study highlights the importance of validating taxonomy of NGS sequences with databases such as NCBI nr/nt, and recommends including the V3 region of 16S in future short amplicon NGS studies that aim to identify bacterial enteric pathogens, as this will improve taxonomic resolution of most, but not all, Enterobacteriaceae species.

Cross-Domain and Viral Interactions in the Microbiome

The importance of the microbiome to human health is increasingly recognized and has become a major focus of recent research. However, much of the work has focused on a few aspects, particularly the bacterial component of the microbiome, most frequently in the gastrointestinal tract. Yet humans and other animals can be colonized by a wide array of organisms spanning all domains of life, including bacteria and archaea, unicellular eukaryotes such as fungi, multicellular eukaryotes such as helminths, and viruses. As they share the same host niches, they can compete with, synergize with, and antagonize each other, with potential impacts on their host. Here, we discuss these major groups making up the human microbiome, with a focus on how they interact with each other and their multicellular host.

Unraveling the hidden universe of small proteins in bacterial genomes

Identification of small open reading frames (smORFs) encoding small proteins (≤ 100 amino acids; SEPs) is a challenge in the fields of genome annotation and protein discovery. Here, by combining a novel bioinformatics tool (RanSEPs) with “‐omics” approaches, we were able to describe 109 bacterial small ORFomes. Predictions were first validated by performing an exhaustive search of SEPs present in Mycoplasma pneumoniae proteome via mass spectrometry, which illustrated the limitations of shotgun approaches. Then, RanSEPs predictions were validated and compared with other tools using proteomic datasets from different bacterial species and SEPs from the literature. We found that up to 16 ± 9% of proteins in an organism could be classified as SEPs. Integration of RanSEPs predictions with transcriptomics data showed that some annotated non‐coding RNAs could in fact encode for SEPs. A functional study of SEPs highlighted an enrichment in the membrane, translation, metabolism, and nucleotide‐binding categories. Additionally, 9.7% of the SEPs included a N‐terminus predicted signal peptide. We envision RanSEPs as a tool to unmask the hidden universe of small bacterial proteins.

Next-Generation Sequencing and Its Application: Empowering in Public Health Beyond Reality

Next-generation sequencing has the ability to revolutionize almost all fields of biological science. It has drastically reduced the cost of sequencing. This allows us to study the whole genome or part of the genome to understand how the cellular functions are governed by the genetic code. The data obtained in huge quantity from sequencing upon analysis gives an insight to understand the mechanism of pathogen biology, virulence, and phenomenon of bacterial resistance, which helps in investigating the outbreak. This ultimately helps in the development of therapies for public health welfare against human pathogen and diagnostic reagents for the screening. This chapter includes the basic of Sanger’s method of DNA sequencing and next-generation sequencing, different available platforms for sequencing with their advantages, and limitations and their chemistry with an overview of downstream data analysis. Furthermore, the breadth of applications of high-throughput NGS technology for human health has been discussed.

The global catalogue of microorganisms 10K type strain sequencing project: closing the genomic gaps for the validly published prokaryotic and fungi species

Genomic information is essential for taxonomic, phylogenetic, and functional studies to comprehensively decipher the characteristics of microorganisms, to explore microbiomes through metagenomics, and to answer fundamental questions of nature and human life. However, large gaps remain in the available genomic sequencing information published for bacterial and archaeal species, and the gaps are even larger for fungal type strains. The Global Catalogue of Microorganisms (GCM) leads an internationally coordinated effort to sequence type strains and close gaps in the genomic maps of microorganisms. Hence, the GCM aims to promote research by deep-mining genomic data.

Proteomics and the microbiome: pitfalls and potential

Introduction: Human symbiotic microbiota are now known to play important roles in human health and disease. Significant progress in our understanding of the human microbiome has been driven by recent technological advances in the fields of genomics, transcriptomics, and proteomics. As a complementary method to metagenomics, proteomics is enabling detailed protein profiling of the microbiome to decipher its structure and function and to analyze its relationship with the human body. Fecal proteomics is being increasingly applied to discover and validate potential health and disease biomarkers, and Therapeutic Goods Administration (TGA)-approved instrumentation and a range of clinical assays are being developed that will collectively play key roles in advancing personalized medicine. Areas covered: This review will introduce the complexity of the microbiome and its role in health and disease (in particular the gastrointestinal tract or gut microbiome), discuss current genomic and proteomic methods for studying this system, including the discovery of potential biomarkers, and outline the development of clinically accepted protocols leading to personalized medicine. Expert commentary: Recognition of the important role the microbiome plays in both health and disease is driving current research in this key area. A proteogenomics approach will be essential to unravel the biologies underlying this complex network.

An efficient classification algorithm for NGS data based on text similarity

With the advancement of high-throughput sequencing technologies, the amount of available sequencing data is growing at a pace that has now begun to greatly challenge the data processing and storage capacities of modern computer systems. Removing redundancy from such data by clustering could be crucial for reducing memory, disk space and running time consumption. In addition, it also has good performance on reducing dataset noise in some analysis applications. In this study, we propose a high-performance short sequence classification algorithm (HSC) for next generation sequencing (NGS) data based on efficient hash function and text similarity. First, HSC converts all reads into k-mers, then it forms a unique k-mer set by merging the duplicated and reverse complementary elements. Second, all unique k-mers are stored in a hash table, where the k-mer string is stored in the key field, and the ID of the reads containing the k-mer are stored in the value field. Third, each hash unit is transformed into a short text consisting of reads. Fourth, texts that satisfy the similarity threshold are combined into a long text, the merge operation is executed iteratively until there is no text that satisfies the merge condition. Finally, the long text is transformed into a cluster consisting of reads. We tested HSC using five real datasets. The experimental results showed that HSC cluster 100 million short reads within 2 hours, and it has excellent performance in reducing memory consumption. Compared to existing methods, HSC is much faster than other tools, it can easily handle tens of millions of sequences. In addition, when HSC is used as a preprocessing tool to produce assembly data, the memory and time consumption of the assembler is greatly reduced. It can help the assembler to achieve better assemblies in terms of N50, NA50 and genome fraction.

Comparison of normalization methods for the analysis of metagenomic gene abundance data

Background

In shotgun metagenomics, microbial communities are studied through direct sequencing of DNA without any prior cultivation. By comparing gene abundances estimated from the generated sequencing reads, functional differences between the communities can be identified. However, gene abundance data is affected by high levels of systematic variability, which can greatly reduce the statistical power and introduce false positives. Normalization, which is the process where systematic variability is identified and removed, is therefore a vital part of the data analysis. A wide range of normalization methods for high-dimensional count data has been proposed but their performance on the analysis of shotgun metagenomic data has not been evaluated.

Results

Here, we present a systematic evaluation of nine normalization methods for gene abundance data. The methods were evaluated through resampling of three comprehensive datasets, creating a realistic setting that preserved the unique characteristics of metagenomic data. Performance was measured in terms of the methods ability to identify differentially abundant genes (DAGs), correctly calculate unbiased p-values and control the false discovery rate (FDR). Our results showed that the choice of normalization method has a large impact on the end results. When the DAGs were asymmetrically present between the experimental conditions, many normalization methods had a reduced true positive rate (TPR) and a high false positive rate (FPR). The methods trimmed mean of M-values (TMM) and relative log expression (RLE) had the overall highest performance and are therefore recommended for the analysis of gene abundance data. For larger sample sizes, CSS also showed satisfactory performance.

Conclusions

This study emphasizes the importance of selecting a suitable normalization methods in the analysis of data from shotgun metagenomics. Our results also demonstrate that improper methods may result in unacceptably high levels of false positives, which in turn may lead to incorrect or obfuscated biological interpretation.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4637-6) contains supplementary material, which is available to authorized users.

Manipulating the microbiome: evolution of a strategy to prevent S. aureus disease in children

Hospitalized infants have the highest rates of invasive Staphylococcus aureus disease of any population and infection control strategies such as decolonization have been insufficient. For decades, researchers began studying the microbiome in search of new prevention strategies. The resident microbiota was found to be closely associated with susceptibility and at times, resistance to S. aureus colonization. The evolution of nucleic acid based techniques has enhanced our understanding of the complex relationship between the nasal microbiota and S. aureus colonization. We review what is known about bacterial communities in the nasal cavity of infants and discuss how future microbiome studies may help identify novel interventions to protect high-risk infants from S. aureus disease.

Recent insights into the tick microbiome gained through next-generation sequencing

The tick microbiome comprises communities of microorganisms, including viruses, bacteria and eukaryotes, and is being elucidated through modern molecular techniques. The advent of next-generation sequencing (NGS) technologies has enabled the genes and genomes within these microbial communities to be explored in a rapid and cost-effective manner. The advantages of using NGS to investigate microbiomes surpass the traditional non-molecular methods that are limited in their sensitivity, and conventional molecular approaches that are limited in their scalability. In recent years the number of studies using NGS to investigate the microbial diversity and composition of ticks has expanded. Here, we provide a review of NGS strategies for tick microbiome studies and discuss the recent findings from tick NGS investigations, including the bacterial diversity and composition, influential factors, and implications of the tick microbiome.

Microbiota-Brain-Gut Axis and Neurodegenerative Diseases

PURPOSE OF REVIEW

The purposes of this review were as follows: first, to provide an overview of the gut microbiota and its interactions with the gut and the central nervous system (the microbiota-gut-brain axis) in health, second, to review the relevance of this axis to the pathogenesis of neurodegenerative diseases, such as Parkinson's disease, and, finally, to assess the potential for microbiota-targeted therapies.

RECENT FINDINGS

Work on animal models has established the microbiota-gut-brain axis as a real phenomenon; to date, the evidence for its operation in man has been limited and has been confronted by considerable logistical challenges. Animal and translational models have incriminated a disturbed gut microbiota in a number of CNS disorders, including Parkinson's disease; data from human studies is scanty. While a theoretical basis can be developed for the use of microbiota-directed therapies in neurodegenerative disorders, support is yet to come from high-quality clinical trials. In theory, a role for the microbiota-gut-brain axis is highly plausible; clinical confirmation is awaited.

Development and Use of a Real-Time Quantitative PCR Method for Detecting and Quantifying Equol-Producing Bacteria in Human Faecal Samples and Slurry Cultures

This work introduces a novel real-time quantitative PCR (qPCR) protocol for detecting and quantifying equol-producing bacteria. To this end, two sets of primers targeting the dihydrodaidzein reductase (ddr) and tetrahydrodaidzein reductase (tdr) genes, which are involved in the synthesis of equol, were designed. The primers showed high specificity and sensitivity when used to examine DNA from control bacteria, such as Slackia isoflavoniconvertens, Slackia equolifaciens, Asaccharobacter celatus, Adlercreutzia equolifaciens, and Enterorhabdus mucosicola. To demonstrate the validity and reliability of the protocol, it was used to detect and quantify equol-producing bacteria in human faecal samples and their derived slurry cultures. These samples were provided by 18 menopausal women under treatment of menopause symptoms with a soy isoflavone concentrate, among whom three were known to be equol-producers given the prior detection of the molecule in their urine. The tdr gene was detected in the faeces of all these equol-producing women at about 4–5 log₁₀ copies per gram of faeces. In contrast, the ddr gene was only amplified in the faecal samples of two of these three women, suggesting the presence in the non-amplified sample of reductase genes unrelated to those known to be involved in equol formation and used for primer design in this study. When tdr and ddr were present in the same sample, similar copy numbers of the two genes were recorded. However, no significant increase in the copy number of equol-related genes along isoflavone treatment was observed. Surprisingly, positive amplification for both tdr and ddr genes was obtained in faecal samples and derived slurry cultures from two non-equol producing women, suggesting the genes could be non-functional or the daidzein metabolized to other compounds in samples from these two women. This novel qPCR tool provides a technique for monitoring gut microbes that produce equol in humans. Monitoring equol-producing bacteria in the human gut could provide a means of evaluating strategies aimed at increasing the endogenous formation of this bioactive compound.

Optimizing methods and dodging pitfalls in microbiome research

Research on the human microbiome has yielded numerous insights into health and disease, but also has resulted in a wealth of experimental artifacts. Here, we present suggestions for optimizing experimental design and avoiding known pitfalls, organized in the typical order in which studies are carried out. We first review best practices in experimental design and introduce common confounders such as age, diet, antibiotic use, pet ownership, longitudinal instability, and microbial sharing during cohousing in animal studies. Typically, samples will need to be stored, so we provide data on best practices for several sample types. We then discuss design and analysis of positive and negative controls, which should always be run with experimental samples. We introduce a convenient set of non-biological DNA sequences that can be useful as positive controls for high-volume analysis. Careful analysis of negative and positive controls is particularly important in studies of samples with low microbial biomass, where contamination can comprise most or all of a sample. Lastly, we summarize approaches to enhancing experimental robustness by careful control of multiple comparisons and to comparing discovery and validation cohorts. We hope the experimental tactics summarized here will help researchers in this exciting field advance their studies efficiently while avoiding errors.

HirBin: high-resolution identification of differentially abundant functions in metagenomes

Background

Gene-centric analysis of metagenomics data provides information about the biochemical functions present in a microbiome under a certain condition. The ability to identify significant differences in functions between metagenomes is dependent on accurate classification and quantification of the sequence reads (binning). However, biological effects acting on specific functions may be overlooked if the classes are too general.

Methods

Here we introduce High-Resolution Binning (HirBin), a new method for gene-centric analysis of metagenomes. HirBin combines supervised annotation with unsupervised clustering to bin sequence reads at a higher resolution. The supervised annotation is performed by matching sequence fragments to genes using well-established protein domains, such as TIGRFAM, PFAM or COGs, followed by unsupervised clustering where each functional domain is further divided into sub-bins based on sequence similarity. Finally, differential abundance of the sub-bins is statistically assessed.

Results

We show that HirBin is able to identify biological effects that are only present at more specific functional levels. Furthermore we show that changes affecting more specific functional levels are often diluted at the more general level and therefore overlooked when analyzed using standard binning approaches.

Conclusions

HirBin improves the resolution of the gene-centric analysis of metagenomes and facilitates the biological interpretation of the results. HirBin is implemented as a Python package and is freely available for download at http://bioinformatics.math.chalmers.se/hirbin.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3686-6) contains supplementary material, which is available to authorized users.

Basic Definitions and Concepts: Organization of the Gut Microbiome

New claims are frequently made for a role for the microbiome in a disease or disorder previously considered remote from the gut. The microbiome has been linked to such seemingly unrelated entities as depression, anorexia nervosa, autism, Parkinson disease, allergy, and asthma. Although many of these proposals have been based on animal studies, explorations of the microbiome in human disease continue to proliferate, facilitated by technologies that provide a detailed assessment of the microbial inhabitants of our gastrointestinal tract and their biological activities and metabolic products. With these technologies come new terminologies, which are identified in this article.

Probing the diversity of healthy oral microbiome with bioinformatics approaches

The human oral cavity contains a highly personalized microbiome essential to maintaining health, but capable of causing oral and systemic diseases. Thus, an in-depth definition of “healthy oral microbiome” is critical to understanding variations in disease states from preclinical conditions, and disease onset through progressive states of disease. With rapid advances in DNA sequencing and analytical technologies, population-based studies have documented the range and diversity of both taxonomic compositions and functional potentials observed in the oral microbiome in healthy individuals. Besides factors specific to the host, such as age and race/ethnicity, environmental factors also appear to contribute to the variability of the healthy oral microbiome. Here, we review bioinformatic techniques for metagenomic datasets, including their strengths and limitations. In addition, we summarize the interpersonal and intrapersonal diversity of the oral microbiome, taking into consideration the recent large-scale and longitudinal studies, including the Human Microbiome Project.

Understanding the role of the microbiome in chronic obstructive pulmonary disease: principles, challenges, and future directions

In the past several years, advances in sequencing platforms and bioinformatics have transformed our understanding of the relationship between microbial ecology and human health. Both the normal and diseased lung are host to hundreds of bacterial genera, blurring the lines between "colonization" and "infection". However, whereas in health the respiratory microbiome is determined primarily by the dynamic balance of immigration and elimination, in chronic disease conditions become much more favorable for the reproduction of resident bacteria. Recent studies demonstrate that the microbiota of the chronic obstructive pulmonary disease (COPD) lung differ from the healthy lung although significant intrasubject and intersubject heterogeneity are still present with variation impacted by factors such as disease stage and inhaled medications. Changes in the relative abundance of specific bacterial taxa during COPD exacerbations have also been noted although further longitudinal analyses are needed to ascertain the malleability and resilience of this ecological system and its role in the occurrence and frequency of exacerbations. Whether patients with a "frequent exacerbator" phenotype possess specific or greater alterations in their airway microbiome that predispose them to recurrent exacerbations as compared with nonfrequent exacerbators needs to be determined. Although recent data suggest that the presence of bacteria has the potential to influence the host immune response, a key challenge in the next few years will be to continue to move beyond descriptive studies to define the clinical relevance of differences in lung microbiota associated with COPD.

The gut microbiota: a puppet master in the pathogenesis of endometriosis?

Endometriosis is a frequent gynecologic disease with a complex, multifactorial cause. It is characterized by the cyclic estrogen-driven proliferation and bleeding of endometriotic lesions (ie, ectopic endometrial glands and stroma) outside the uterus. These lesions induce a chronic activation of the innate immune system within the peritoneal cavity that is associated with the release of various inflammatory cytokines and angiogenic growth factors into the peritoneal fluid. This stimulates angiogenesis and the further spread of the lesions and triggers the typical pain that is symptomatic of the disease. Moreover, circulating stem and progenitor cells are recruited into the ectopic endometrial tissue and contribute to its growth and vascularization. In recent years, an increasing number of studies have indicated that the gut microbiota is not only essential for a physiologic gastrointestinal function but acts as a central regulator of a variety of inflammatory and proliferative conditions. Besides, the gut flora affects estrogen metabolism and stem-cell homeostasis. Based on these findings, we hypothesize that the gut microbiota may be involved crucially in the onset and progression of endometriosis. In the future, this novel view of the pathogenesis of endometriosis may be verified by analysis of the development of endometriotic lesions in animal models with a defined composition of the gut microbiota and by investigation of the microbiota of patients with endometriosis with modern next-generation sequencing tools. This could open the door for completely new preventive, diagnostic, and therapeutic approaches for endometriosis.