High-resolution characterization of the human microbiome

Distinct anal microbiome is correlated with anal cancer precursors in MSM with HIV

OBJECTIVES

Anal cancer risk is elevated in MSM with HIV (MSMWH). Anal high-risk human papillomavirus (hr-HPV) infection is necessary but insufficient to develop high-grade squamous intraepithelial lesion (HSIL), the anal cancer precursor, suggesting additional factors. We sought to determine whether the microbiome of the anal canal is distinct by comparing it with the microbiome of stool. We also sought to determine whether changes in the anal microbiome are associated with HSIL among MSMWH.

DESIGN

Cross-sectional comparison of the microbiome of the anal canal with the microbiome of stool in MSMWH and cross-sectional comparison of the anal microbiome of MSMWH with anal HSIL with the anal microbiome of MSMWH without anal HSIL.

METHODS

Sterile swabs were used to sample the anus of MSMWH for microbiome and HPV testing, followed by high-resolution anoscopy. Stool samples were mailed from home. 16S sequencing was used for bacterial identification. Measures of alpha diversity, beta diversity, and differential abundance analysis were used to compare samples.

RESULTS

One hundred sixty-six anal samples and 103 matching stool samples were sequenced. Beta diversity showed clustering of stool and anal samples. Of hr-HPV-positive MSMWH, 31 had HSIL and 13 had no SIL. Comparison of the microbiome between these revealed 28 different species. The highest-fold enrichment among MSMWH/hr-HPV/HSIL included pro-inflammatory and carcinogenic Prevotella, Parasuterella, Hungatella, Sneathia, and Fusobacterium species. The anti-inflammatory Anaerostipes caccae showed the greatest reduction among MSMWH/hr-HPV/HSIL.

CONCLUSION

The anal microbiome is distinct from stool. A pro-inflammatory and carcinogenic environment may be associated with anal HSIL.

PUPpy: a primer design pipeline for substrain-level microbial detection and absolute quantification

Characterizing microbial communities at high resolution and with absolute quantification is crucial to unravel the complexity and diversity of microbial ecosystems. This can be achieved with PCR assays, which enable highly selective detection and absolute quantification of microbial DNA. However, a major challenge that has hindered PCR applications in microbiome research is the design of highly specific primer sets that exclusively amplify intended targets. Here, we introduce Phylogenetically Unique Primers in python (PUPpy), a fully automated pipeline to design microbe- and group-specific primers within a given microbial community. PUPpy can be executed from a user-friendly graphical user interface, or two simple terminal commands, and it only requires coding sequence files of the community members as input. PUPpy-designed primers enable the detection of individual microbes and quantification of absolute microbial abundance in defined communities below the strain level. We experimentally evaluated the performance of PUPpy-designed primers using two bacterial communities as benchmarks. Each community comprises 10 members, exhibiting a range of genetic similarities that spanned from different phyla to substrains. PUPpy-designed primers also enable the detection of groups of bacteria in an undefined community, such as the detection of a gut bacterial family in a complex stool microbiota sample. Taxon-specific primers designed with PUPpy showed 100% specificity to their intended targets, without unintended amplification, in each community tested. Lastly, we show the absolute quantification of microbial abundance using PUPpy-designed primers in droplet digital PCR, benchmarked against 16S rRNA and shotgun sequencing. Our data shows that PUPpy-designed microbe-specific primers can be used to quantify substrain-level absolute counts, providing more resolved and accurate quantification in defined communities than short-read 16S rRNA and shotgun sequencing.

IMPORTANCE

Profiling microbial communities at high resolution and with absolute quantification is essential to uncover hidden ecological interactions within microbial ecosystems. Nevertheless, achieving resolved and quantitative investigations has been elusive due to methodological limitations in distinguishing and quantifying highly related microbes. Here, we describe Phylogenetically Unique Primers in python (PUPpy), an automated computational pipeline to design taxon-specific primers within defined microbial communities. Taxon-specific primers can be used to selectively detect and quantify individual microbes and larger taxa within a microbial community. PUPpy achieves substrain-level specificity without the need for computationally intensive databases and prioritizes user-friendliness by enabling both terminal and graphical user interface applications. Altogether, PUPpy enables fast, inexpensive, and highly accurate perspectives into microbial ecosystems, supporting the characterization of bacterial communities in both in vitro and complex microbiota settings.

A review of machine learning methods for cancer characterization from microbiome data

Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

Unlocking the Potential of Brewers' Spent Grain: A Sustainable Model to Use Beer for Better Outcome in Chronic Kidney Disease

The rising global incidence of chronic inflammatory diseases calls for innovative and sustainable medical solutions. Brewers' spent grain (BSG), a byproduct of beer production, presents a unique opportunity in this regard. This review explores the multifaceted health benefits of BSG, with a focus on managing chronic kidney disease (CKD). BSG is identified as a potent prebiotic with potential as a therapeutic agent in CKD. We emphasize the role of gut dysbiosis in CKD and discuss how BSG could help mitigate metabolic derangements resulting from dysbiosis and CKD. Fermentation of BSG further enhances its positive impact on gut health. Incorporating fermented BSG as a key component in preventive health care could promote a more sustainable and healthier future. By optimizing the use of this typically discarded byproduct, we can align proactive health-care strategies with responsible resource management, benefiting both people and the environment.

YACHT: an ANI-based statistical test to detect microbial presence/absence in a metagenomic sample

MOTIVATION

In metagenomics, the study of environmentally associated microbial communities from their sampled DNA, one of the most fundamental computational tasks is that of determining which genomes from a reference database are present or absent in a given sample metagenome. Existing tools generally return point estimates, with no associated confidence or uncertainty associated with it. This has led to practitioners experiencing difficulty when interpreting the results from these tools, particularly for low-abundance organisms as these often reside in the "noisy tail" of incorrect predictions. Furthermore, few tools account for the fact that reference databases are often incomplete and rarely, if ever, contain exact replicas of genomes present in an environmentally derived metagenome.

RESULTS

We present solutions for these issues by introducing the algorithm YACHT: Yes/No Answers to Community membership via Hypothesis Testing. This approach introduces a statistical framework that accounts for sequence divergence between the reference and sample genomes, in terms of ANI, as well as incomplete sequencing depth, thus providing a hypothesis test for determining the presence or absence of a reference genome in a sample. After introducing our approach, we quantify its statistical power and how this changes with varying parameters. Subsequently, we perform extensive experiments using both simulated and real data to confirm the accuracy and scalability of this approach.

AVAILABILITY AND IMPLEMENTATION

The source code implementing this approach is available via Conda and at https://github.com/KoslickiLab/YACHT. We also provide the code for reproducing experiments at https://github.com/KoslickiLab/YACHT-reproducibles.

YACHT: an ANI-based statistical test to detect microbial presence/absence in a metagenomic sample

In metagenomics, the study of environmentally associated microbial communities from their sampled DNA, one of the most fundamental computational tasks is that of determining which genomes from a reference database are present or absent in a given sample metagenome. While tools exist to answer this question, all existing approaches to date return point estimates, with no associated confidence or uncertainty associated with it. This has led to practitioners experiencing difficulty when interpreting the results from these tools, particularly for low abundance organisms as these often reside in the "noisy tail" of incorrect predictions. Furthermore, no tools to date account for the fact that reference databases are often incomplete and rarely, if ever, contain exact replicas of genomes present in an environmentally derived metagenome. In this work, we present solutions for these issues by introducing the algorithm YACHT: Yes/No Answers to Community membership via Hypothesis Testing. This approach introduces a statistical framework that accounts for sequence divergence between the reference and sample genomes, in terms of average nucleotide identity, as well as incomplete sequencing depth, thus providing a hypothesis test for determining the presence or absence of a reference genome in a sample. After introducing our approach, we quantify its statistical power as well as quantify theoretically how this changes with varying parameters. Subsequently, we perform extensive experiments using both simulated and real data to confirm the accuracy and scalability of this approach. Code implementing this approach, as well as all experiments performed, is available at https://github.com/KoslickiLab/YACHT.

A Review on the Nasal Microbiome and Various Disease Conditions for Newer Approaches to Treatments

Introduction: Commensal bacteria have always played a significant role in the maintenance of health and disease but are being unravelled only recently. Studies suggest that the nasal microbiome has a significant role in the development of various disease conditions. Search engines were used for searching articles having a nasal microbiome and disease correlation. In olfactory dysfunction, dysbiosis of the microbiome may have a significant role to play in the pathogenesis. The nasal microbiome influences the phenotype of CRS and is also capable of modulating the immune response and plays a role in polyp formation. Microbiome dysbiosis has a pivotal role in the development of Allergic Rhinitis; but, yet known how is this role played. The nasal microbiome has a close association with the severity and phenotype of asthma. They contribute significantly to the onset, severity, and development of asthma. The nasal microbiome has a significant impact on the immunity and protection of its host. The nasal microbiome has been a stimulus in the development of Otitis Media and its manifestations. Studies suggest that the resident nasal microbiome is responsible for the initiation of neurodegenerative diseases like Parkinson's Disease.Materials and Methods: Literature search from PubMed, Medline, and Google with the Mesh terms: nasal microbiome AND diseases. Conclusion: With increasing evidence on the role of the nasal microbiome on various diseases, it would be interesting to see how this microbiome can be modulated by pro/pre/post biotics to prevent a disease or the severity of illness.

Multi-'omics of gut microbiome-host interactions in short- and long-term myalgic encephalomyelitis/chronic fatigue syndrome patients

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, debilitating disorder manifesting as severe fatigue and post-exertional malaise. The etiology of ME/CFS remains elusive. Here, we present a deep metagenomic analysis of stool combined with plasma metabolomics and clinical phenotyping of two ME/CFS cohorts with short-term (<4 years, n = 75) or long-term disease (>10 years, n = 79) compared with healthy controls (n = 79). First, we describe microbial and metabolomic dysbiosis in ME/CFS patients. Short-term patients showed significant microbial dysbiosis, while long-term patients had largely resolved microbial dysbiosis but had metabolic and clinical aberrations. Second, we identified phenotypic, microbial, and metabolic biomarkers specific to patient cohorts. These revealed potential functional mechanisms underlying disease onset and duration, including reduced microbial butyrate biosynthesis and a reduction in plasma butyrate, bile acids, and benzoate. In addition to the insights derived, our data represent an important resource to facilitate mechanistic hypotheses of host-microbiome interactions in ME/CFS.

Biochemical and Metabolical Pathways Associated with Microbiota-Derived Butyrate in Colorectal Cancer and Omega-3 Fatty Acids Implications: A Narrative Review

Knowledge regarding the influence of the microbial community in cancer promotion or protection has expanded even more through the study of bacterial metabolic products and how they can modulate cancer risk, which represents an extremely challenging approach for the relationship between intestinal microbiota and colorectal cancer (CRC). This review discusses research progress on the effect of bacterial dysbiosis from a metabolic point of view, particularly on the biochemical mechanisms of butyrate, one of the main short chain fatty acids (SCFAs) with anti-inflammatory and anti-tumor properties in CRC. Increased daily intake of omega-3 polyunsaturated fatty acids (PUFAs) significantly increases the density of bacteria that are known to produce butyrate. Omega-3 PUFAs have been proposed as a treatment to prevent gut microbiota dysregulation and lower the risk or progression of CRC.

Characteristics of the sputum microbiome in COPD exacerbations and correlations between clinical indices

Background

Chronic obstructive pulmonary disease (COPD) is a prevalent, progressive respiratory disease, and acute exacerbations of COPD (AECOPD) can accelerate the deterioration of the disease. Increasing evidence suggests that airway bacterial dysbiosis is associated with AECOPD. However, the exact relationship between changes in the sputum microbiome during AECOPD and clinical indices remains unclear.

Methods

In this study, a total of 76 sputum samples were collected from patients with AECOPD (n = 28), stable COPD (n = 23), recovery (n = 15) and healthy controls (HCs; n = 10). The sputum microbiome profile was analysed by sequencing the V3‑V4 amplicon of the 16S rRNA (ribosomal RNA) gene.

Results

The bacterial diversity (Shannon and Simpson’s index) was found to be significantly decreased in the AECOPD and recovery groups when compared to that in the stable COPD and HC groups. The most dominant phylum identified in the sputum samples of AECOPD patients was Proteobacteria, accounting for 30% of the microbiome. Compared to the stable COPD groups, the relative abundances of Firmicutes and Bacteroidetes were decreased, whereas those of Proteobacteria and Actinobacteria were increased in AECOPD patients. Furthermore, discriminative bacteria, such as Haemophilus, were identified as being specific taxa in AECOPD patients. Functional analysis showed that genes involved in membrane transport and signal transduction metabolism were enriched in the AECOPD group. Importantly, the proportions of Veillonella were positively correlated with lung function, and Staphylococcus was positively correlated with inflammatory indices.

Conclusion

Our study revealed variations in the sputum microbiome of AECOPD (based on composition and function) in a Chinese cohort and highlighted its correlation to clinical indices. These results indicated that microbial dysbiosis may contribute to disease progression and provide microbial biomarkers for the diagnosis of AECOPD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03278-x.

Bacterial keratitis: identifying the areas of clinical uncertainty

Bacterial keratitis is a common corneal infection that is treated with topical antimicrobials. By the time of presentation there may already be severe visual loss from corneal ulceration and opacity, which may persist despite treatment. There are significant differences in the associated risk factors and the bacterial isolates between high income and low- or middle-income countries, so that general management guidelines may not be appropriate. Although the diagnosis of bacterial keratitis may seem intuitive there are multiple uncertainties about the criteria that are used, which impacts the interpretation of investigations and recruitment to clinical studies. Importantly, the concept that bacterial keratitis can only be confirmed by culture ignores the approximately 50% of cases clinically consistent with bacterial keratitis in which investigations are negative. The aetiology of these culture-negative cases is unknown. Currently, the estimation of bacterial susceptibility to antimicrobials is based on data from systemic administration and achievable serum or tissue concentrations, rather than relevant corneal concentrations and biological activity in the cornea. The provision to the clinician of minimum inhibitory concentrations of the antimicrobials for the isolated bacteria would be an important step forward. An increase in the prevalence of antimicrobial resistance is a concern, but the effect this has on disease outcomes is yet unclear. Virulence factors are not routinely assessed although they may affect the pathogenicity of bacteria within species and affect outcomes. New technologies have been developed to detect and kill bacteria, and their application to bacterial keratitis is discussed. In this review we present the multiple areas of clinical uncertainty that hamper research and the clinical management of bacterial keratitis, and we address some of the assumptions and dogma that have become established in the literature.

Accurate quantification of bacterial abundance in metagenomic DNAs accounting for variable DNA integrity levels

The quantification of the total microbial content in metagenomic samples is critical for investigating the interplay between the microbiome and its host, as well as for assessing the accuracy and precision of the relative microbial composition which can be strongly biased in low microbial biomass samples. In the present study, we demonstrate that digital droplet PCR (ddPCR) can provide accurate quantification of the total copy number of the 16S rRNA gene, the gene usually exploited for assessing total bacterial abundance in metagenomic DNA samples. Notably, using DNA templates with different integrity levels, as measured by the DNA integrity number (DIN), we demonstrated that 16S rRNA copy number quantification is strongly affected by DNA quality and determined a precise correlation between quantification underestimation and DNA degradation levels. Therefore, we propose an input DNA mass correction, according to the observed DIN value, which could prevent inaccurate quantification of 16S copy number in degraded metagenomic DNAs. Our results highlight that a preliminary evaluation of the metagenomic DNA integrity should be considered before performing metagenomic analyses of different samples, both for the assessment of the reliability of observed differential abundances in different conditions and to obtain significant functional insights.

Review: The Nose as a Route for Therapy. Part 2 Immunotherapy

The nose provides a route of access to the body for inhalants and fluids. Unsurprisingly it has a strong immune defense system, with involvement of innate (e.g., epithelial barrier, muco- ciliary clearance, nasal secretions with interferons, lysozyme, nitric oxide) and acquired (e.g., secreted immunoglobulins, lymphocytes) arms. The lattice network of dendritic cells surrounding the nostrils allows rapid uptake and sampling of molecules able to negotiate the epithelial barrier. Despite this many respiratory infections, including SARS-CoV2, are initiated through nasal mucosal contact, and the nasal mucosa is a significant "reservoir" for microbes including Streptococcus pneumoniae, Neisseria meningitidis and SARS -CoV-2. This review includes consideration of the augmentation of immune defense by the nasal application of interferons, then the reduction of unnecessary inflammation and infection by alteration of the nasal microbiome. The nasal mucosa and associated lymphoid tissue (nasopharynx-associated lymphoid tissue, NALT) provides an important site for vaccine delivery, with cold-adapted live influenza strains (LAIV), which replicate intranasally, resulting in an immune response without significant clinical symptoms, being the most successful thus far. Finally, the clever intranasal application of antibodies bispecific for allergens and Intercellular Adhesion Molecule 1 (ICAM-1) as a topical treatment for allergic and RV-induced rhinitis is explained.

A comprehensive evaluation of binning methods to recover human gut microbial species from a non-redundant reference gene catalog

The human gut microbiota performs functions that are essential for the maintenance of the host physiology. However, characterizing the functioning of microbial communities in relation to the host remains challenging in reference-based metagenomic analyses. Indeed, as taxonomic and functional analyses are performed independently, the link between genes and species remains unclear. Although a first set of species-level bins was built by clustering co-abundant genes, no reference bin set is established on the most used gut microbiota catalog, the Integrated Gene Catalog (IGC). With the aim to identify the best suitable method to group the IGC genes, we benchmarked nine taxonomy-independent binners implementing abundance-based, hybrid and integrative approaches. To this purpose, we designed a simulated non-redundant gene catalog (SGC) and computed adapted assessment metrics. Overall, the best trade-off between the main metrics is reached by an integrative binner. For each approach, we then compared the results of the best-performing binner with our expected community structures and applied the method to the IGC. The three approaches are distinguished by specific advantages, and by inherent or scalability limitations. Hybrid and integrative binners show promising and potentially complementary results but require improvements to be used on the IGC to recover human gut microbial species.

Relationship between dental and periodontal health status and the salivary microbiome: bacterial diversity, co-occurrence networks and predictive models

The present study used 16S rRNA gene amplicon sequencing to assess the impact on salivary microbiome of different grades of dental and periodontal disease and the combination of both (hereinafter referred to as oral disease), in terms of bacterial diversity, co-occurrence network patterns and predictive models. Our scale of overall oral health was used to produce a convenience sample of 81 patients from 270 who were initially recruited. Saliva samples were collected from each participant. Sequencing was performed in Illumina MiSeq with 2 × 300 bp reads, while the raw reads were processed according to the Mothur pipeline. The statistical analysis of the 16S rDNA sequencing data at the species level was conducted using the phyloseq, DESeq2, Microbiome, SpiecEasi, igraph, MixOmics packages. The simultaneous presence of dental and periodontal pathology has a potentiating effect on the richness and diversity of the salivary microbiota. The structure of the bacterial community in oral health differs from that present in dental, periodontal or oral disease, especially in high grades. Supragingival dental parameters influence the microbiota’s abundance more than subgingival periodontal parameters, with the former making a greater contribution to the impact that oral health has on the salivary microbiome. The possible keystone OTUs are different in the oral health and disease, and even these vary between dental and periodontal disease: half of them belongs to the core microbiome and are independent of the abundance parameters. The salivary microbiome, involving a considerable number of OTUs, shows an excellent discriminatory potential for distinguishing different grades of dental, periodontal or oral disease; considering the number of predictive OTUs, the best model is that which predicts the combined dental and periodontal status.

Can change in gut microbiota composition be used as a surrogate marker of treatment efficacy of ketogenic diet in patients with drug-resistant epilepsy?

To answer the question posed in the title of the manuscript, we critically examined the connection between ketogenic diet (KD), gut microbiota (GM), and epilepsy. We conclude that although the evidence for a KD-GM-epilepsy link is fairly robust in rodent epilepsy models, it is very hard to draw meaningful conclusions in humans. The limitations of human studies that have investigated the KD-microbiota-epilepsy relationship include small sample size, a heterogeneous patient population with regard to age and epilepsy type, failure to account for the effect of dietary habits, antiseizure drugs (ASDs) and comedications on GM composition, variability in the KD administered and in the duration of the intervention, and different approaches used in sequencing the microbiome. Although alteration in the GM composition may be a potential indicator of responsiveness/resistance to a KD, we need well-designed randomized case-control and cohort studies involving a large number of a fairly homogenous population of patients with epilepsy adjusted to their habitual dietary habits and region of residence before labeling it as a surrogate marker. Research in this direction may also help us to unravel the mysteries of GM-brain axis not only concerning epilepsy but also in other neurological diseases.

Variations in fecal microbial profiles of acute exacerbations and stable chronic obstructive pulmonary disease

AIM

Alterations in the respiratory and digestive tract microbiomes influence the occurrence and progression of chronic obstructive pulmonary disease (COPD). Here, we aimed to identify fecal microbiome profiles during COPD development.

METHODS

Fecal samples were collected from 29 COPD patients with acute exacerbation (AECOPD), 29 stable COPD patients, and 22 normal subjects (NS). The fecal microbial profiles were obtained using 16S rRNA gene sequencing.

KEY FINDINGS

The diversity and richness were lower and fewer variations in the taxonomic composition of fecal microbiota were observed in AECOPD patients than in stable COPD and NS. The relative abundances of Firmicutes and Actinobacteria were decreased, while those of Bacteroidetes and Proteobacteria were increased in AECOPD compared to COPD and NS. Among the top ten genera, the proportions of Lachnoclostridium and Parabacteroides significantly increased in AECOPD, whereas those of other genera decreased. Discriminative bacteria, such as p_Bacteroidetes, c_Bacteroidia, o_Bacteroidales, Lactobacillales, and Proteobacteria, were identified in AECOPD compared to stable COPD and NS. The weighted gene co-expression networks showed that Firmicutes and Actinobacteria were the main hub bacterial taxa related with lung function (FEV1% and FEV1/FVC%) and inflammatory indices (TNF-α, IL-6, IL-8, PCT, and CRP).

SIGNIFICANCE

These findings emphasized the changes in the abundance and composition of the fecal microbiome in stable COPD and AECOPD. Variations in fecal microbiota may be associated with COPD progression.

Nicotinamide riboside alleviates alcohol-induced depression-like behaviours in C57BL/6J mice by altering the intestinal microbiota associated with microglial activation and BDNF expression

The gut microbiota play an important role in many central nervous system diseases through the gut microbiota-brain axis. Recent studies suggest that nicotinamide riboside (NR) has neuroprotective properties. However, it is unknown whether NR can prevent or protect against alcohol-induced depression. Furthermore, it is unclear whether its therapeutic action involves changes in the composition of the gut microbiome. Here, we investigated the effects of NR in the mouse model of alcohol-induced depression. Treatment with NR improved the alcohol-induced depressive behaviour in mice. In addition, NR decreased the number of activated microglia in the hippocampus, and it reduced the levels of pro-inflammatory (IL-1β, IL-6, and TNF-α) and anti-inflammatory (IL-10 and TGF-β) cytokines in the brain of mice with alcohol-induced depression. Furthermore, NR significantly upregulated BDNF and diminished the inhibition of the AKT/GSK3β/β-catenin signalling pathway in the hippocampus of these mice. 16S rRNA sequencing revealed that, compared with control and NR-treated mice, the gut microbiome richness and composition were significantly altered in the depressed mice. Spearman's correlation analysis showed that differential gut bacterial genera correlated with the levels of inflammation-related cytokines and BDNF in the brain. After faecal microbiota transplantation, cognitive behaviours, microglial activity, levels of cytokines and BDNF, and activation state of the AKT/GSK3β/β-catenin signalling pathway (which is downstream of the BDNF receptor, TrkB) in recipient mice were similar to those in donor mice. Collectively, our findings show that NR dietary supplementation protects against alcohol-induced depression-like behaviours, possibly by altering the composition of the gut microbiota.

Predictive analysis methods for human microbiome data with application to Parkinson's disease

Microbiome data consists of operational taxonomic unit (OTU) counts characterized by zero-inflation, over-dispersion, and grouping structure among samples. Currently, statistical testing methods are commonly performed to identify OTUs that are associated with a phenotype. The limitations of statistical testing methods include that the validity of p-values/q-values depend sensitively on the correctness of models and that the statistical significance does not necessarily imply predictivity. Predictive analysis using methods such as LASSO is an alternative approach for identifying associated OTUs and for measuring the predictability of the phenotype variable with OTUs and other covariate variables. We investigate three strategies of performing predictive analysis: (1) LASSO: fitting a LASSO multinomial logistic regression model to all OTU counts with specific transformation; (2) screening+GLM: screening OTUs with q-values returned by fitting a GLMM to each OTU, then fitting a GLM model using a subset of selected OTUs; (3) screening+LASSO: fitting a LASSO to a subset of OTUs selected with GLMM. We have conducted empirical studies using three simulation datasets generated using Dirichlet-multinomial models and a real gut microbiome data related to Parkinson’s disease to investigate the performance of the three strategies for predictive analysis. Our simulation studies show that the predictive performance of LASSO with appropriate variable transformation works remarkably well on zero-inflated data. Our results of real data analysis show that Parkinson’s disease can be predicted based on selected OTUs after the binary transformation, age, and sex with high accuracy (Error Rate = 0.199, AUC = 0.872, AUPRC = 0.912). These results provide strong evidences of the relationship between Parkinson’s disease and the gut microbiome.

Prediction of advanced fibrosis in non-alcoholic fatty liver disease using gut microbiota-based approaches compared with simple non-invasive tools

Liver fibrosis is the major determinant of liver related complications in patients with non-alcoholic fatty liver disease (NAFLD). A gut microbiota signature has been explored to predict advanced fibrosis in NAFLD patients. The aim of this study was to validate and compare the diagnostic performance of gut microbiota-based approaches to simple non-invasive tools for the prediction of advanced fibrosis in NAFLD. 16S rRNA gene sequencing was performed in a cohort of 83 biopsy-proven NAFLD patients and 13 patients with non-invasively diagnosed NAFLD-cirrhosis. Random Forest models based on clinical data and sequencing results were compared with transient elastography, the NAFLD fibrosis score (NFS) and FIB-4 index. A Random Forest model containing clinical features and bacterial taxa achieved an area under the curve (AUC) of 0.87 which was only marginally superior to a model without microbiota features (AUC 0.85). The model that aimed to validate a published algorithm achieved an AUC of 0.71. AUC’s for NFS and FIB-4 index were 0.86 and 0.85. Transient elastography performed best with an AUC of 0.93. Gut microbiota signatures might help to predict advanced fibrosis in NAFLD. However, transient elastography achieved the best diagnostic performance for the detection of NAFLD patients at risk for disease progression.

Gut-Brain Axis and Neurodegeneration: State-of-the-Art of Meta-Omics Sciences for Microbiota Characterization

Recent advances in the field of meta-omics sciences and related bioinformatics tools have allowed a comprehensive investigation of human-associated microbiota and its contribution to achieving and maintaining the homeostatic balance. Bioactive compounds from the microbial community harboring the human gut are involved in a finely tuned network of interconnections with the host, orchestrating a wide variety of physiological processes. These includes the bi-directional crosstalk between the central nervous system, the enteric nervous system, and the gastrointestinal tract (i.e., gut-brain axis). The increasing accumulation of evidence suggest a pivotal role of the composition and activity of the gut microbiota in neurodegeneration. In the present review we aim to provide an overview of the state-of-the-art of meta-omics sciences including metagenomics for the study of microbial genomes and taxa strains, metatranscriptomics for gene expression, metaproteomics and metabolomics to identify and/or quantify microbial proteins and metabolites, respectively. The potential and limitations of each discipline were highlighted, as well as the advantages of an integrated approach (multi-omics) to predict microbial functions and molecular mechanisms related to human diseases. Particular emphasis is given to the latest results obtained with these approaches in an attempt to elucidate the link between the gut microbiota and the most common neurodegenerative diseases, such as multiple sclerosis (MS), Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS).

Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches

In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in study design and confounding variables across studies often lead to inconsistent observations. In this narrative review, we have discussed the potential influence of study design and confounding variables on the current sequencing-based oral microbiome-systemic disease link studies. The current limitations of oral microbiome-systemic link studies on type 2 diabetes mellitus, rheumatoid arthritis, pregnancy, atherosclerosis, and pancreatic cancer are discussed in this review, followed by our perspective on how artificial intelligence (AI), particularly machine learning and deep learning approaches, can be employed for predicting systemic disease and host metadata from the oral microbiome. The application of AI for predicting systemic disease as well as host metadata requires the establishment of a global database repository with microbiome sequences and annotated host metadata. However, this task requires collective efforts from researchers working in the field of oral microbiome to establish more comprehensive datasets with appropriate host metadata. Development of AI-based models by incorporating consistent host metadata will allow prediction of systemic diseases with higher accuracies, bringing considerable clinical benefits.

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.

Systematic review: the effects of proton pump inhibitors on the microbiome of the digestive tract-evidence from next-generation sequencing studies

BACKGROUND

Proton pump inhibitors (PPI) are widely used to treat acid-related disorders of the upper gastrointestinal tract. However, large observational studies have raised concerns about PPI-associated adverse events. In recent years, data from next-generation sequencing studies suggested that PPIs affect the composition of the intestinal microbiota, while a balanced gut microbiome is essential for maintaining health.

AIM

To review the available evidence from next-generation sequencing studies on the effect of PPIs on the intestinal microbiome and to discuss possible implications of PPI-induced dysbiosis in health and disease.

METHODS

A systematic review was conducted following the recommendations of the Preferred Reporting Items for Systematic reviews and Meta-Analyses statement. A PubMed query yielded 197 results. 19 publications met the prespecified eligibility criteria.

RESULTS

Twelve observational study cohorts with 708 PPI users and 11 interventional cohorts with 180 PPI users were included in the review. In most studies, PPI treatment did not affect microbiological richness and diversity, but was associated with distinct taxonomic alterations: In the upper gastrointestinal tract, PPI users showed overgrowth of orally derived bacteria, mostly Streptococcaceae (findings based on six independent cohorts with 126 PPI users). In faecal samples, PPIs increased multiple taxa from the orders Bacillales (eg, Staphylococcaceae), Lactobacillales (eg, Enterococcaceae, Lactobacillaceae, Streptococcaceae) and Actinomycetales (eg, Actinomycetaceae, Micrococcaceae), the families Pasteurellaceae and Enterobacteriaceae and the genus Veillonella. Taxa decreased by PPIs include Bifidobacteriaceae, Ruminococcaceae, Lachnospiraceae and Mollicutes (findings in faecal samples based on 19 independent cohorts with 790 PPI users).

CONCLUSION

PPI use is associated with moderate alterations to upper and distal gut microbiota. The available data suggest that PPI-induced hypochlorhydria facilitates colonization of more distal parts of the digestive tract by upper gastrointestinal microbiota.

Bioinformatic strategies to address limitations of 16rRNA short-read amplicons from different sequencing platforms

Sequencing the 16S gene rRNA has become a popular method when identifying bacterial communities. However, recent studies address differences in the characterization based on sample preparation, sequencing platforms, and data analysis. In this work, we tested some of the available user-friendly protocols for data analysis with the reads obtained from the sequencing machines Illumina MiSeq and Ion Torrent Personal Genome Machine (PGM). We sought for the advantages and disadvantages of both platforms in terms of accuracy, detected species, and abundance, analyzing a staggered mock community. Four different pipelines were applied: QIIME 1.9.1 with default parameters, QIIME 1.9.1 with modified parameters and chimera removal, VSEARCH 2.3.4, and QIIME 2 v.2018.2. To address the limitations of species level detection we used species-classifier SPINGO. The optimal pipeline for PGM platform, was the use of QIIME 1.9.1 with default parameters (QIIME1), except when a study requires the detection of Bacteroides or other Bacteroidaceae members, in which QIIME1MOD (with chimera removal) seems to be a good alternative. For Illumina Miseq, VSEARCH strategy can be a good option. Our results also confirm that all the tested pipelines can be used for metagenomic analysis at family and genus level.

Functional effects of the microbiota in chronic respiratory disease

The composition of the lung microbiome is increasingly well characterised, with changes in microbial diversity or abundance observed in association with several chronic respiratory diseases such as asthma, cystic fibrosis, bronchiectasis, and chronic obstructive pulmonary disease. However, the precise effects of the microbiome on pulmonary health and the functional mechanisms by which it regulates host immunity are only now beginning to be elucidated. Bacteria, viruses, and fungi from both the upper and lower respiratory tract produce structural ligands and metabolites that interact with the host and alter the development and progression of chronic respiratory diseases. Here, we review recent advances in our understanding of the composition of the lung microbiome, including the virome and mycobiome, the mechanisms by which these microbes interact with host immunity, and their functional effects on the pathogenesis, exacerbations, and comorbidities of chronic respiratory diseases. We also describe the present understanding of how respiratory microbiota can influence the efficacy of common therapies for chronic respiratory disease, and the potential of manipulation of the microbiome as a therapeutic strategy. Finally, we highlight some of the limitations in the field and propose how these could be addressed in future research.

How MALDI-TOF mass spectrometry can aid the diagnosis of hard-to-identify pathogenic bacteria - the rare and the unknown

Introduction: Ten years after its introduction into clinical microbiology, MALDI-TOF mass spectrometry has become the standard routine identification tool for bacteria in most laboratories. The technology has accelerated analyses and improved the quality of results. The greatest significance has been observed for bacteria that were challenging to be identified by traditional methods. Areas covered: We searched in existing literature (Pubmed) for reports how MALDI-TOF MS has contributed to identification of rare and unknown bacteria from different groups. We describe how this has improved the diagnostics in different groups of bacteria. Reference patterns for strains which yet cannot be assigned to a known species even enable the search for related bacteria in studies as well as in routine diagnostics. MALDI-TOF MS can help to discover and investigate new species and their clinical relevance. It is a powerful tool in the elucidation of the bacterial composition of complex microbiota in culturomics studies. Expert opinion: MALDI-TOF MS has improved the diagnosis of bacterial infections. It also enables knowledge generation for prospective diagnostics. The term 'hard-to-identify' might only be rarely attributed to bacteria in the future. Novel applications are being developed, e.g. subspecies differentiation, typing, and antibiotic resistance testing which may further contribute to improved microbial diagnostics.

Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data

Diseases prediction has been performed by machine learning approaches with various biological data. One of the representative data is the gut microbial community, which interacts with the host's immune system. The abundance of a few microorganisms has been used as markers to predict diverse diseases. In this study, we hypothesized that multi-classification using machine learning approach could distinguish the gut microbiome from following six diseases: multiple sclerosis, juvenile idiopathic arthritis, myalgic encephalomyelitis/chronic fatigue syndrome, acquired immune deficiency syndrome, stroke and colorectal cancer. We used the abundance of microorganisms at five taxonomy levels as features in 696 samples collected from different studies to establish the best prediction model. We built classification models based on four multi-class classifiers and two feature selection methods including a forward selection and a backward elimination. As a result, we found that the performance of classification is improved as we use the lower taxonomy levels of features; the highest performance was observed at the genus level. Among four classifiers, LogitBoost-based prediction model outperformed other classifiers. Also, we suggested the optimal feature subsets at the genus-level obtained by backward elimination. We believe the selected feature subsets could be used as markers to distinguish various diseases simultaneously. The finding in this study suggests the potential use of selected features for the diagnosis of several diseases.

Age-stratified comparative analysis of the differences of gut microbiota associated with blood glucose level

Background

Gut bacteria are an important component of the microbiota ecosystem in humans and other animals, and they play important roles in human health. The aim of this study was to investigate the relationship between gut microbiota and multiple demographical-, behavioral-, or biochemical-related factors in subjects with chronic disease. Subjects with a very wide age range who participated in community-based chronic disease prevention and screening programs in China were enrolled. We analyzed the intestinal microbiota composition using 16S rRNA-based high-throughput sequencing of fecal samples, analyzed the association between gut microbiota structure and multiple demographical, behavioral, and biochemical factors, and compared the differences in microbiota composition in age-stratified groups with different blood glucose levels.

Results

Our results showed that both age and blood glucose levels had a significant impact on the gut microbiota structure. We also identified several taxa showed distinct abundance in groups with different glucose levels. Lactobacillus and Bifidobacterium at genus level and their related taxa were more abundant in the GLU high group comparing with GLU normal group and in NGR group comparing with DM group. Further analysis using the age-stratified data showed that blood glucose levels had a more significant impact on the gut microbiota in the ≥76 y age group than in the ≤75 y age group, which indicated that it is necessary to take age into account when conducting such studies. Moreover, we identified several taxa that were highly associated with blood glucose levels in the ≥76 y age group but not in the ≤75 y age group. Within the ≥76 y age group, Lachnospiraceae incertae sedis and Bacteroides were more abundant in the GLU normal group, whereas Lactobacillus and Bifidobacterium at genus level were more abundant in the GLU high group.

Conclusions

This result suggested that taxa that are capable of differentiating blood glucose levels might differ significantly in different age groups.

Whole metagenomic shotgun sequencing of the subgingival microbiome of diabetics and non-diabetics with different periodontal conditions

OBJECTIVE

The aim of this study was to use high-resolution whole metagenomic shotgun sequencing to characterize the subgingival microbiome of patients with/without type 2 Diabetes Mellitus and with/without periodontitis.

DESIGN

Twelve subjects, falling into one of the four study groups based on the presence/absence of poorly controlled type 2 Diabetes Mellitus and moderate-severe periodontitis, were selected. For each eligible subject, subgingival plaque samples were collected at 4 sites, all representative of the periodontal condition of the individual (i.e., non-bleeding sulci in subjects without a history of periodontitis, bleeding pockets in patients with moderate-severe periodontitis). The subgingival microbiome was evaluated using high-resolution whole metagenomic shotgun sequencing.

RESULTS

The results showed that: (i) the presence of type 2 Diabetes Mellitus and/or periodontitis were associated with a tendency of the subgingival microbiome to decrease in richness and diversity; (ii) the presence of type 2 Diabetes Mellitus was not associated with significant differences in the relative abundance of one or more species in patients either with or without periodontitis; (iii) the presence of periodontitis was associated with a significantly higher relative abundance of Anaerolineaceae bacterium oral taxon 439 in type 2 Diabetes Mellitus patients.

CONCLUSIONS

Whole metagenomic shotgun sequencing of the subgingival microbiome was extremely effective in the detection of low-abundant taxon. Our results point out a significantly higher relative abundance of Anaerolineaceae bacterium oral taxon 439 in patients with moderate to severe periodontitis vs patients without history of periodontitis, which was maintained when the comparison was restricted to type 2 diabetics.

Switching on the light: using metagenomic shotgun sequencing to characterize the intestinal microbiome of Atlantic cod

Atlantic cod (Gadus morhua) is an ecologically important species with a wide-spread distribution in the North Atlantic Ocean, yet little is known about the diversity of its intestinal microbiome in its natural habitat. No geographical differentiation in this microbiome was observed based on 16S rRNA amplicon analyses, yet such finding may result from an inherent lack of power of this method to resolve fine-scaled biological complexity. Here, we use metagenomic shotgun sequencing to investigate the intestinal microbiome of 19 adult Atlantic cod individuals from two coastal populations in Norway-located 470 km apart. Resolving the species community to unprecedented resolution, we identify two abundant species, Photobacterium iliopiscarium and Photobacterium kishitanii, which comprise over 50% of the classified reads. Interestingly, the intestinal P. kishitanii strains have functionally intact lux genes, and its high abundance suggests that fish intestines form an important part of its ecological niche. These observations support a hypothesis that bioluminescence plays an ecological role in the marine food web. Despite our improved taxonomical resolution, we identify no geographical differences in bacterial community structure, indicating that the intestinal microbiome of these coastal cod is colonized by a limited number of closely related bacterial species with a broad geographical distribution.

A review of 10 years of human microbiome research activities at the US National Institutes of Health, Fiscal Years 2007-2016

The National Institutes of Health (NIH) is the primary federal government agency for biomedical research in the USA. NIH provides extensive support for human microbiome research with 21 of 27 NIH Institutes and Centers (ICs) currently funding this area through their extramural research programs. This analysis of the NIH extramural portfolio in human microbiome research briefly reviews the early history of this field at NIH, summarizes the program objectives and the resources developed in the recently completed 10-year (fiscal years 2007-2016) $215 M Human Microbiome Project (HMP) program, evaluates the scope and range of the $728 M NIH investment in extramural human microbiome research activities outside of the HMP over fiscal years 2012-2016, and highlights some specific areas of research which emerged from this investment. This analysis closes with a few comments on the technical needs and knowledge gaps which remain for this field to be able to advance over the next decade and for the outcomes of this research to be able to progress to microbiome-based interventions for treating disease and supporting health.

The model squid-vibrio symbiosis provides a window into the impact of strain- and species-level differences during the initial stages of symbiont engagement

Among horizontally acquired symbioses, the mechanisms underlying microbial strain- and species-level specificity remain poorly understood. Here, confocal-microscopy analyses and genetic manipulation of the squid-vibrio association revealed quantitative differences in a symbiont's capacity to interact with the host during initial engagement. Specifically, dominant strains of Vibrio fischeri, 'D-type', previously named for their dominant, single-strain colonization of the squid's bioluminescent organ, were compared with 'S-type', or 'sharing', strains, which can co-colonize the organ. These D-type strains typically: (i) formed aggregations of 100s-1000s of cells on the light-organ surface, up to 3 orders of magnitude larger than those of S-type strains; (ii) showed dominance in co-aggregation experiments, independent of inoculum size or strain proportion; (iii) perturbed larger areas of the organ's ciliated surface; and, (iv) appeared at the pore of the organ approximately 4×s more quickly than S-type strains. At least in part, genes responsible for biofilm synthesis control the hyperaggregation phenotype of a D-type strain. Other marine vibrios produced relatively small aggregations, while an array of marine Gram-positive and -negative species outside of the Vibrionaceae did not attach to the organ's surface. These studies provide insight into the impact of strain variation on early events leading to establishment of an environmentally acquired symbiosis.

No effects without causes: the Iron Dysregulation and Dormant Microbes hypothesis for chronic, inflammatory diseases

Since the successful conquest of many acute, communicable (infectious) diseases through the use of vaccines and antibiotics, the currently most prevalent diseases are chronic and progressive in nature, and are all accompanied by inflammation. These diseases include neurodegenerative (e.g. Alzheimer's, Parkinson's), vascular (e.g. atherosclerosis, pre-eclampsia, type 2 diabetes) and autoimmune (e.g. rheumatoid arthritis and multiple sclerosis) diseases that may appear to have little in common. In fact they all share significant features, in particular chronic inflammation and its attendant inflammatory cytokines. Such effects do not happen without underlying and initially 'external' causes, and it is of interest to seek these causes. Taking a systems approach, we argue that these causes include (i) stress-induced iron dysregulation, and (ii) its ability to awaken dormant, non-replicating microbes with which the host has become infected. Other external causes may be dietary. Such microbes are capable of shedding small, but functionally significant amounts of highly inflammagenic molecules such as lipopolysaccharide and lipoteichoic acid. Sequelae include significant coagulopathies, not least the recently discovered amyloidogenic clotting of blood, leading to cell death and the release of further inflammagens. The extensive evidence discussed here implies, as was found with ulcers, that almost all chronic, infectious diseases do in fact harbour a microbial component. What differs is simply the microbes and the anatomical location from and at which they exert damage. This analysis offers novel avenues for diagnosis and treatment.

Nephele: a cloud platform for simplified, standardized and reproducible microbiome data analysis

Motivation

Widespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools.

Results

The National Institute of Allergy and Infectious Diseases (NIAID) has created Nephele, a cloud-based microbiome data analysis platform with standardized pipelines and a simple web interface for transforming raw data into biological insights. Nephele integrates common microbiome analysis tools as well as valuable reference datasets like the healthy human subjects cohort of the Human Microbiome Project (HMP). Nephele is built on the Amazon Web Services cloud, which provides centralized and automated storage and compute capacity, thereby reducing the burden on researchers and their institutions.

Availability and implementation

https://nephele.niaid.nih.gov and https://github.com/niaid/Nephele.

Contact

darrell.hurt@nih.gov.

Species classifier choice is a key consideration when analysing low-complexity food microbiome data

BACKGROUND

The use of shotgun metagenomics to analyse low-complexity microbial communities in foods has the potential to be of considerable fundamental and applied value. However, there is currently no consensus with respect to choice of species classification tool, platform, or sequencing depth. Here, we benchmarked the performances of three high-throughput short-read sequencing platforms, the Illumina MiSeq, NextSeq 500, and Ion Proton, for shotgun metagenomics of food microbiota. Briefly, we sequenced six kefir DNA samples and a mock community DNA sample, the latter constructed by evenly mixing genomic DNA from 13 food-related bacterial species. A variety of bioinformatic tools were used to analyse the data generated, and the effects of sequencing depth on these analyses were tested by randomly subsampling reads.

RESULTS

Compositional analysis results were consistent between the platforms at divergent sequencing depths. However, we observed pronounced differences in the predictions from species classification tools. Indeed, PERMANOVA indicated that there was no significant differences between the compositional results generated by the different sequencers (p = 0.693, R² = 0.011), but there was a significant difference between the results predicted by the species classifiers (p = 0.01, R² = 0.127). The relative abundances predicted by the classifiers, apart from MetaPhlAn2, were apparently biased by reference genome sizes. Additionally, we observed varying false-positive rates among the classifiers. MetaPhlAn2 had the lowest false-positive rate, whereas SLIMM had the greatest false-positive rate. Strain-level analysis results were also similar across platforms. Each platform correctly identified the strains present in the mock community, but accuracy was improved slightly with greater sequencing depth. Notably, PanPhlAn detected the dominant strains in each kefir sample above 500,000 reads per sample. Again, the outputs from functional profiling analysis using SUPER-FOCUS were generally accordant between the platforms at different sequencing depths. Finally, and expectedly, metagenome assembly completeness was significantly lower on the MiSeq than either on the NextSeq (p = 0.03) or the Proton (p = 0.011), and it improved with increased sequencing depth.

CONCLUSIONS

Our results demonstrate a remarkable similarity in the results generated by the three sequencing platforms at different sequencing depths, and, in fact, the choice of bioinformatics methodology had a more evident impact on results than the choice of sequencer did.

From systems biology to P4 medicine: applications in respiratory medicine

Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, “omics” data and bioinformatic computing power. It paves the way forward towards “P4 medicine” (predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field.

Systems biology and network medicine have the potential to transform medical research and practicehttp://ow.ly/r3jR30hf35x

Dark matter in host-microbiome metabolomics: Tackling the unknowns-A review

The "dark matter" in metabolomics (unknowns) represents an exciting frontier with significant potential for discovery in relation to biochemistry, yet it also presents one of the largest challenges to overcome. This focussed review takes a close look at the current state-of-the-art and future challenges in tackling the unknowns with specific focus on the human gut microbiome and host-microbe interactions. Metabolomics, like metabolism itself, is a very dynamic discipline, with many workflows and methods under development, both in terms of chemical analysis and post-analysis data processing. Here, we look at developments in the mutli-omic analyses and the use of mass spectrometry to investigate the exchange of metabolites between the host and the microbiome as well as the environment within the microbiome. A case study using HuMiX, a microfluidics-based human-microbial co-culture system that enables the co-culture of human and microbial cells under controlled conditions, is used to highlight opportunities and current limitations. Common definitions, approaches, databases and elucidation techniques from both the environmental and metabolomics fields are covered, with perspectives on how to merge these, as the boundaries blur between the fields. While reflecting on the number of unknowns remaining to be conquered in typical complex samples measured with mass spectrometry (often orders of magnitude above the "knowns"), we provide an outlook on future perspectives and challenges in elucidating the relevant "dark matter".

Can Consideration of the Microbiome Improve Antimicrobial Utilization and Treatment Outcomes in the Oncology Patient?

The need to provide effective and timely antimicrobial treatment to cancer patients with infections is well recognized but tempered by preliminary, but accumulating, evidence that antibiotic-induced microbiome dysbiosis affects cancer therapy response, noninfectious toxicities, and infectious complications. Given only a minority of empirically treated cancer patients are proven to have a true bacterial infection, it is important to consider the potential negative consequences of extensive broad-spectrum antimicrobial use on the commensal microbiota. Herein, we review the literature substantiating the dilemma oncologists face when treating suspected or documented infections with respect to the interaction between the host microbiome, antibiotics, and cancer-related clinical outcomes. We propose microbiome-based explorations that could assist oncologists in optimizing treatment strategies for cancer-related infections as well as the cancer itself. In addition, we discuss knowledge gaps and challenges in this nascent field that must be addressed to deliver medically relevant, translational applications. We anticipate that the emerging knowledge regarding the role of the microbiota in the health of cancer patients may cause a reappraisal of the manner in which antibiotics are used in the oncologic setting and how microorganisms are viewed by oncologists. Clin Cancer Res; 23(13); 3263-8. ©2017 AACRSee related commentary by Fessler and Gajewski, p. 3229.

Exploring the human microbiome from multiple perspectives: factors altering its composition and function

Our microbiota presents peculiarities and characteristics that may be altered by multiple factors. The degree and consequences of these alterations depend on the nature, strength and duration of the perturbations as well as the structure and stability of each microbiota. The aim of this review is to sketch a very broad picture of the factors commonly influencing different body sites, and which have been associated with alterations in the human microbiota in terms of composition and function. To do so, first, a graphical representation of bacterial, fungal and archaeal genera reveals possible associations among genera affected by different factors. Then, the revision of sequence-based predictions provides associations with functions that become part of the active metabolism. Finally, examination of microbial metabolite contents and fluxes reveals whether metabolic alterations are a reflection of the differences observed at the level of population structure, and in the last step, link microorganisms to functions under perturbations that differ in nature and aetiology. The utilisation of complementary technologies and methods, with a special focus on metabolomics research, is thoroughly discussed to obtain a global picture of microbiota composition and microbiome function and to convey the urgent need for the standardisation of protocols.

Systematic Characterization and Analysis of the Taxonomic Drivers of Functional Shifts in the Human Microbiome

Comparative analyses of the human microbiome have identified both taxonomic and functional shifts that are associated with numerous diseases. To date, however, microbiome taxonomy and function have mostly been studied independently and the taxonomic drivers of functional imbalances have not been systematically identified. Here, we present FishTaco, an analytical and computational framework that integrates taxonomic and functional comparative analyses to accurately quantify taxon-level contributions to disease-associated functional shifts. Applying FishTaco to several large-scale metagenomic cohorts, we show that shifts in the microbiome's functional capacity can be traced back to specific taxa. Furthermore, the set of taxa driving functional shifts and their contribution levels vary markedly between functions. We additionally find that similar functional imbalances in different diseases are driven by both disease-specific and shared taxa. Such integrated analysis of microbiome ecological and functional dynamics can inform future microbiome-based therapy, pinpointing putative intervention targets for manipulating the microbiome's functional capacity.

Life in the small intestine: the forgotten microbiome?

The gastrointestinal (GI) microbiota is now widely accepted to be an important modulator of our health and well-being. The microbes colonising the GI tract aid in promoting gut and immune homeostasis, while alterations in the composition and/or density of these microbes, often referred to as dysbiosis, have been implicated in many intestinal and extra-intestinal disorders. As a result, the GI microbiota is of increasing interest as a therapeutic target. This is particularly the case in the context of GI disorders linked to chronic inflammation of the mucosa. In this article, we focus on the small intestinal microbiota, which in many senses can be considered the ‘forgotten' gut microbiome.

WITHDRAWN: The microbiome and human disease pathogenesis: how do you do what you do to me …?

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