Context and the human microbiome

Assessment of Skimmed Milk Flocculation for Bacterial Enrichment from Water Samples, and Benchmarking of DNA Extraction and 16S rRNA Databases for Metagenomics

Water samples for bacterial microbiome studies undergo biomass concentration, DNA extraction, and taxonomic identification steps. Through benchmarking, we studied the applicability of skimmed milk flocculation (SMF) for bacterial enrichment, an adapted in-house DNA extraction protocol, and six 16S rRNA databases (16S-DBs). Surface water samples from two rivers were treated with SMF and vacuum filtration (VF) and subjected to amplicon or shotgun metagenomics. A microbial community standard underwent five DNA extraction protocols, taxonomical identification with six different 16S-DBs, and evaluation by the Measurement Integrity Quotient (MIQ) score. In SMF samples, the skimmed milk was metabolized by members of lactic acid bacteria or genera such as Polaromonas, Macrococcus, and Agitococcus, resulting in increased relative abundance (p < 0.5) up to 5.0 log fold change compared to VF, rendering SMF inapplicable for bacterial microbiome studies. The best-performing DNA extraction protocols were FastSpin Soil, the in-house method, and EurX. All 16S-DBs yielded comparable MIQ scores within each DNA extraction kit, ranging from 61-66 (ZymoBIOMICs) up to 80-82 (FastSpin). DNA extraction kits exert more bias toward the composition than 16S-DBs. This benchmarking study provided valuable information to inform future water metagenomic study designs.

Comparative 16S rRNA gene sequencing study of subgingival microbiota of healthy subjects and patients with periodontitis from four different countries

AIM

To investigate the differences between the subgingival microbiota of healthy subjects (HS) and periodontitis patients (PP) from four different countries through a metagenomic approach.

MATERIALS AND METHODS

Subgingival samples were obtained from subjects from four different countries. Microbial composition was analysed through high-throughput sequencing of the V3-V4 region of the 16S rRNA gene. The country of origin, diagnosis and clinical and demographic variables of the subjects were used to analyse the microbial profiles.

RESULTS

In total, 506 subgingival samples were analysed: 196 from HS and 310 from patients with periodontitis. Differences in richness, diversity and microbial composition were observed when comparing samples pertaining to different countries of origin and different subject diagnoses. Clinical variables, such as bleeding on probing, did not significantly affect the bacterial composition of the samples. A highly conserved core of microbiota associated with periodontitis was detected, while the microbiota associated with periodontally HS was much more diverse.

CONCLUSIONS

Periodontal diagnosis of the subjects was the main variable explaining the composition of the microbiota in the subgingival niche. Nevertheless, the country of origin also had a significant impact on the microbiota and is therefore an important factor to consider when describing subgingival bacterial communities.

The Debate between the Human Microbiota and Immune System in Treating Aerodigestive and Digestive Tract Cancers: A Review

The human microbiota comprises a group of microorganisms co-existing in the human body. Unbalanced microbiota homeostasis may impact metabolic and immune system regulation, shrinking the edge between health and disease. Recently, the microbiota has been considered a prominent extrinsic/intrinsic element of cancer development and a promising milestone in the modulation of conventional cancer treatments. Particularly, the oral cavity represents a yin-and-yang target site for microorganisms that can promote human health or contribute to oral cancer development, such as Fusobacterium nucleatum. Moreover, Helicobacter pylori has also been implicated in esophageal and stomach cancers, and decreased butyrate-producing bacteria, such as Lachnospiraceae spp. and Ruminococcaceae, have demonstrated a protective role in the development of colorectal cancer. Interestingly, prebiotics, e.g., polyphenols, probiotics (Faecalibacterium, Bifidobacterium, Lactobacillus, and Burkholderia), postbiotics (inosine, butyrate, and propionate), and innovative nanomedicines can modulate antitumor immunity, circumventing resistance to conventional treatments and could complement existing therapies. Therefore, this manuscript delivers a holistic perspective on the interaction between human microbiota and cancer development and treatment, particularly in aerodigestive and digestive cancers, focusing on applying prebiotics, probiotics, and nanomedicines to overcome some challenges in treating cancer.

Alteration of the faecal microbiota composition in patients with constipation: evidence of American Gut Project

There is limited information is known about the composition difference of the gut microbiota in patients with constipation and healthy controls. Here, the faecal 16S rRNA fastq sequence data of microbiota from the publicly available American Gut Project (AGP) were analysed. The tendency score matching (PSM) method was used to match in a 1:1 manner to control for confounding factors age, gender, body mass index (BMI), and country. A total of 524 participants including 262 patients with constipation and 262 healthy controls were included in this analysis. The richness and evenness of the gut microbiota in the constipation group were significantly lower than those in the control group. The dominant genera in the constipation group include Escherichia_Shigella, Pseudomonas, and Citrobacter. The dominant genera in the control group include Faecalibacterium, Prevotella, Roseburia, Clostridium_XlVa, and Blautia. The abundance of three butyrate production-related pathways were significantly higher in the constipation group than in the control groups. There was no significant difference in the diversity and gut microbiota composition in patients with constipation at different ages. In conclusion, patients with constipation showed gut microbiota and butyrate metabolism dysbiosis. This dysbiosis might provide a reference for the diagnosis and clinical therapy of diseases.

Distance-Based Phylogenetic Placement with Statistical Support

Phylogenetic identification of unknown sequences by placing them on a tree is routinely attempted in modern ecological studies. Such placements are often obtained from incomplete and noisy data, making it essential to augment the results with some notion of uncertainty. While the standard likelihood-based methods designed for placement naturally provide such measures of uncertainty, the newer and more scalable distance-based methods lack this crucial feature. Here, we adopt several parametric and nonparametric sampling methods for measuring the support of phylogenetic placements that have been obtained with the use of distances. Comparing the alternative strategies, we conclude that nonparametric bootstrapping is more accurate than the alternatives. We go on to show how bootstrapping can be performed efficiently using a linear algebraic formulation that makes it up to 30 times faster and implement this optimized version as part of the distance-based placement software APPLES. By examining a wide range of applications, we show that the relative accuracy of maximum likelihood (ML) support values as compared to distance-based methods depends on the application and the dataset. ML is advantageous for fragmentary queries, while distance-based support values are more accurate for full-length and multi-gene datasets. With the quantification of uncertainty, our work fills a crucial gap that prevents the broader adoption of distance-based placement tools.

Local Breast Microbiota: A "New" Player on the Block

Simple Summary

Microbiota plays a fundamental role in the induction, training and function of the human immune system. The interactions between microbiota and immune cells have consequences in several settings, namely in carcinogenesis but also in anticancer activity. Immunotherapy, already widely used in the treatment of several solid cancers, modulates the action of the immune system, promoting antitumour effects. Recently, there has been a growing interest in studying the microbiota composition as a possible modulator of the tumour microenvironment and consequently of the response to certain therapies such as immunotherapy.

Abstract

The tumour microenvironment (TME) comprises a complex ecosystem of different cell types, including immune cells, cells of the vasculature and lymphatic system, cancer-associated fibroblasts, pericytes, and adipocytes. Cancer proliferation, invasion, metastasis, drug resistance and immune escape are all influenced by the dynamic interaction between cancer cells and TME. Microbes, such as bacteria, fungi, viruses, archaea and protists, found within tumour tissues, constitute the intratumour microbiota, which is tumour type-specific and distinct among patients with different clinical outcomes. Growing evidence reveals a significant relevance of local microbiota in the colon, liver, breast, lung, oral cavity and pancreas carcinogenesis. Moreover, there is a growing interest in the tumour immune microenvironment (TIME) pointed out in several cross-sectional studies on the correlation between microbiota and TME. It is now known that microorganisms have the capacity to change the density and function of anticancer and suppressive immune cells, enabling the promotion of an inflammatory environment. As immunotherapy (such as immune checkpoint inhibitors) is becoming a promising therapy using TIME as a therapeutic target, the analysis and comprehension of local microbiota and its modulating strategies can help improve cancer treatments.

Considerations and best practices in animal science 16S ribosomal RNA gene sequencing microbiome studies

Microbiome studies in animal science using 16S rRNA gene sequencing have become increasingly common in recent years as sequencing costs continue to fall and bioinformatic tools become more powerful and user-friendly. The combination of molecular biology, microbiology, microbial ecology, computer science, and bioinformatics-in addition to the traditional considerations when conducting an animal science study-makes microbiome studies sometimes intimidating due to the intersection of different fields. The objective of this review is to serve as a jumping-off point for those animal scientists less familiar with 16S rRNA gene sequencing and analyses and to bring up common issues and concerns that arise when planning an animal microbiome study from design through analysis. This review includes an overview of 16S rRNA gene sequencing, its advantages, and its limitations; experimental design considerations such as study design, sample size, sample pooling, and sample locations; wet lab considerations such as field handing, microbial cell lysis, low biomass samples, library preparation, and sequencing controls; and computational considerations such as identification of contamination, accounting for uneven sequencing depth, constructing diversity metrics, assigning taxonomy, differential abundance testing, and, finally, data availability. In addition to general considerations, we highlight some special considerations by species and sample type.

Determine independent gut microbiota-diseases association by eliminating the effects of human lifestyle factors

Lifestyle and physiological variables on human disease risk have been revealed to be mediated by gut microbiota. Low concordance between case-control studies for detecting disease-associated microbe existed due to limited sample size and population-wide bias in lifestyle and physiological variables. To infer gut microbiota-disease associations accurately, we propose to build machine learning models by including both human variables and gut microbiota. When the model's performance with both gut microbiota and human variables is better than the model with just human variables, the independent gut microbiota -disease associations will be confirmed. By building models on the American Gut Project dataset, we found that gut microbiota showed distinct association strengths with different diseases. Adding gut microbiota into human variables enhanced the classification performance of IBD significantly; independent associations between occurrence information of gut microbiota and irritable bowel syndrome, C. difficile infection, and unhealthy status were found; adding gut microbiota showed no improvement on models' performance for diabetes, small intestinal bacterial overgrowth, lactose intolerance, cardiovascular disease. Our results suggested that although gut microbiota was reported to be associated with many diseases, a considerable proportion of these associations may be very weak. We proposed a list of microbes as biomarkers to classify IBD and unhealthy status. Further functional investigations of these microbes will improve understanding of the molecular mechanism of human diseases.

The role of the tumor microbe microenvironment in the tumor immune microenvironment: bystander, activator, or inhibitor?

The efficacy of cancer immunotherapy largely depends on the tumor microenvironment, especially the tumor immune microenvironment. Emerging studies have claimed that microbes reside within tumor cells and immune cells, suggesting that these microbes can impact the state of the tumor immune microenvironment. For the first time, this review delineates the landscape of intra-tumoral microbes and their products, herein defined as the tumor microbe microenvironment. The role of the tumor microbe microenvironment in the tumor immune microenvironment is multifaceted: either as an immune activator, inhibitor, or bystander. The underlying mechanisms include: (I) the presentation of microbial antigens by cancer cells and immune cells, (II) microbial antigens mimicry shared with tumor antigens, (III) microbe-induced immunogenic cell death, (IV) microbial adjuvanticity mediated by pattern recognition receptors, (V) microbe-derived metabolites, and (VI) microbial stimulation of inhibitory checkpoints. The review further suggests the use of potential modulation strategies of the tumor microbe microenvironment to enhance the efficacy and reduce the adverse effects of checkpoint inhibitors. Lastly, the review highlights some critical questions awaiting to be answered in this field and provides possible solutions. Overall, the tumor microbe microenvironment modulates the tumor immune microenvironment, making it a potential target for improving immunotherapy. It is a novel field facing major challenges and deserves further exploration.

Fast and accurate distance-based phylogenetic placement using divide and conquer

Phylogenetic placement of query samples on an existing phylogeny is increasingly used in molecular ecology, including sample identification and microbiome environmental sampling. As the size of available reference trees used in these analyses continues to grow, there is a growing need for methods that place sequences on ultra-large trees with high accuracy. Distance-based placement methods have recently emerged as a path to provide such scalability while allowing flexibility to analyse both assembled and unassembled environmental samples. In this study, we introduce a distance-based phylogenetic placement method, APPLES-2, that is more accurate and scalable than existing distance-based methods and even some of the leading maximum-likelihood methods. This scalability is owed to a divide-and-conquer technique that limits distance calculation and phylogenetic placement to parts of the tree most relevant to each query. The increased scalability and accuracy enables us to study the effectiveness of APPLES-2 for placing microbial genomes on a data set of 10,575 microbial species using subsets of 381 marker genes. APPLES-2 has very high accuracy in this setting, placing 97% of query genomes within three branches of the optimal position in the species tree using 50 marker genes. Our proof-of-concept results show that APPLES-2 can quickly place metagenomic scaffolds on ultra-large backbone trees with high accuracy as long as a scaffold includes tens of marker genes. These results pave the path for a more scalable and widespread use of distance-based placement in various areas of molecular ecology.

Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. We demonstrate that our implementation of a novel attention-based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://github.com/EESI/sequence_attention (a python package) and https://github.com/EESI/seq2att (a command line tool).

Best Practices for Microbiome Study Design in Companion Animal Research

The gut microbiome is a community of microorganisms that inhabits an animal host's gastrointestinal tract, with important effects on animal health that are shaped by multiple environmental, dietary, and host-associated factors. Clinical and dietary trials in companion animals are increasingly including assessment of the microbiome, but interpretation of these results is often hampered by suboptimal choices in study design. Here, we review best practices for conducting feeding trials or clinical trials that intend to study the effects of an intervention on the microbiota. Choices for experimental design, including a review of basic designs, controls, and comparison groups, are discussed in the context of special considerations necessary for microbiome studies. Diet is one of the strongest influences on the composition of gut microbiota, so applications specific to nutritional interventions are discussed in detail. Lastly, we provide specific advice for successful recruitment of colony animals and household pets into an intervention study. This review is intended to serve as a resource to academic and industry researchers, clinicians, and veterinarians alike, for studies that test many different types of interventions.

Impacts of Maternal Diet and Alcohol Consumption during Pregnancy on Maternal and Infant Gut Microbiota

(1) Background: Maternal diet and alcohol consumption can influence both maternal and infant's gut microbiota. These relationships are still not examined in the Chinese population. The purpose of this study was to explore the effect of alcohol consumption and maternal diet during pregnancy on maternal and infant's gut microbiota. (2) Methods: Twenty-nine mother-child dyads were enrolled in central China. Fecal samples of mothers during late pregnancy and of newborns within 48 h were collected. The V3-V4 regions of 16S rRNA sequences were analyzed. A self-administrated questionnaire about simple diet frequency in the past week was completed by mothers before childbirth. The demographic information was finished by mothers at 24 h after childbirth. (3) Results: Among these 29 mothers, 10 mothers reported alcohol consumption during pregnancy. The PCoA (β-diversity) showed significant difference in maternal gut microbiota between the alcohol consumption group vs. the non-alcohol consumption group (abund-Jaccard, r = 0.2, p = 0.006). The same phenomenon was observed in newborns (unweighted-UniFrac full tree, r = 0.174, p = 0.031). Maternal alcohol consumption frequency showed positive associations with maternal Phascolarctobacterium (p = 0.032) and Blautia (p = 0.019); maternal Faecalibacterium (p = 0.013) was negatively correlated with frequency of alcohol consumption. As for newborns, a positive relationship showed between Megamonas (p = 0.035) and newborns with maternal alcohol consumption. The diet was not associated with both maternal and infant's gut microbiota. (4) Conclusions: Maternal alcohol consumption during pregnancy influenced the gut microbiota on both mothers and the newborns. Future research is needed to explore these relationships in a lager birth cohort. Understanding the long-term effect of alcohol consumption on maternal and newborns' gut microbiota is needed.

Genetic grouping of SARS-CoV-2 coronavirus sequences using informative subtype markers for pandemic spread visualization

We propose an efficient framework for genetic subtyping of SARS-CoV-2, the novel coronavirus that causes the COVID-19 pandemic. Efficient viral subtyping enables visualization and modeling of the geographic distribution and temporal dynamics of disease spread. Subtyping thereby advances the development of effective containment strategies and, potentially, therapeutic and vaccine strategies. However, identifying viral subtypes in real-time is challenging: SARS-CoV-2 is a novel virus, and the pandemic is rapidly expanding. Viral subtypes may be difficult to detect due to rapid evolution; founder effects are more significant than selection pressure; and the clustering threshold for subtyping is not standardized. We propose to identify mutational signatures of available SARS-CoV-2 sequences using a population-based approach: an entropy measure followed by frequency analysis. These signatures, Informative Subtype Markers (ISMs), define a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals. Through ISM compression, we find that certain distant nucleotide variants covary, including non-coding and ORF1ab sites covarying with the D614G spike protein mutation which has become increasingly prevalent as the pandemic has spread. ISMs are also useful for downstream analyses, such as spatiotemporal visualization of viral dynamics. By analyzing sequence data available in the GISAID database, we validate the utility of ISM-based subtyping by comparing spatiotemporal analyses using ISMs to epidemiological studies of viral transmission in Asia, Europe, and the United States. In addition, we show the relationship of ISMs to phylogenetic reconstructions of SARS-CoV-2 evolution, and therefore, ISMs can play an important complementary role to phylogenetic tree-based analysis, such as is done in the Nextstrain project. The developed pipeline dynamically generates ISMs for newly added SARS-CoV-2 sequences and updates the visualization of pandemic spatiotemporal dynamics, and is available on Github at https://github.com/EESI/ISM (Jupyter notebook), https://github.com/EESI/ncov_ism (command line tool) and via an interactive website at https://covid19-ism.coe.drexel.edu/.

Plant-Based Diets, the Gut Microbiota, and Trimethylamine N-Oxide Production in Chronic Kidney Disease: Therapeutic Potential and Methodological Considerations

High circulating trimethylamine-N-oxide (TMAO) is associated with an increased risk of cardiovascular disease and mortality in people with chronic kidney disease (CKD). In individuals with CKD, reduced kidney function leads to decreased excretion of TMAO, which results in accumulation in the circulation. Higher circulating TMAO has been linked to higher intake of animal-based foods in omnivorous diets. Thus, plant-based diets have been suggested as an intervention to slow the progression of CKD and reduce cardiovascular risk, perhaps explained in part by reduced TMAO production. This article reviews the current evidence on plant-based diets as a dietary intervention to decrease gut-derived TMAO production in patients with CKD, while highlighting methodological issues that present challenges to advancing research and subsequent translation of this approach. Overall, we find that plant-based diets are promising for reducing gut-derived TMAO production in patients with CKD but that further interventional studies are warranted.

Defining the oral microbiome by whole-genome sequencing and resistome analysis: the complexity of the healthy picture

Background

The microbiome of the oral cavity is the second-largest and diverse microbiota after the gut, harboring over 700 species of bacteria and including also fungi, viruses, and protozoa. With its diverse niches, the oral cavity is a very complex environment, where different microbes preferentially colonize different habitats. Recent data indicate that the oral microbiome has essential functions in maintaining oral and systemic health, and the emergence of 16S rRNA gene next-generation sequencing (NGS) has greatly contributed to revealing the complexity of its bacterial component. However, a detailed site-specific map of oral microorganisms (including also eukaryotes and viruses) and their relative abundance is still missing. Here, we aimed to obtain a comprehensive view of the healthy oral microbiome (HOM), including its drug-resistance features.

Results

The oral microbiome of twenty healthy subjects was analyzed by whole-genome sequencing (WGS) and real-time quantitative PCR microarray. Sampled oral micro-habitat included tongue dorsum, hard palate, buccal mucosa, keratinized gingiva, supragingival and subgingival plaque, and saliva with or without rinsing. Each sampled oral niche evidenced a different microbial community, including bacteria, fungi, and viruses. Alpha-diversity evidenced significant differences among the different sampled sites (p < 0.0001) but not among the enrolled subjects (p = 0.876), strengthening the notion of a recognizable HOM. Of note, oral rinse microbiome was more representative of the whole site-specific microbiomes, compared with that of saliva. Interestingly, HOM resistome included highly prevalent genes conferring resistance to macrolide, lincosamides, streptogramin, and tetracycline.

Conclusions

The data obtained in 20 subjects by WGS and microarray analysis provide for the first time a comprehensive view of HOM and its resistome, contributing to a deeper understanding of the composition of oral microbiome in the healthy subject, and providing an important reference for future studies, allowing to identify microbial signatures related to functional and metabolic alterations associated with diseases, potentially useful for targeted therapies and precision medicine.

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.

Effect of time restricted feeding on the gut microbiome in adults with obesity: A pilot study

BACKGROUND

Time restricted feeding is a form of intermittent fasting where participants shorten the daily window in which they eat.

AIM

This is the first study to examine the effects of intermittent fasting on changes in the gut microbiome.

METHODS

Adults with obesity (n = 14) participated in a daily 8-hour time restricted feeding intervention (8-hour feeding window/16-hour fasting window) for 12 weeks. Fecal microbiota were determined by 16 S rRNA (ribosomal ribonucleic acid) gene sequencing of stool samples.

RESULTS

Body weight decreased (P < 0.05) by -2 ± 1 kg. Gut microbiota phylogenetic diversity remained unchanged. The two most common phyla were Firmicutes and Bacteroidetes accounting for 61.2% and 26.9% of total abundance at baseline. No significant alterations in the abundance of Firmicutes, Bacteroidetes, or any other phyla were detected after 12 weeks of time restricted feeding.

CONCLUSIONS

Time restricted feeding did not significantly alter the diversity or overall composition of the gut microbiome.

A Descriptive Review on the Prevalence of Gastrointestinal Disturbances and Their Multiple Associations in Autism Spectrum Disorder

Background and Objectives: Gastrointestinal disturbances have been frequently, but not unanimously, reported in autism spectrum disorder (ASD) individuals. Thus, digestive symptoms, such as constipation, diarrhea, abdominal bloating, and pain have been reported to correlate to the various maladaptive behaviors in ASD children, such as irritability, social withdrawal, stereotypy, hyperactivity, and even language regression. In this context, the present study provides an overview on the prevalence of the gastrointestinal (GI) disorders in ASD and the correlation between these and ASD symptoms and comorbidities and subsequently discusses the metabolic and microbiome factors underlying the effects of GI disorders in ASD. Materials and Methods: For our analysis of GI symptoms in children with ASD, we have searched peer-reviewed journals from 2005 to 2017 in PubMed databases that addressed the specificity of GI symptoms in ASD and included correlations of GI and ASD symptoms. The criteria for inclusion were clear quantitative mentioning of GI modifications, GI symptoms correlation with specific ASD symptoms or comorbidities, an appropriate methodology for defining ASD, and larger size samples. For this topic, only studies on human patients and original research were considered. A subsequent search in PubMed databases in journals from 2000 to 2017 we analyzed 13 articles on the mechanisms underlying the impact of GI dysfunctions in ASD, including gut microbial dysbiosis, immune reactivity, genetics, and altered neurotransmitters on the gut-brain axis. Results: In the 18 original research studies that we selected out of an initial 327 studies, despite the different methodology, a predominant 83% highlighted the increased prevalence of GI symptoms in ASD patients. Constipation was most frequently cited, appearing in 12 of the studies (80%), followed by diarrhea reports in eight studies (53%). The association between cognitive and behavioral deficits and GI disorders was suggested in certain groups of ASD individuals. Conclusion: The evidence presented so far by numerous studies seems to indicate that GI dysfunctions are of particular relevance in ASD, underlined by various abnormalities along the nervous connections between the central nervous system and the gut, such as impaired parasympathetic activity and increased endocrine stress response. Sufficiently large size samples and standardized methodology are required for future studies to clarify the complex interactions between GI disturbances and ASD symptoms.

Food allergy and omics

Food allergy (FA) prevalence has been increasing over the last few decades and is now a global health concern. Current diagnostic methods for FA result in a high number of false-positive results, and the standard of care is either allergen avoidance or use of epinephrine on accidental exposure, although currently with no other approved treatments. The increasing prevalence of FA, lack of robust biomarkers, and inadequate treatments warrants further research into the mechanism underlying food allergies. Recent technological advances have made it possible to move beyond traditional biological techniques to more sophisticated high-throughput approaches. These technologies have created the burgeoning field of omics sciences, which permit a more systematic investigation of biological problems. Omics sciences, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and exposomics, have enabled the construction of regulatory networks and biological pathway models. Parallel advances in bioinformatics and computational techniques have enabled the integration, analysis, and interpretation of these exponentially growing data sets and opens the possibility of personalized or precision medicine for FA.

Constructing and Analyzing Microbiome Networks in R

Microbiomes are complex microbial communities whose structure and function are heavily influenced by microbe-microbe and microbe-host interactions mediated by a range of mechanisms, all of which have been implicated in the modulation of disease progression and clinical outcome. Therefore, understanding the microbiome as a whole, including both the complex interplay among microbial taxa and interactions with their hosts, is essential for understanding the spectrum of roles played by microbiomes in host health, development, dysbiosis, and polymicrobial infections. Network theory, in the form of systems-oriented, graph-theoretical approaches, is an exciting holistic methodology that can facilitate microbiome analysis and enhance our understanding of the complex ecological and evolutionary processes involved. Using network theory, one can model and analyze a microbiome and all its complex interactions in a single network. Here, we describe in detail and step by step, the process of building, analyzing and visualizing microbiome networks from operational taxonomic unit (OTU) tables in R and RStudio, using several different approaches and extensively commented code snippets.

Phy-PMRFI: Phylogeny-Aware Prediction of Metagenomic Functions Using Random Forest Feature Importance

High-throughput sequencing techniques have accelerated functional metagenomics studies through the generation of large volumes of omics data. The integration of these data using computational approaches is potentially useful for predicting metagenomic functions. Machine learning (ML) models can be trained using microbial features which are then used to classify microbial data into different functional classes. For example, ML analyses over the human microbiome data has been linked to the prediction of important biological states. For analysing omics data, integrating abundance count of taxonomical features with their biological relationships is important. These relationships can potentially be uncovered from the phylogenetic tree of microbial taxa. In this paper, we propose a novel integrative framework Phy-PMRFI. This framework is driven by the phylogeny-based modeling of omics data to predict metagenomic functions using important features selected by a random forest importance (RFI) strategy. The proposed framework integrates the underlying phylogenetic tree information with abundance measures of microbial species (features) by creating a novel phylogeny and abundance aware matrix structure (PAAM). Phy-PMRFI progresses by ranking the microbial features using an RFI measure. This is then used as input for microbiome classification. The resultant feature set enhances the performance of the state-of-art methods such as support vector machines. Our proposed integrative framework also outperforms the state-of-the-art pipeline of phylogenetic isometric log-ratio transform (PhILR) and MetaPhyl. Prediction accuracy of 90 % is obtained with Phy-PMRFI over human throat microbiome in comparison to other approaches of PhILR with 53% and MetaPhyl with 71% accuracy.

Commentary for Special Issue of Prevention Science "Using Genetics in Prevention: Science Fiction or Science Fact?"

A growing number of prevention studies have incorporated genetic information. In this commentary, I discuss likely reasons for growing interest in this line of research and reflect on the current state of the literature. I review challenges associated with the incorporation of genotypic information into prevention studies, as well as ethical considerations associated with this line of research. I discuss areas where developmental psychologists and prevention scientists can make substantive contributions to the study of genetic predispositions, as well as areas that could benefit from closer collaborations between prevention scientists and geneticists to advance this area of study. In short, this commentary tackles the complex questions associated with what we hope to achieve by adding genetic components to prevention research and where this research is likely to lead in the future.

Composition of gut microbiota and its association with body mass index and lifestyle factors in a cohort of 7-18 years old children from the American Gut Project

BACKGROUND

The association between the gut microbiota and obesity in young children and adolescents is not fully studied.

OBJECTIVES

This study investigated the associations between the gut microbiota and body mass index (BMI) level (underweight, normal, overweight, obese) and lifestyles (diet type and exercise frequency), controlling for demographic and clinical factors among children aged 7-18 years.

METHODS

A cohort study was conducted on 267 children aged 7-18 years from the American Gut Project. 16S rRNA sequences were analysed by QIIME 2™. Composition of gut microbiota and its associations with BMI level, weight change and lifestyles were analysed using linear decomposition model.

RESULTS

Significant factors affecting the gut microbiota were BMI level (p = 0.009), exercise frequency (p = 0.003) and diet type (p = 0.01), controlling for age, sex and use of antibiotics and probiotics. More bacterial operational taxonomic units (OTUs) were associated with BMI level (120 OTUs) and diet type (122 OTUs) than exercise frequency (67 OTUs). Actinobacteria phylum had significantly depleted OTUs for BMI level, diet type and exercise frequency; Proteobacteria phylum had significantly enriched OTUs for higher BMI level and Firmicutes phylum had significantly enriched OTUs for more frequent exercise.

CONCLUSIONS

Significant associations were found between the gut microbiota composition and BMI level and lifestyles controlling for demographic and clinical factors in children aged 7-18 years.

The Hologenome Concept of Evolution: Medical Implications

All natural animals and plants are holobionts, consisting of the host and microbiome, which is composed of abundant and diverse microorganisms. Health and disease of holobionts depend as much on interactions between host and microbiome and within the microbiome, as on interactions between organs and body parts of the host. Recent evidence indicates that a significant fraction of the microbiome is transferred by a variety of mechanisms from parent to offspring for many generations. Genetic variation in holobionts can occur in the microbiome as well as in the host genome, and it occurs more rapidly and by more mechanisms in genomes of microbiomes than in host genomes (e.g. via acquisition of novel microbes and horizontal gene transfer of microbial genes into host chromosomes). Evidence discussed in this review supports the concept that holobionts with their hologenomes can be considered levels of selection in evolution. Though changes in the microbiome can lead to evolution of the holobiont, it can also lead to dysbiosis and diseases (e.g. obesity, diarrhea, inflammatory bowel disease, and autism). In practice, the possibility of manipulating microbiomes offers the potential to prevent and cure diseases.

Gut microbiota diversity across ethnicities in the United States

Composed of hundreds of microbial species, the composition of the human gut microbiota can vary with chronic diseases underlying health disparities that disproportionally affect ethnic minorities. However, the influence of ethnicity on the gut microbiota remains largely unexplored and lacks reproducible generalizations across studies. By distilling associations between ethnicity and differences in two US-based 16S gut microbiota data sets including 1,673 individuals, we report 12 microbial genera and families that reproducibly vary by ethnicity. Interestingly, a majority of these microbial taxa, including the most heritable bacterial family, Christensenellaceae, overlap with genetically associated taxa and form co-occurring clusters linked by similar fermentative and methanogenic metabolic processes. These results demonstrate recurrent associations between specific taxa in the gut microbiota and ethnicity, providing hypotheses for examining specific members of the gut microbiota as mediators of health disparities.

Hybrid-denovo: a de novo OTU-picking pipeline integrating single-end and paired-end 16S sequence tags

Background

Illumina paired-end sequencing has been increasingly popular for 16S rRNA gene-based microbiota profiling. It provides higher phylogenetic resolution than single-end reads due to a longer read length. However, the reverse read (R2) often has significant low base quality, and a large proportion of R2s will be discarded after quality control, resulting in a mixture of paired-end and single-end reads. A typical 16S analysis pipeline usually processes either paired-end or single-end reads but not a mixture. Thus, the quantification accuracy and statistical power will be reduced due to the loss of a large amount of reads. As a result, rare taxa may not be detectable with the paired-end approach, or low taxonomic resolution will result in a single-end approach.

Results

To have both the higher phylogenetic resolution provided by paired-end reads and the higher sequence coverage by single-end reads, we propose a novel OTU-picking pipeline, hybrid-denovo, that can process a hybrid of single-end and paired-end reads. Using high-quality paired-end reads as a gold standard, we show that hybrid-denovo achieved the highest correlation with the gold standard and performed better than the approaches based on paired-end or single-end reads in terms of quantifying the microbial diversity and taxonomic abundances. By applying our method to a rheumatoid arthritis (RA) data set, we demonstrated that hybrid-denovo captured more microbial diversity and identified more RA-associated taxa than a paired-end or single-end approach.

Conclusions

Hybrid-denovo utilizes both paired-end and single-end 16S sequencing reads and is recommended for 16S rRNA gene targeted paired-end sequencing data.

Competitive lottery-based assembly of selected clades in the human gut microbiome

BACKGROUND

While the composition of the gut microbiome has now been well described by several large-scale studies, models that can account for the range of microbiome compositions that have been observed are still lacking. One model that has been well studied in macro communities and that could be useful for understanding microbiome assembly is the competitive lottery model. This model posits that groups of organisms from a regional pool of species are able to colonize the same niche and that the first species to arrive will take over the entire niche, excluding other group members.

RESULTS

Here, we examined whether this model also plays a role in the assembly of the human gut microbiome, defining measures to identify groups of organisms whose distribution across samples conforms to the competitive lottery schema. Applying this model to multiple datasets with thousands of human gut microbiome samples, we identified several taxonomic groups that exhibit a lottery-like distribution, including the Akkermansia, Dialister, and Phascolarctobacterium genera. We validated that these groups exhibit lottery-like assembly in multiple independent microbiome datasets confirming that this assembly schema is universal and not cohort specific. Examining the distribution of species from these groups in the gut microbiome of developing infants, we found that the initial lottery winner can be replaced by a different member of the group. We further found that species from lottery-like groups tend to have fewer genes in their genomes, suggesting more specialized species that are less able to engage in niche differentiation.

CONCLUSIONS

Combined, our findings highlight the complex and dynamic process through which microbial communities assemble and suggest that different phylogenetic groups may follow different models during this process.

Reliability of a participant-friendly fecal collection method for microbiome analyses: a step towards large sample size investigation

Background

The effects of gut microbiota on human traits are expected to be small to moderate and adding the complexity of the human diseases, microbiome research demands big sample sizes. Fecal samples for such studies are mostly self-collected by participants at home. This imposes an extra level of complexity as sample collection and storage can be challenging. Effective, low-burden collection and storage methods allowing fecal samples to be transported properly and ensuring optimal quality and quantity of bacterial DNA for upstream analyses are necessary. Moreover, accurate assessment of the microbiome composition also depends on bacterial DNA extraction method. The aim of this study was to evaluate the reliability and efficiency of the OMNIgene•GUT kit as a participant-fecal friendly collection method (storage at room temperature for 24 h (O24h) or 7 days (O7d)) in comparison to the standard collection method (Fresh, storage at 4 °C for less than 24 h) in terms of amount of variability and information content accounting for two common DNA extraction methods.

Results

Fourteen fecal samples were collected from healthy individuals (7 males, 7 females). Collection and storage methods did not differ significantly in terms of DNA concentration and Shannon diversity index. Phylum relative abundance showed significant differences for Bacteroidetes, Actinobacteria and Cyanobacteria. The differences were observed between control (Fresh) and O24h methods, but not between Fresh and O7d. These differences were not seen when performing bacterial DNA quantification based on three bacterial groups: Bacteroides spp., Bifidobacterium spp. and Clostridium cluster IV, which represent three major phyla: Bacteroidetes, Actinobacteria and Firmicutes respectively. The two DNA extraction methods differ in terms of DNA quantity, quality, bacterial diversity and bacterial relative abundance. Furthermore, principal component analysis revealed differences in microbial structure, which are driven by the DNA extraction methods more than the collection/storage methods.

Conclusion

Our results have highlighted the potential of using the OMNIgene•GUT kit for collection and storage at ambient temperature, which is convenient for studies aiming to collect large samples by giving participants the possibility to send samples by post. Importantly, we revealed that the choice of DNA extraction method have an impact on the microbiome profiling.

Electronic supplementary material

The online version of this article (10.1186/s12866-018-1249-x) contains supplementary material, which is available to authorized users.

Regional variation limits applications of healthy gut microbiome reference ranges and disease models

Dysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression^1-3. Diagnostic applications have been proposed for inflammatory bowel disease diagnosis and prognosis⁴, colorectal cancer prescreening⁵ and therapeutic choices in melanoma⁶. Noninvasive sampling could facilitate large-scale public health applications, including early diagnosis and risk assessment in metabolic⁷ and cardiovascular diseases⁸. To understand the generalizability of microbiota-based diagnostic models of metabolic disease, we characterized the gut microbiota of 7,009 individuals from 14 districts within 1 province in China. Among phenotypes, host location showed the strongest associations with microbiota variations. Microbiota-based metabolic disease models developed in one location failed when used elsewhere, suggesting that such models cannot be extrapolated. Interpolated models performed much better, especially in diseases with obvious microbiota-related characteristics. Interpolation efficiency decreased as geographic scale increased, indicating a need to build localized baseline and disease models to predict metabolic risks.

Optimizing body fluid recognition from microbial taxonomic profiles

In forensics the DNA-profile is used to identify the person who left a biological trace, but information on body fluid can also be essential in the evidence evaluation process. Microbial composition data could potentially be used for body fluid recognition as an improved alternative to the currently used presumptive tests. We have developed a customized workflow for interpretation of bacterial 16S sequence data based on a model composed of Partial Least Squares (PLS) in combination with Linear Discriminant Analysis (LDA). Large data sets from the Human Microbiome Project (HMP) and the American Gut Project (AGP) were used to test different settings in order to optimize performance. From the initial cross-validation of body fluid recognition within the HMP data, the optimal overall accuracy was close to 98%. Sensitivity values for the fecal and oral samples were ≥0.99, followed by the vaginal samples with 0.98 and the skin and nasal samples with 0.96 and 0.81 respectively. Specificity values were high for all 5 categories, mostly >0.99. This optimal performance was achieved by using the following settings: Taxonomic profiles based on operational taxonomic units (OTUs) with 0.98 identity (OTU98), Aitchisons simplex transform with C = 1 pseudo-count and no regularization (r = 1) in the PLS step. Variable selection did not improve the performance further. To test for robustness across sequencing platforms, we also trained the classifier on HMP data and tested on the AGP data set. In this case, the standard OTU based approach showed moderately decline in accuracy. However, by using taxonomic profiles made by direct assignment of reads to a genus, we were able to nearly maintain the high accuracy levels. The optimal combination of settings was still used, except the taxonomic level being genus instead of OTU98. The performance may be improved even further by using higher resolution taxonomic bins.

A Comprehensive Study on Predicting Functional Role of Metagenomes Using Machine Learning Methods

"Metagenomics" is the study of genomic sequences obtained directly from environmental microbial communities with the aim to linking their structures with functional roles. The field has been aided in the unprecedented advancement through high-throughput omics data sequencing. The outcome of sequencing are biologically rich data sets. Metagenomic data consisting of microbial spe-cies which outnumber microbial samples, lead to the "curse of dimensionality". Hence the focus in metagenomics studies has moved towards developing efficient computational models using Machine Learning (ML), reducing the computational cost. In this paper, we comprehensively assessed various ML approaches to classifying high-dimensional human microbiota effectively into their functional phenotypes. We propose the application of embedded feature selection methods, namely, Extreme Gradient Boost-ing and Penalized Logistic Regression to determine important species. The resultant feature set enhanced the performance of one of the most popular state-of-the-art methods, Random Forest (RF) over metagenomic studies. Experimental results indicate that the proposed method achieved best results in terms of accuracy, area under Receiver Operating Characteristic curve (ROC-AUC) and major improvement in processing time. It outperformed other feature selection methods of filters or wrappers over RF and classifiers such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), and -Nearest Neighbors (-NN).

Assessing the Influence of Vegan, Vegetarian and Omnivore Oriented Westernized Dietary Styles on Human Gut Microbiota: A Cross Sectional Study

Diet and lifestyle have a strong influence on gut microbiota, which in turn has important implications on a variety of health-related aspects. Despite great advances in the field, it remains unclear to which extent the composition of the gut microbiota is modulated by the intake of animal derived products, compared to a vegetable based diet. Here the specific impact of vegan, vegetarian, and omnivore feeding type on the composition of gut microbiota of 101 adults was investigated among groups homogeneous for variables known to have a role in modulating gut microbial composition such as age, anthropometric variables, ethnicity, and geographic area. The results displayed a picture where the three different dietetic profiles could be well distinguished on the basis of participant's dietetic regimen. Regarding the gut microbiota; vegetarians had a significantly greater richness compared to omnivorous. Moreover, counts of Bacteroidetes related operational taxonomic units (OTUs) were greater in vegans and vegetarians compared to omnivores. Interestingly considering the whole bacterial community composition the three cohorts were unexpectedly similar, which is probably due to their common intake in terms of nutrients rather than food, e.g., high fat content and reduced protein and carbohydrate intake. This finding suggests that fundamental nutritional choices such as vegan, vegetarian, or omnivore do influence the microbiota but do not allow to infer conclusions on gut microbial composition, and suggested the possibility for a preferential impact of other variables, probably related to the general life style on shaping human gut microbial community in spite of dietary influence. Consequently, research were individuals are categorized on the basis of their claimed feeding types is of limited use for scientific studies, since it appears to be oversimplified.

Dysbiosis Across Multiple Body Sites in Critically Ill Adult Surgical Patients

The microbiota of critically ill patients likely undergoes dramatic changes but has not been rigorously studied with a culture-independent high-throughput approach. The aim of this study was to characterize spatial and temporal variation in the microbiota of critically ill patients. Trauma and acute surgery patients admitted to the intensive care unit (ICU) were sampled at five body sites (stool, tongue, skin, trachea, urine) every 3 to 4 days. A mean of 10.8 samples was collected from 32 patients with a mean sampling period of 8.8 days. Bacterial 16S rRNA sequences were amplified and sequenced for microbiota analyses. Results were compared to data from unhospitalized adult participants in the American Gut and Human Microbiome Projects. Relative to healthy adults, alpha diversity was decreased in ICU gut and skin samples at all time points. Diversity in tongue swabs decreased over time. Beta diversity measures indicated differences in community membership between critically ill and healthy adults at each body site. Taxonomic alterations in the ICU included depletion of important commensal bacteria such as Faecalibacterium in GI samples and Corynebacterium in skin swabs and enrichment with pathogens such as Enterococcus, Mycoplasma, and Staphylococcus. A high proportion of ICU sample sets contained pathogens present simultaneously at three body sites indicating widespread colonization. In several cases, clinically relevant airway infections were preceded by the appearance of the causative pathogen in tracheal microbiome profiles. These results demonstrate that the microbiome of critically ill patients undergoes a loss of diversity, loss of site specificity, and a shift toward dominant pathogens. These changes may provide opportunities to precisely modulate the microbiome and thereby improve patient outcomes.

Exploring the microbiome in health and disease

The analysis of human microbiome is an exciting and rapidly expanding field of research. In the past decade, the biological relevance of the microbiome for human health has become evident. Microbiome comprises a complex collection of microorganisms, with their genes and metabolites, colonizing different body niches. It is now well known that the microbiome interacts with its host, assisting in the bioconversion of nutrients and detoxification, supporting immunity, protecting against pathogenic microbes, and maintaining health. Remarkable new findings showed that our microbiome not only primarily affects the health and function of the gastrointestinal tract but also has a strong influence on general body health through its close interaction with the nervous system and the lung. Therefore, a perfect and sensitive balanced interaction of microbes with the host is required for a healthy body. In fact, growing evidence suggests that the dynamics and function of the indigenous microbiota can be influenced by many factors, including genetics, diet, age, and toxicological agents like cigarette smoke, environmental contaminants, and drugs. The disruption of this balance, that is called dysbiosis, is associated with a plethora of diseases, including metabolic diseases, inflammatory bowel disease, chronic obstructive pulmonary disease, periodontitis, skin diseases, and neurological disorders. The importance of the host microbiome for the human health has also led to the emergence of novel therapeutic approaches focused on the intentional manipulation of the microbiota, either by restoring missing functions or eliminating harmful roles. In the present review, we outline recent studies devoted to elucidate not only the role of microbiome in health conditions and the possible link with various types of diseases but also the influence of various toxicological factors on the microbial composition and function.

Elucidation of complexity and prediction of interactions in microbial communities

Microorganisms engage in complex interactions with other members of the microbial community, higher organisms as well as their environment. However, determining the exact nature of these interactions can be challenging due to the large number of members in these communities and the manifold of interactions they can engage in. Various omic data, such as 16S rRNA gene sequencing, shotgun metagenomics, metatranscriptomics, metaproteomics and metabolomics, have been deployed to unravel the community structure, interactions and resulting community dynamics in situ. Interpretation of these multi-omic data often requires advanced computational methods. Modelling approaches are powerful tools to integrate, contextualize and interpret experimental data, thus shedding light on the underlying processes shaping the microbiome. Here, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.

Role of human microbiome and selected bacterial infections in the pathogenesis of rheumatoid arthritis

Microorganisms inhabiting human body form a complex ecosystem. The mutual influence of the microbiome and the immune system of the host constitute the basis for numerous diseases, e.g. pseudomembranous colitis, inflammatory bowel disease, type 1 diabetes, atopic diseases, obesity, reactive arthritis. New molecular diagnostic methods and multi-center studies may help in understanding of the role of microbiota in health and disease. Rheumatoid arthritis has a multi-faceted etiology, and its causes are not entirely understood. There are indications for the influence of microbiomes of oral cavity, intestines, lungs and urinary tract on the development of rheumatoid arthritis. Interactions between microorganisms and human immune system play role in the pathogenesis of the disease.

The Human Microbiome and Understanding the 16S rRNA Gene in Translational Nursing Science

BACKGROUND

As more is understood regarding the human microbiome, it is increasingly important for nurse scientists and healthcare practitioners to analyze these microbial communities and their role in health and disease. 16S rRNA sequencing is a key methodology in identifying these bacterial populations that has recently transitioned from use primarily in research to having increased utility in clinical settings.

OBJECTIVES

The objectives of this review are to (a) describe 16S rRNA sequencing and its role in answering research questions important to nursing science; (b) provide an overview of the oral, lung, and gut microbiomes and relevant research; and (c) identify future implications for microbiome research and 16S sequencing in translational nursing science.

DISCUSSION

Sequencing using the 16S rRNA gene has revolutionized research and allowed scientists to easily and reliably characterize complex bacterial communities. This type of research has recently entered the clinical setting, one of the best examples involving the use of 16S sequencing to identify resistant pathogens, thereby improving the accuracy of bacterial identification in infection control. Clinical microbiota research and related requisite methods are of particular relevance to nurse scientists-individuals uniquely positioned to utilize these techniques in future studies in clinical settings.

Sampling Terrestrial Environments for Bacterial Polyketides

Bacterial polyketides are highly biologically active molecules that are frequently used as drugs, particularly as antibiotics and anticancer agents, thus the discovery of new polyketides is of major interest. Since the 1980s discovery of polyketides has slowed dramatically due in large part to the repeated rediscovery of known compounds. While recent scientific and technical advances have improved our ability to discover new polyketides, one key area has been under addressed, namely the distribution of polyketide-producing bacteria in the environment. Identifying environments where producing bacteria are abundant and diverse should improve our ability to discover (bioprospect) new polyketides. This review summarizes for the bioprospector the state-of-the-field in terrestrial microbial ecology. It provides insight into the scientific and technical challenges limiting the application of microbial ecology discoveries for bioprospecting and summarizes key developments in the field that will enable more effective bioprospecting. The major recent efforts by researchers to sample new environments for polyketide discovery is also reviewed and key emerging environments such as insect associated bacteria, desert soils, disease suppressive soils, and caves are highlighted. Finally strategies for taking and characterizing terrestrial samples to help maximize discovery efforts are proposed and the inclusion of non-actinomycetal bacteria in any terrestrial discovery strategy is recommended.

The salivary microbiome is altered in the presence of a high salivary glucose concentration

Background

Type II diabetes (T2D) has been associated with changes in oral bacterial diversity and frequency. It is not known whether these changes are part of the etiology of T2D, or one of its effects.

Methods

We measured the glucose concentration, bacterial counts, and relative frequencies of 42 bacterial species in whole saliva samples from 8,173 Kuwaiti adolescents (mean age 10.00 ± 0.67 years) using DNA probe analysis. In addition, clinical data related to obesity, dental caries, and gingivitis were collected. Data were compared between adolescents with high salivary glucose (HSG; glucose concentration ≥ 1.0 mg/d, n = 175) and those with low salivary glucose (LSG, glucose concentration < 0.1 mg/dL n = 2,537).

Results

HSG was associated with dental caries and gingivitis in the study population. The overall salivary bacterial load in saliva decreased with increasing salivary glucose concentration. Under HSG conditions, the bacterial count for 35 (83%) of 42 species was significantly reduced, and relative bacterial frequencies in 27 species (64%) were altered, as compared with LSG conditions. These alterations were stronger predictors of high salivary glucose than measures of oral disease, obesity, sleep or fitness.

Conclusions

HSG was associated with a reduction in overall bacterial load and alterations to many relative bacterial frequencies in saliva when compared with LSG in samples from adolescents. We propose that hyperglycemia due to obesity and/or T2D results in HSG and subsequent acidification of the oral environment, leading to a generalized perturbation in the oral microbiome. This suggests a basis for the observation that hyperglycemia is associated with an increased risk of dental erosion, dental caries, and gingivitis. We conclude that HSG in adolescents may be predicted from salivary microbial diversity or frequency, and that the changes in the oral microbial composition seen in adolescents with developing metabolic disease may the consequence of hyperglycemia.

Motivations of participants in the citizen science of microbiomics: data from the British Gut Project

PURPOSE

The establishment of databases for research in human microbiomics is dependent on the recruitment of sufficient numbers and diversity of participants. Factors that support or impede participant recruitment in studies of this type have not yet been studied.

METHODS

We report the results of a survey aimed at establishing the motivations of participants in the British Gut Project, a research project that relies on volunteers to provide samples and to help fund the project.

RESULTS

The two most frequently reported motivations for participation were altruism and solidarity. Low education levels appeared to be a recruitment obstacle. More than half of our 151 respondents said they would participate in further citizen-science projects; 38% said they would not participate in a similar project if it was for-profit or in a project that did not release data sets in repositories accessible to scientists (30%).

CONCLUSIONS

The desire to take part in research was reported as a key motivation for participation in the British Gut Project (BGP). Such prosocial motivations can be mobilized for the establishment of large data sets for research.Genet Med advance online publication 26 January 2017.