Geographic social vulnerability is associated with the alpha diversity of the human microbiome.

IMPORTANCE: As a risk factor for conditions related to the microbiome, understanding the role of SVI on microbiome diversity may assist in identifying public health implications for microbiome research. Here we found, using a sub-sample of the Human Microbiome Project phase 1 cohort, that SVI was linked to microbiome diversity across body sites and that SVI may influence race/ethnicity-based differences in diversity. Our findings, build on the current knowledge regarding the role of human geography in microbiome research, suggest that measures of geographic social vulnerability be considered as additional contextual factors when exploring microbiome alpha diversity.

A Resistome Roadmap: From the Human Body to Pristine Environments.

A comprehensive characterization of the human body resistome [sets of antibiotic resistance genes (ARGs)] is yet to be done and paramount for addressing the antibiotic microbial resistance threat. Here, we study the resistome of 771 samples from five major body parts (skin, nares, vagina, gut, and oral cavity) of healthy subjects from the Human Microbiome Project (HMP) and addressed the potential dispersion of ARGs in pristine environments. A total of 28,714 ARGs belonging to 235 different ARG types were found in the HMP proteome dataset (n = 9.1 × 107 proteins analyzed). Our study reveals a distinct resistome profile (ARG type and abundance) between body sites and high interindividual variability. Nares had the highest ARG load (≈5.4 genes/genome) followed by the oral cavity, whereas the gut showed one of the highest ARG richness (shared with nares) but the lowest abundance (≈1.3 genes/genome). The fluroquinolone resistance genes were the most abundant in the human body, followed by macrolide-lincosamide-streptogramin (MLS) or tetracycline. Most ARGs belonged to common bacterial commensals and multidrug resistance trait were predominant in the nares and vagina. Many ARGs detected here were considered as low risk for human health, whereas only a few of them, such as BlaZ, dfrA14, dfrA17, or tetM, were classified as high-risk ARG. Our data also provide hope, since the spread of common ARG from the human body to pristine environments (n = 271 samples; 77 Gb of sequencing data and 2.1 × 108 proteins analyzed) thus far remains very unlikely (only one case found in an autochthonous bacterium from a pristine environment). These findings broaden our understanding of ARG in the context of the human microbiome and the One-Health Initiative of WHO uniting human host-microbes and environments as a whole.

Machine learning clustering and classification of human microbiome source body sites.

Distinct microbial signatures associated with specific human body sites can play a role in the identification of biological materials recovered from the crime scene, but at present, methods that have capability to predict origin of biological materials based on such signatures are limited. Metagenomic sequencing and machine learning (ML) offer a promising enhancement to current identification protocols. We use ML for forensic source body site identification using shotgun metagenomic sequenced data to verify the presence of microbiomic signatures capable of discriminating between source body sites and then show that accurate prediction is possible. The consistency between cluster membership and actual source body site (purity) exceeded 99% at the genus taxonomy using off-the-shelf ML clustering algorithms. Similar results were obtained at the family level. Accurate predictions were observed for genus, family, and order taxonomies, as well as with a core set of 51 genera. The accurate outcomes from our replicable process should encourage forensic scientists to seriously consider integrating ML predictors into their source body site identification protocols.

Opportunistic detection of Fusobacterium nucleatum as a marker for the early gut microbial dysbiosis.

BACKGROUND: The essential roles of gut microbiome have been emphasized in modulating human health and disease. Fusobacterium nucleatum (F. nucleatum), an obligate Gram-negative microorganism residing in oral cavity, gastrointestinal tract and elsewhere, has been recently considered as a potential oncobacterium associated with human cancers. However, the consequence of its enrichment was not extensively explored in terms of microbial homeostasis and stability at the early stage of disease development. RESULT: Our analysis on longitudinal metagenomic data generated by the Integrative Human Microbiome Project (iHMP) showed that F. nucleatum was frequently found in inflammatory bowel diseases (IBD) subjects with reduced microbial diversity. Using non-parametric logarithmic linear discriminant analysis (LDA) effect size (LEfSe) algorithm, 12 IBD- and 14 non-IBD-specific bacterial species were identified in the fecal metagenome and the IBD-specific ones were over-represented in the F. nucleatum-experienced subjects during long-term surveillance. In addition, F. nucleatum experience severely abrogated intra-personal stability of microbiome in IBD patients and induced highly variable gut microbiome between subjects. From the longitudinal comparison between microbial distributions prior and posterior to F. nucleatum detection, 41 species could be proposed as indicative "classifiers" for dysbiotic gut state. By multiple logistic regression models established on these classifiers, the high probability of experiencing F. nucleatum was significantly correlated with decreased alpha-diversity and increased number of biomarker species for IBD and colorectal cancer (CRC). Finally, microbial clustering confirmed that biomarker species for IBD and non-IBD conditions as well as CRC signature markers were well distinguishable and could be utilized for explaining gut symbiosis and dysbiosis. CONCLUSION: F. nucleatum opportunistically appeared under early dysbiotic condition in gut, and discriminative classifier species associated with F. nucleatum were successfully applied to predict microbial alterations in both IBD and non-IBD conditions. Our prediction model and microbial classifier biomarkers for estimating gut dysbiosis should provide a novel aspect of microbial homeostasis/dynamics and useful information on non-invasive biomarker screening.

Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz.

The rich data produced by the second phase of the Human Microbiome Project (iHMP) offers a unique opportunity to test hypotheses that interactions between microbial communities and a human host might impact an individual's health or disease status. In this work we describe infrastructure that integrates Metaviz, an interactive microbiome data analysis and visualization tool, with the iHMP Data Coordination Center web portal and the HMP2Data R/Bioconductor package. We describe integrative statistical and visual analyses of two datasets from iHMP using Metaviz along with the metagenomeSeq R/Bioconductor package for statistical analysis of differential abundance analysis. These use cases demonstrate the utility of a combined approach to access and analyze data from this resource.

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.

HMP16SData: Efficient Access to the Human Microbiome Project Through Bioconductor.

Phase 1 of the Human Microbiome Project (HMP) investigated 18 body subsites of 242 healthy American adults to produce the first comprehensive reference for the composition and variation of the "healthy" human microbiome. Publicly available data sets from amplicon sequencing of two 16S ribosomal RNA variable regions, with extensive controlled-access participant data, provide a reference for ongoing microbiome studies. However, utilization of these data sets can be hindered by the complex bioinformatic steps required to access, import, decrypt, and merge the various components in formats suitable for ecological and statistical analysis. The HMP16SData package provides count data for both 16S ribosomal RNA variable regions, integrated with phylogeny, taxonomy, public participant data, and controlled participant data for authorized researchers, using standard integrative Bioconductor data objects. By removing bioinformatic hurdles of data access and management, HMP16SData enables epidemiologists with only basic R skills to quickly analyze HMP data.

Invited Commentary: Improving the Accessibility of Human Microbiome Project Data Through Integration With R/Bioconductor.

Alterations in the composition of the microbiota have been implicated in many diseases. The Human Microbiome Project (HMP) provides a comprehensive reference data set of the "normal" human microbiome of 242 healthy adults at 5 major body sites. The HMP used both 16S ribosomal RNA gene sequencing and whole-genome metagenomic sequencing to profile the subjects' microbial communities. However, accessing and analyzing the HMP data set still presents technical and bioinformatic challenges, given that researchers must import the microbiome data, integrate phylogenetic trees, and access and merge public and restricted metadata. The HMP16SData R/Bioconductor package developed by Schiffer et al. (Am J Epidemiol. 2019;188(6):1023-1026) greatly simplifies access to the HMP data by combining 16S taxonomic abundance data, public patient metadata, and phylogenetic trees as a single data object. The authors also provide an interface for users with approved Database of Genotypes and Phenotypes (dbGaP) projects to easily retrieve and merge the controlled-access HMP metadata. This package has a broad range of appeal to researchers across disciplines and with various levels of expertise in using R and/or other statistical tools, which translates to improved data accessibility for public health research, with data from healthy individuals serving as a reference for disease-associated studies.

Comparison of Microbiota Variation in Korean Healthy Adolescents with Adults Suggests Notable Maturity Differences.

Comparative studies of microbiome variation in world populations and different developmental stages of organisms are essential to decipher the linkages among microbiome, health, and disease. Notably, the gut microbiota are believed to mature in early life. In this context, we compared the gut microbiota diversity in Korean adolescent healthy samples (KAHSs) to healthy Korean adults (HKAs) as well as the Human Microbiome Project healthy samples (HMPHSs), the latter being one of the largest adult cohorts, based on organismal composition, alpha- and beta-diversities, function/pathway prediction analysis, and co-occurrence networks. We found that the gut microbiota compositions, including the ratios of firmicutes to bacteroidetes, between KAHSs and HMPHSs were different, and the diversities of KAHSs were less than those of HMPHSs. The predicted functions, for example, secondary bile acid synthesis and insulin signaling of KAHSs and HMPHSs, were also significantly different. Genus-level networks showed that co-occurrences among different taxa more frequently happened in HMPHSs than in KAHSs. Even though both KAHSs and HMPHSs represent healthy microbiomes, comparisons showed substantial differences, likely implicating different diets, environments, and demographics. Interestingly, we observed lower microbial diversities and less frequent co-occurrences among different taxa in KAHSs than adult HMPHSs and HKAs. These new findings collectively suggest that the adolescent gut microbiota in the present Korean sample did not reach the extent of maturity of adult microbiota diversity. In all, further population studies of microbiome variation across geographies and developmental stages are warranted, and should usefully inform future diagnostics and therapeutics innovation targeting the microbiome.

Host genetic variation and its microbiome interactions within the Human Microbiome Project.

BACKGROUND: Despite the increasing recognition that microbial communities within the human body are linked to health, we have an incomplete understanding of the environmental and molecular interactions that shape the composition of these communities. Although host genetic factors play a role in these interactions, these factors have remained relatively unexplored given the requirement for large population-based cohorts in which both genotyping and microbiome characterization have been performed. METHODS: We performed whole-genome sequencing of 298 donors from the Human Microbiome Project (HMP) healthy cohort study to accompany existing deep characterization of their microbiomes at various body sites. This analysis yielded an average sequencing depth of 32x, with which we identified 27 million (M) single nucleotide variants and 2.3 M insertions-deletions. RESULTS: Taxonomic composition and functional potential of the microbiome covaried significantly with genetic principal components in the gastrointestinal tract and oral communities, but not in the nares or vaginal microbiota. Example associations included validation of known associations between FUT2 secretor status, as well as a variant conferring hypolactasia near the LCT gene, with Bifidobacterium longum abundance in stool. The associations of microbial features with both high-level genetic attributes and single variants were specific to particular body sites, highlighting the opportunity to find unique genetic mechanisms controlling microbiome properties in the microbial communities from multiple body sites. CONCLUSIONS: This study adds deep sequencing of host genomes to the body-wide microbiome sequences already extant from the HMP healthy cohort, creating a unique, versatile, and well-controlled reference for future studies seeking to identify host genetic modulators of the microbiome.

Pancreatic cyst fluid harbors a unique microbiome.

BACKGROUND: It is clear that specific intestinal bacteria are involved in the development of different premalignant conditions along the gastrointestinal tract. An analysis of the microbial constituents in the context of pancreatic cystic lesions has, however, as yet not been performed. This consideration prompted us to explore whether endoscopically obtained pancreatic cyst fluids (PCF) contain bacterial DNA and to determine the genera of bacteria present in such material. METHODS: Total DNA was isolated from 69 PCF samples. Bacterial 16S rRNA gene-specific PCR was performed followed by Sanger sequencing and de novo deep sequencing for the V3-V4 variable region of 16S rRNA gene. RESULTS: We observed that 98.2% of the samples were positive in conventional PCR, and that 100% of selected PCF samples (n = 33) were positive for bacterial microbiota as determined by next generation sequencing (NGS). Comprehensive NGS data analysis of PCF showed the presence of 408 genera of bacteria, of which 17 bacterial genera were uniquely abundant to PCF, when compared to the Human Microbiome Project (HMP) database and 15 bacterial microbiota were uniquely abundant in HMP only. Bacteroides spp., Escherichia/Shigella spp., and Acidaminococcus spp. which were predominant in PCF, while also a substantial Staphylococcus spp. and Fusobacterium spp. component was detected. CONCLUSION: These results reveal and characterize an apparently specific bacterial ecosystem in pancreatic cyst fluid samples and may reflect the local microbiota in the pancreas. Some taxa with potential deleterious functions are present in the bacterial abundance profiles, suggesting that the unique microbiome in this specific niche may contribute to neoplastic processes in the pancreas. Further studies are needed to explore the intricate relationship between pathophysiological status in the host pancreas and its microbiota.

A Stoichioproteomic Analysis of Samples from the Human Microbiome Project.

Ecological stoichiometry (ES) uses organism-specific elemental content to explain differences in species life histories, species interactions, community organization, environmental constraints and even ecosystem function. Although ES has been successfully applied to a range of different organisms, most emphasis on microbial ecological stoichiometry focuses on lake, ocean, and soil communities. With the recent advances in human microbiome research, however, large amounts of data are being generated that describe differences in community composition across body sites and individuals. We suggest that ES may provide a framework for beginning to understand the structure, organization, and function of human microbial communities, including why certain organisms exist at certain locations, and how they interact with both the other microbes in their environment and their human host. As a first step, we undertake a stoichioproteomic analysis of microbial communities from different body sites. Specifically, we compare and contrast the elemental composition of microbial protein samples using annotated sequencing data from 690 gut, vaginal, oral, nares, and skin samples currently available through the Human Microbiome Project. Our results suggest significant differences in both the median and variance of the carbon, oxygen, nitrogen, and sulfur contents of microbial protein samples from different locations. For example, whereas proteins from vaginal sites are high in carbon, proteins from skin and nasal sites are high in nitrogen and oxygen. Meanwhile, proteins from stool (the gut) are particularly high in sulfur content. We interpret these differences in terms of the local environments at different human body sites, including atmospheric exposure and food intake rates.

An Atlas of ß-Glucuronidases in the Human Intestinal Microbiome.

Microbiome-encoded ß-glucuronidase (GUS) enzymes play important roles in human health by metabolizing drugs in the gastrointestinal (GI) tract. The numbers, types, and diversity of these proteins in the human GI microbiome, however, remain undefined. We present an atlas of GUS enzymes comprehensive for the Human Microbiome Project GI database. We identify 3,013 total and 279 unique microbiome-encoded GUS proteins clustered into six unique structural categories. We assign their taxonomy, assess cellular localization, reveal the inter-individual variability within the 139 individuals sampled, and discover 112 novel microbial GUS enzymes. A representative in vitro panel of the most common GUS proteins by read abundances highlights structural and functional variabilities within the family, including their differential processing of smaller glucuronides and larger carbohydrates. These data provide a sequencing-to-molecular roadmap for examining microbiome-encoded enzymes essential to human health.

High-resolution microbiome profiling uncovers Fusobacterium nucleatum, Lactobacillus gasseri/johnsonii, and Lactobacillus vaginalis associated to oral and oropharyngeal cancer in saliva from HPV positive and HPV negative patients treated with surgery and chemo-radiation.

Microbiome studies show altered microbiota in head and neck squamous cell carcinoma (HNSCC), both in terms of taxonomic composition and metabolic capacity. These studies utilized a traditional bioinformatics methodology, which allows for accurate taxonomic assignment down to the genus level, but cannot accurately resolve species level membership. We applied Resphera Insight, a high-resolution methodology for 16S rRNA taxonomic assignment that is able to provide species-level context in its assignments of 16S rRNA next generation sequencing (NGS) data. Resphera Insight applied to saliva samples from HNSCC patients and healthy controls led to the discovery that a subset of HNSCC saliva samples is significantly enriched with commensal species from the vaginal flora, including Lactobacillus gasseri/johnsonii (710x higher in saliva) and Lactobacillus vaginalis (52x higher in saliva). These species were not observed in normal saliva from Johns Hopkins patients, nor in 16S rRNA NGS saliva samples from the Human Microbiome Project (HMP). Interestingly, both species were only observed in saliva from Human Papilloma Virus (HPV) positive and HPV negative oropharyngeal cancer patients. We confirmed the representation of both species in HMP data obtained from mid-vagina (n=128) and vaginal introitus (n=121) samples. Resphera Insight also led to the discovery that Fusobacterium nucleatum, an oral cavity flora commensal bacterium linked to colon cancer, is enriched (600x higher) in saliva from a subset of HNSCC patients with advanced tumors stages. Together, these high-resolution analyses on 583 samples suggest a possible role for bacterial species in the therapeutic outcome of HPV positive and HPV negative HNSCC patients.

Metabolic modeling with Big Data and the gut microbiome.

The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework - or metabolic model - on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.

A healthy gastrointestinal microbiome is dependent on dietary diversity.

BACKGROUND: Like all healthy ecosystems, richness of microbiota species characterizes the GI microbiome in healthy individuals. Conversely, a loss in species diversity is a common finding in several disease states. This biome is flooded with energy in the form of undigested and partially digested foods, and in some cases drugs and dietary supplements. Each microbiotic species in the biome transforms that energy into new molecules, which may signal messages to physiological systems of the host. SCOPE OF REVIEW: Dietary choices select substrates for species, providing a competitive advantage over other GI microbiota. The more diverse the diet, the more diverse the microbiome and the more adaptable it will be to perturbations. Unfortunately, dietary diversity has been lost during the past 50 years and dietary choices that exclude food products from animals or plants will narrow the GI microbiome further. MAJOR CONCLUSION: Additional research into expanding gut microbial richness by dietary diversity is likely to expand concepts in healthy nutrition, stimulate discovery of new diagnostics, and open up novel therapeutic possibilities.

The Microbiome: The Trillions of Microorganisms That Maintain Health and Cause Disease in Humans and Companion Animals.

The microbiome is the complex collection of microorganisms, their genes, and their metabolites, colonizing the human and animal mucosal surfaces, digestive tract, and skin. It is now well known that the microbiome interacts with its host, assisting in digestion and detoxification, supporting immunity, protecting against pathogens, and maintaining health. Studies published to date have demonstrated that healthy individuals are often colonized with different microbiomes than those with disease involving various organ systems. This review covers a brief history of the development of the microbiome field, the main objectives of the Human Microbiome Project, and the most common microbiomes inhabiting the human respiratory tract, companion animal digestive tract, and skin in humans and companion animals. The main changes in the microbiomes in patients with pulmonary, gastrointestinal, and cutaneous lesions are described.

The human gut sterolbiome: bile acid-microbiome endocrine aspects and therapeutics.

The human body is now viewed as a complex ecosystem that on a cellular and gene level is mainly prokaryotic. The mammalian liver synthesizes and secretes hydrophilic primary bile acids, some of which enter the colon during the enterohepatic circulation, and are converted into numerous hydrophobic metabolites which are capable of entering the portal circulation, returned to the liver, and in humans, accumulating in the biliary pool. Bile acids are hormones that regulate their own synthesis, transport, in addition to glucose and lipid homeostasis, and energy balance. The gut microbial community through their capacity to produce bile acid metabolites distinct from the liver can be thought of as an "endocrine organ" with potential to alter host physiology, perhaps to their own favor. We propose the term "sterolbiome" to describe the genetic potential of the gut microbiome to produce endocrine molecules from endogenous and exogenous steroids in the mammalian gut. The affinity of secondary bile acid metabolites to host nuclear receptors is described, the potential of secondary bile acids to promote tumors, and the potential of bile acids to serve as therapeutic agents are discussed.

Characterization of the human gut microbiome during travelers' diarrhea.

Alterations in the gut microbiota are correlated with ailments such as obesity, inflammatory bowel disease, and diarrhea. Up to 60% of individuals traveling from industrialized to developing countries acquire a form of secretory diarrhea known as travelers' diarrhea (TD), and enterotoxigenic Escherichia coli (ETEC) and norovirus (NoV) are the leading causative pathogens. Presumably, TD alters the gut microbiome, however the effect of TD on gut communities has not been studied. We report the first analysis of bacterial gut populations associated with TD. We examined and compared the gut microbiomes of individuals who developed TD associated with ETEC, NoV, or mixed pathogens, and TD with no pathogen identified, to healthy travelers. We observed a signature dysbiotic gut microbiome profile of high Firmicutes:Bacteroidetes ratios in the travelers who developed diarrhea, regardless of etiologic agent or presence of a pathogen. There was no significant difference in α-diversity among travelers. The bacterial composition of the microbiota of the healthy travelers was similar to the diarrheal groups, however the ß-diversity of the healthy travelers was significantly different than any pathogen-associated TD group. Further comparison of the healthy traveler microbiota to those from healthy subjects who were part of the Human Microbiome Project also revealed a significantly higher Firmicutes:Bacteriodetes ratio in the healthy travelers and significantly different ß-diversity. Thus, the composition of the gut microbiome in healthy, diarrhea-free travelers has characteristics of a dysbiotic gut, suggesting that these alterations could be associated with factors such as travel.