Intestinal Blastocystis is linked to healthier diets and more favorable cardiometabolic outcomes in 56,989 individuals from 32 countries.

Diet impacts human health, influencing body adiposity and the risk of developing cardiometabolic diseases. The gut microbiome is a key player in the diet-health axis, but while its bacterial fraction is widely studied, the role of micro-eukaryotes, including Blastocystis, is underexplored. We performed a global-scale analysis on 56,989 metagenomes and showed that human Blastocystis exhibits distinct prevalence patterns linked to geography, lifestyle, and dietary habits. Blastocystis presence defined a specific bacterial signature and was positively associated with more favorable cardiometabolic profiles and negatively with obesity (p < 1e-16) and disorders linked to altered gut ecology (p < 1e-8). In a diet intervention study involving 1,124 individuals, improvements in dietary quality were linked to weight loss and increases in Blastocystis prevalence (p = 0.003) and abundance (p < 1e-7). Our findings suggest a potentially beneficial role for Blastocystis, which may help explain personalized host responses to diet and downstream disease etiopathogenesis.

Discovery of bioactive microbial gene products in inflammatory bowel disease.

Microbial communities and their associated bioactive compounds1-3 are often disrupted in conditions such as the inflammatory bowel diseases (IBD)4. However, even in well-characterized environments (for example, the human gastrointestinal tract), more than one-third of microbial proteins are uncharacterized and often expected to be bioactive5-7. Here we systematically identified more than 340,000 protein families as potentially bioactive with respect to gut inflammation during IBD, about half of which have not to our knowledge been functionally characterized previously on the basis of homology or experiment. To validate prioritized microbial proteins, we used a combination of metagenomics, metatranscriptomics and metaproteomics to provide evidence of bioactivity for a subset of proteins that are involved in host and microbial cell-cell communication in the microbiome; for example, proteins associated with adherence or invasion processes, and extracellular von Willebrand-like factors. Predictions from high-throughput data were validated using targeted experiments that revealed the differential immunogenicity of prioritized Enterobacteriaceae pilins and the contribution of homologues of von Willebrand factors to the formation of Bacteroides biofilms in a manner dependent on mucin levels. This methodology, which we term MetaWIBELE (workflow to identify novel bioactive elements in the microbiome), is generalizable to other environmental communities and human phenotypes. The prioritized results provide thousands of candidate microbial proteins that are likely to interact with the host immune system in IBD, thus expanding our understanding of potentially bioactive gene products in chronic disease states and offering a rational compendium of possible therapeutic compounds and targets.

High-sensitivity pattern discovery in large, paired multiomic datasets.

MOTIVATION: Modern biological screens yield enormous numbers of measurements, and identifying and interpreting statistically significant associations among features are essential. In experiments featuring multiple high-dimensional datasets collected from the same set of samples, it is useful to identify groups of associated features between the datasets in a way that provides high statistical power and false discovery rate (FDR) control. RESULTS: Here, we present a novel hierarchical framework, HAllA (Hierarchical All-against-All association testing), for structured association discovery between paired high-dimensional datasets. HAllA efficiently integrates hierarchical hypothesis testing with FDR correction to reveal significant linear and non-linear block-wise relationships among continuous and/or categorical data. We optimized and evaluated HAllA using heterogeneous synthetic datasets of known association structure, where HAllA outperformed all-against-all and other block-testing approaches across a range of common similarity measures. We then applied HAllA to a series of real-world multiomics datasets, revealing new associations between gene expression and host immune activity, the microbiome and host transcriptome, metabolomic profiling and human health phenotypes. AVAILABILITY AND IMPLEMENTATION: An open-source implementation of HAllA is freely available at http://huttenhower.sph.harvard.edu/halla along with documentation, demo datasets and a user group. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multivariable association discovery in population-scale meta-omics studies.

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.

Species-level functional profiling of metagenomes and metatranscriptomes.

Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types.

bioBakery: a meta'omic analysis environment.

Summary: bioBakery is a meta'omic analysis environment and collection of individual software tools with the capacity to process raw shotgun sequencing data into actionable microbial community feature profiles, summary reports, and publication-ready figures. It includes a collection of pre-configured analysis modules also joined into workflows for reproducibility. Availability and implementation: bioBakery (http://huttenhower.sph.harvard.edu/biobakery) is publicly available for local installation as individual modules and as a virtual machine image. Each individual module has been developed to perform a particular task (e.g. quantitative taxonomic profiling or statistical analysis), and they are provided with source code, tutorials, demonstration data, and validation results; the bioBakery virtual image includes the entire suite of modules and their dependencies pre-installed. Images are available for both Amazon EC2 and Google Compute Engine. All software is open source under the MIT license. bioBakery is actively maintained with a support group at biobakery-users@googlegroups.com and new tools being added upon their release. Contact: chuttenh@hsph.harvard.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Divergence of Drosophila melanogaster repeatomes in response to a sharp microclimate contrast in Evolution Canyon, Israel.

Repeat sequences, especially mobile elements, make up large portions of most eukaryotic genomes and provide enormous, albeit commonly underappreciated, evolutionary potential. We analyzed repeatomes of Drosophila melanogaster that have been diverging in response to a microclimate contrast in Evolution Canyon (Mount Carmel, Israel), a natural evolutionary laboratory with two abutting slopes at an average distance of only 200 m, which pose a constant ecological challenge to their local biotas. Flies inhabiting the colder and more humid north-facing slope carried about 6% more transposable elements than those from the hot and dry south-facing slope, in parallel to a suite of other genetic and phenotypic differences between the two populations. Nearly 50% of all mobile element insertions were slope unique, with many of them disrupting coding sequences of genes critical for cognition, olfaction, and thermotolerance, consistent with the observed patterns of thermotolerance differences and assortative mating.

Large scale comparison of non-human sequences in human sequencing data.

Several studies have demonstrated that unmapped reads in next generation sequencing data could be used to identify infectious agents or structural variants, but there has been no intensive effort to analyze and classify all non-human sequences found in individual large data sets. To identify commonality in non-human sequences by infectious agents and putative contamination events, we analyzed non-human sequences in 150 genomic sequencing data files from the 1000 Genomes Project and observed that 0.13% of reads on average showed similarities to non-human genomes. We compared results among different sample groups divided based on ethnicities, sequencing centers and enrichment methods (whole genome sequencing vs. exome sequencing) and found that sequencing centers had specific signatures of contaminating genomes as 'time stamps'. We also observed many unmapped reads that falsely indicated contamination because of the high similarity of human sequences to sequences in non-human genome assemblies such as mouse and Nicotiana.

A two-cohort study on the association between the gut microbiota and bone density, microarchitecture, and strength.

The gut microbiome affects the inflammatory environment through effects on T-cells, which influence the production of immune mediators and inflammatory cytokines that stimulate osteoclastogenesis and bone loss in mice. However, there are few large human studies of the gut microbiome and skeletal health. We investigated the association between the human gut microbiome and high resolution peripheral quantitative computed tomography (HR-pQCT) scans of the radius and tibia in two large cohorts; Framingham Heart Study (FHS [n=1227, age range: 32 - 89]), and the Osteoporosis in Men Study (MrOS [n=836, age range: 78 - 98]). Stool samples from study participants underwent amplification and sequencing of the V4 hypervariable region of the 16S rRNA gene. The resulting 16S rRNA sequencing data were processed separately for each cohort, with the DADA2 pipeline incorporated in the16S bioBakery workflow. Resulting amplicon sequence variants were assigned taxonomies using the SILVA reference database. Controlling for multiple covariates, we tested for associations between microbial taxa abundances and HR-pQCT measures using general linear models as implemented in microbiome multivariable association with linear model (MaAslin2). Abundance of 37 microbial genera in FHS, and 4 genera in MrOS, were associated with various skeletal measures (false discovery rate [FDR] ≤ 0.1) including the association of DTU089 with bone measures, which was independently replicated in the two cohorts. A meta-analysis of the taxa-bone associations further revealed (FDR ≤ 0.25) that greater abundances of the genera; Akkermansia and DTU089, were associated with lower radius total vBMD, and tibia cortical vBMD respectively. Conversely, higher abundances of the genera; Lachnospiraceae NK4A136 group, and Faecalibacterium were associated with greater tibia cortical vBMD. We also investigated functional capabilities of microbial taxa by testing for associations between predicted (based on 16S rRNA amplicon sequence data) metabolic pathways abundance and bone phenotypes in each cohort. While there were no concordant functional associations observed in both cohorts, a meta-analysis revealed 8 pathways including the super-pathway of histidine, purine, and pyrimidine biosynthesis, associated with bone measures of the tibia cortical compartment. In conclusion, our findings suggest that there is a link between the gut microbiome and skeletal metabolism.

Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4.

Metagenomic assembly enables new organism discovery from microbial communities, but it can only capture few abundant organisms from most metagenomes. Here we present MetaPhlAn 4, which integrates information from metagenome assemblies and microbial isolate genomes for more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01 M prokaryotic reference and metagenome-assembled genomes, we define unique marker genes for 26,970 species-level genome bins, 4,992 of them taxonomically unidentified at the species level. MetaPhlAn 4 explains ~20% more reads in most international human gut microbiomes and >40% in less-characterized environments such as the rumen microbiome and proves more accurate than available alternatives on synthetic evaluations while also reliably quantifying organisms with no cultured isolates. Application of the method to >24,500 metagenomes highlights previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mouse microbiomes and shows that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains.

Alterations in the gut microbiome implicate key taxa and metabolic pathways across inflammatory arthritis phenotypes.

Musculoskeletal diseases affect up to 20% of adults worldwide. The gut microbiome has been implicated in inflammatory conditions, but large-scale metagenomic evaluations have not yet traced the routes by which immunity in the gut affects inflammatory arthritis. To characterize the community structure and associated functional processes driving gut microbial involvement in arthritis, the Inflammatory Arthritis Microbiome Consortium investigated 440 stool shotgun metagenomes comprising 221 adults diagnosed with rheumatoid arthritis, ankylosing spondylitis, or psoriatic arthritis and 219 healthy controls and individuals with joint pain without an underlying inflammatory cause. Diagnosis explained about 2% of gut taxonomic variability, which is comparable in magnitude to inflammatory bowel disease. We identified several candidate microbes with differential carriage patterns in patients with elevated blood markers for inflammation. Our results confirm and extend previous findings of increased carriage of typically oral and inflammatory taxa and decreased abundance and prevalence of typical gut clades, indicating that distal inflammatory conditions, as well as local conditions, correspond to alterations to the gut microbial composition. We identified several differentially encoded pathways in the gut microbiome of patients with inflammatory arthritis, including changes in vitamin B salvage and biosynthesis and enrichment of iron sequestration. Although several of these changes characteristic of inflammation could have causal roles, we hypothesize that they are mainly positive feedback responses to changes in host physiology and immune homeostasis. By connecting taxonomic alternations to functional alterations, this work expands our understanding of the shifts in the gut ecosystem that occur in response to systemic inflammation during arthritis.

Sporadic breast cancer patients' germline DNA exhibit an AT-rich microsatellite signature.

Using a custom CGH-like oligonucleotide array to measure the global microsatellite content in the genomes of 72 cancer, cancer-free, and high risk patient and cell line samples (56 germline DNA and 16 in tumor or tumor cell line DNA) we found a unique, reproducible, and statistically significant pattern of 18 motif-specific microsatellite families (out of 962 possible 1-6 mer repeats) in breast cancer patient germline and tumor DNA, but not in germline DNA of cancer-free volunteer controls or in breast cancer patients with BRCA1/2 mutations. These high-similarity A/T rich repetitive motifs were also more pronounced in the germlines and tumors of colon cancer tumor patients (3/6 samples) and microsatellite unstable colon cancer cell lines; however, germline DNA of sporadic breast cancer patients exhibited the largest global content shift for those motifs with extreme AT/GC ratios. These results indicate that global microsatellite variability is complex, suggest the existence of a previously unknown genomic destabilization mechanism in breast cancer patients' germline DNA, and warrant further testing of such microsatellite variability as a predictor of future breast cancer development.

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3.

Culture-independent analyses of microbial communities have progressed dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1262 metagenomes) and IBD (1635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.

Overview of the Microbiome Among Nurses study (Micro-N) as an example of prospective characterization of the microbiome within cohort studies.

A lack of prospective studies has been a major barrier for assessing the role of the microbiome in human health and disease on a population-wide scale. To address this significant knowledge gap, we have launched a large-scale collection targeting fecal and oral microbiome specimens from 20,000 women within the Nurses' Health Study II cohort (the Microbiome Among Nurses study, or Micro-N). Leveraging the rich epidemiologic data that have been repeatedly collected from this cohort since 1989; the established biorepository of archived blood, urine, buccal cell, and tumor tissue specimens; the available genetic and biomarker data; the cohort's ongoing follow-up; and the BIOM-Mass microbiome research platform, Micro-N furnishes unparalleled resources for future prospective studies to interrogate the interplay between host, environmental factors, and the microbiome in human health. These prospectively collected materials will provide much-needed evidence to infer causality in microbiome-associated outcomes, paving the way toward development of microbiota-targeted modulators, preventives, diagnostics and therapeutics. Here, we describe a generalizable, scalable and cost-effective platform used for stool and oral microbiome specimen and metadata collection in the Micro-N study as an example of how prospective studies of the microbiome may be carried out.

Comparative Global Gene Expression Profiles of Wild-Type Yersinia pestis CO92 and Its Braun Lipoprotein Mutant at Flea and Human Body Temperatures.

Braun/murein lipoprotein (Lpp) is involved in inflammatory responses and septic shock. We previously characterized a Deltalpp mutant of Yersinia pestis CO92 and found that this mutant was defective in surviving in macrophages and was attenuated in a mouse inhalation model of plague when compared to the highly virulent wild-type (WT) bacterium. We performed global transcriptional profiling of WT Y. pestis and its Deltalpp mutant using microarrays. The organisms were cultured at 26 and 37 degrees Celsius to simulate the flea vector and mammalian host environments, respectively. Our data revealed vastly different effects of lpp mutation on the transcriptomes of Y. pestis grown at 37 versus 26 degrees C. While the absence of Lpp resulted mainly in the downregulation of metabolic genes at 26 degrees C, the Y. pestis Deltalpp mutant cultured at 37 degrees C exhibited profound alterations in stress response and virulence genes, compared to WT bacteria. We investigated one of the stress-related genes (htrA) downregulated in the Deltalpp mutant relative to WT Y. pestis. Indeed, complementation of the Deltalpp mutant with the htrA gene restored intracellular survival of the Y. pestis Deltalpp mutant. Our results support a role for Lpp in Y. pestis adaptation to the host environment, possibly via transcriptional activation of htrA.

Transcriptional profile of isoproterenol-induced cardiomyopathy and comparison to exercise-induced cardiac hypertrophy and human cardiac failure.

BACKGROUND: Isoproterenol-induced cardiac hypertrophy in mice has been used in a number of studies to model human cardiac disease. In this study, we compared the transcriptional response of the heart in this model to other animal models of heart failure, as well as to the transcriptional response of human hearts suffering heart failure. RESULTS: We performed microarray analyses on RNA from mice with isoproterenol-induced cardiac hypertrophy and mice with exercise-induced physiological hypertrophy and identified 865 and 2,534 genes that were significantly altered in pathological and physiological cardiac hypertrophy models, respectively. We compared our results to 18 different microarray data sets (318 individual arrays) representing various other animal models and four human cardiac diseases and identified a canonical set of 64 genes that are generally altered in failing hearts. We also produced a pairwise similarity matrix to illustrate relatedness of animal models with human heart disease and identified ischemia as the human condition that most resembles isoproterenol treatment. CONCLUSION: The overall patterns of gene expression are consistent with observed structural and molecular differences between normal and maladaptive cardiac hypertrophy and support a role for the immune system (or immune cell infiltration) in the pathology of stress-induced hypertrophy. Cross-study comparisons such as the results presented here provide targets for further research of cardiac disease that might generally apply to maladaptive cardiac stresses and are also a means of identifying which animal models best recapitulate human disease at the transcriptional level.

Author Correction: Gut microbiome structure and metabolic activity in inflammatory bowel disease.

In the Supplementary Tables 2, 4 and 6 originally published with this Article, the authors mistakenly included sample identifiers in the form of UMCGs rather than UMCG IBDs in the validation cohort; this has now been amended.

Gut microbiome structure and metabolic activity in inflammatory bowel disease.

The inflammatory bowel diseases (IBDs), which include Crohn's disease (CD) and ulcerative colitis (UC), are multifactorial chronic conditions of the gastrointestinal tract. While IBD has been associated with dramatic changes in the gut microbiota, changes in the gut metabolome-the molecular interface between host and microbiota-are less well understood. To address this gap, we performed untargeted metabolomic and shotgun metagenomic profiling of cross-sectional stool samples from discovery (n = 155) and validation (n = 65) cohorts of CD, UC and non-IBD control patients. Metabolomic and metagenomic profiles were broadly correlated with faecal calprotectin levels (a measure of gut inflammation). Across >8,000 measured metabolite features, we identified chemicals and chemical classes that were differentially abundant in IBD, including enrichments for sphingolipids and bile acids, and depletions for triacylglycerols and tetrapyrroles. While > 50% of differentially abundant metabolite features were uncharacterized, many could be assigned putative roles through metabolomic 'guilt by association' (covariation with known metabolites). Differentially abundant species and functions from the metagenomic profiles reflected adaptation to oxidative stress in the IBD gut, and were individually consistent with previous findings. Integrating these data, however, we identified 122 robust associations between differentially abundant species and well-characterized differentially abundant metabolites, indicating possible mechanistic relationships that are perturbed in IBD. Finally, we found that metabolome- and metagenome-based classifiers of IBD status were highly accurate and, like the vast majority of individual trends, generalized well to the independent validation cohort. Our findings thus provide an improved understanding of perturbations of the microbiome-metabolome interface in IBD, including identification of many potential diagnostic and therapeutic targets.

Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.

Inflammatory bowel disease (IBD) is a group of chronic diseases of the digestive tract that affects millions of people worldwide. Genetic, environmental and microbial factors have been implicated in the onset and exacerbation of IBD. However, the mechanisms associating gut microbial dysbioses and aberrant immune responses remain largely unknown. The integrative Human Microbiome Project seeks to close these gaps by examining the dynamics of microbiome functionality in disease by profiling the gut microbiomes of >100 individuals sampled over a 1-year period. Here, we present the first results based on 78 paired faecal metagenomes and metatranscriptomes, and 222 additional metagenomes from 59 patients with Crohn's disease, 34 with ulcerative colitis and 24 non-IBD control patients. We demonstrate several cases in which measures of microbial gene expression in the inflamed gut can be informative relative to metagenomic profiles of functional potential. First, although many microbial organisms exhibited concordant DNA and RNA abundances, we also detected species-specific biases in transcriptional activity, revealing predominant transcription of pathways by individual microorganisms per host (for example, by Faecalibacterium prausnitzii). Thus, a loss of these organisms in disease may have more far-reaching consequences than suggested by their genomic abundances. Furthermore, we identified organisms that were metagenomically abundant but inactive or dormant in the gut with little or no expression (for example, Dialister invisus). Last, certain disease-specific microbial characteristics were more pronounced or only detectable at the transcript level, such as pathways that were predominantly expressed by different organisms in patients with IBD (for example, Bacteroides vulgatus and Alistipes putredinis). This provides potential insights into gut microbial pathway transcription that can vary over time, inducing phenotypical changes that are complementary to those linked to metagenomic abundances. The study's results highlight the strength of analysing both the activity and the presence of gut microorganisms to provide insight into the role of the microbiome in IBD.