Obstructive sleep apnea (OSA), a common sleep disorder characterized by intermittent hypoxia and hypercapnia (IHC), increases atherosclerosis risk. However, the contribution of intermittent hypoxia (IH) or intermittent hypercapnia (IC) in promoting atherosclerosis remains unclear. Since gut microbiota and metabolites have been implicated in atherosclerosis, we examined whether IH or IC alters the microbiome and metabolome to induce a pro-atherosclerotic state. Apolipoprotein E deficient mice (ApoE-/- ), treated with IH or IC on a high-fat diet (HFD) for 10 weeks, were compared to Air controls. Atherosclerotic lesions were examined, gut microbiome was profiled using 16S rRNA gene amplicon sequencing and metabolome was assessed by untargeted mass spectrometry. In the aorta, IC-induced atherosclerosis was significantly greater than IH and Air controls (aorta, IC 11.1 ± 0.7% vs. IH 7.6 ± 0.4%, p < 0.05 vs. Air 8.1 ± 0.8%, p < 0.05). In the pulmonary artery (PA), however, IH, IC, and Air were significantly different from each other in atherosclerotic formation with the largest lesion observed under IH (PA, IH 40.9 ± 2.0% vs. IC 20.1 ± 2.6% vs. Air 12.2 ± 1.5%, p < 0.05). The most differentially abundant microbial families (p < 0.001) were Peptostreptococcaceae, Ruminococcaceae, and Erysipelotrichaceae. The most differentially abundant metabolites (p < 0.001) were tauro-β-muricholic acid, ursodeoxycholic acid, and lysophosphoethanolamine (18:0). We conclude that IH and IC (a) modulate atherosclerosis progression differently in distinct vascular beds with IC, unlike IH, facilitating atherosclerosis in both aorta and PA and (b) promote an atherosclerotic luminal gut environment that is more evident in IH than IC. We speculate that the resulting changes in the gut metabolome and microbiome interact differently with distinct vascular beds.

Colorectal cancer (CRC) is driven by genomic alterations in concert with dietary influences, with the gut microbiome implicated as an effector in disease development and progression. While meta-analyses have provided mechanistic insight into patients with CRC, study heterogeneity has limited causal associations. Using multi-omics studies on genetically controlled cohorts of mice, we identify diet as the major driver of microbial and metabolomic differences, with reductions in α diversity and widespread changes in cecal metabolites seen in high-fat diet (HFD)-fed mice. In addition, non-classic amino acid conjugation of the bile acid cholic acid (AA-CA) increased with HFD. We show that AA-CAs impact intestinal stem cell growth and demonstrate that Ileibacterium valens and Ruminococcus gnavus are able to synthesize these AA-CAs. This multi-omics dataset implicates diet-induced shifts in the microbiome and the metabolome in disease progression and has potential utility in future diagnostic and therapeutic developments.

Microbial diversity in the cystic fibrosis (CF) lung decreases over decades as pathogenic bacteria such as Pseudomonas aeruginosa take over. The dynamics of the CF microbiome and metabolome over shorter time frames, however, remain poorly studied. Here, we analyze paired microbiome and metabolome data from 594 sputum samples collected over 401 days from six adult CF subjects (subject mean = 179 days) through periods of clinical stability and 11 CF pulmonary exacerbations (CFPE). While microbiome profiles were personalized (permutational multivariate analysis of variance [PERMANOVA] r 2 = 0.79, P < 0.001), we observed significant intraindividual temporal variation that was highest during clinical stability (linear mixed-effects [LME] model, P = 0.002). This included periods where the microbiomes of different subjects became highly similar (UniFrac distance, <0.05). There was a linear increase in the microbiome alpha-diversity and in the log ratio of anaerobes to pathogens with time (n = 14 days) during the development of a CFPE (LME P = 0.0045 and P = 0.029, respectively). Collectively, comparing samples across disease states showed there was a reduction of these two measures during antibiotic treatment (LME P = 0.0096 and P = 0.014, respectively), but the stability data and CFPE data were not significantly different from each other. Metabolome alpha-diversity was higher during CFPE than during stability (LME P = 0.0085), but no consistent metabolite signatures of CFPE across subjects were identified. Virulence-associated metabolites from P. aeruginosa were temporally dynamic but were not associated with any disease state. One subject died during the collection period, enabling a detailed look at changes in the 194 days prior to death. This subject had over 90% Pseudomonas in the microbiome at the beginning of sampling, and that level gradually increased to over 99% prior to death. This study revealed that the CF microbiome and metabolome of some subjects are dynamic through time. Future work is needed to understand what drives these temporal dynamics and if reduction of anaerobes correlate to clinical response to CFPE therapy.IMPORTANCE Subjects with cystic fibrosis battle polymicrobial lung infections throughout their lifetime. Although antibiotic therapy is a principal treatment for CF lung disease, we have little understanding of how antibiotics affect the CF lung microbiome and metabolome and how much the community changes on daily timescales. By analyzing 594 longitudinal CF sputum samples from six adult subjects, we show that the sputum microbiome and metabolome are dynamic. Significant changes occur during times of stability and also through pulmonary exacerbations (CFPEs). Microbiome alpha-diversity increased as a CFPE developed and then decreased during treatment in a manner corresponding to the reduction in the log ratio of anaerobic bacteria to classic pathogens. Levels of metabolites from the pathogen P. aeruginosa were also highly variable through time and were negatively associated with anaerobes. The microbial dynamics observed in this study may have a significant impact on the outcome of antibiotic therapy for CFPEs and overall subject health.

Lifestyle factors, such as diet, strongly influence the structure, diversity, and composition of the microbiome. While we have witnessed over the last several years a resurgence of interest in fermented foods, no study has specifically explored the effects of their consumption on gut microbiota in large cohorts. To assess whether the consumption of fermented foods is associated with a systematic signal in the gut microbiome and metabolome, we used a multi-omic approach (16S rRNA amplicon sequencing, metagenomic sequencing, and untargeted mass spectrometry) to analyze stool samples from 6,811 individuals from the American Gut Project, including 115 individuals specifically recruited for their frequency of fermented food consumption for a targeted 4-week longitudinal study. We observed subtle but statistically significant differences between consumers and nonconsumers in beta diversity as well as differential taxa between the two groups. We found that the metabolome of fermented food consumers was enriched with conjugated linoleic acid (CLA), a putatively health-promoting molecule. Cross-omic analyses between metagenomic sequencing and mass spectrometry suggest that CLA may be driven by taxa associated with fermented food consumers. Collectively, we found modest yet persistent signatures associated with fermented food consumption that appear present in multiple -omic types which motivate further investigation of how different types of fermented food impact the gut microbiome and overall health.IMPORTANCE Public interest in the effects of fermented food on the human gut microbiome is high, but limited studies have explored the association between fermented food consumption and the gut microbiome in large cohorts. Here, we used a combination of omics-based analyses to study the relationship between the microbiome and fermented food consumption in thousands of people using both cross-sectional and longitudinal data. We found that fermented food consumers have subtle differences in their gut microbiota structure, which is enriched in conjugated linoleic acid, thought to be beneficial. The results suggest that further studies of specific kinds of fermented food and their impacts on the microbiome and health will be useful.

Obstructive sleep apnea (OSA) is a common disorder characterized by episodic obstruction to breathing due to upper airway collapse during sleep. Because of the episodic airway obstruction, intermittently low O2 (hypoxia) and high CO2 (hypercapnia) ensue. OSA has been associated with adverse cardiovascular and metabolic outcomes, although data regarding potential causal pathways are still evolving. As changes in inspired O2 and CO2 can affect the ecology of the gut microbiota and the microbiota has been shown to contribute to various cardiometabolic disorders, we hypothesized that OSA alters the gut ecosystem, which, in turn, exacerbates the downstream physiological consequences. Here, we model human OSA and its cardiovascular consequence using Ldlr-/- mice fed a high-fat diet and exposed to intermittent hypoxia and hypercapnia (IHH). The gut microbiome and metabolome were characterized longitudinally (using 16S rRNA amplicon sequencing and untargeted liquid chromatography-tandem mass spectrometry [LC-MS/MS]) and seen to covary during IHH. Joint analysis of microbiome and metabolome data revealed marked compositional changes in both microbial (>10%, most remarkably in Clostridia) and molecular (>22%) species in the gut. Moreover, molecules that altered in abundance included microbe-dependent bile acids, enterolignans, and fatty acids, highlighting the impact of IHH on host-commensal organism cometabolism in the gut. Thus, we present the first evidence that IHH perturbs the gut microbiome functionally, setting the stage for understanding its involvement in cardiometabolic disorders. IMPORTANCE Intestinal dysbiosis mediates various cardiovascular diseases comorbid with OSA. To understand the role of dysbiosis in cardiovascular and metabolic disease caused by OSA, we systematically study the effect of intermittent hypoxic/hypercapnic stress (IHH, mimicking OSA) on gut microbes in an animal model. We take advantage of a longitudinal study design and paired omics to investigate the microbial and molecular dynamics in the gut to ascertain the contribution of microbes on intestinal metabolism in IHH. We observe microbe-dependent changes in the gut metabolome that will guide future research on unrecognized mechanistic links between gut microbes and comorbidities of OSA. Additionally, we highlight novel and noninvasive biomarkers for OSA-linked cardiovascular and metabolic disorders.

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality-including ordination integration and animations-alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of 'omic data.IMPORTANCE Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.

Ulcerative colitis (UC) is driven by disruptions in host-microbiota homoeostasis, but current treatments exclusively target host inflammatory pathways. To understand how host-microbiota interactions become disrupted in UC, we collected and analysed six faecal- or serum-based omic datasets (metaproteomic, metabolomic, metagenomic, metapeptidomic and amplicon sequencing profiles of faecal samples and proteomic profiles of serum samples) from 40 UC patients at a single inflammatory bowel disease centre, as well as various clinical, endoscopic and histologic measures of disease activity. A validation cohort of 210 samples (73 UC, 117 Crohn's disease, 20 healthy controls) was collected and analysed separately and independently. Data integration across both cohorts showed that a subset of the clinically active UC patients had an overabundance of proteases that originated from the bacterium Bacteroides vulgatus. To test whether B. vulgatus proteases contribute to UC disease activity, we first profiled B. vulgatus proteases found in patients and bacterial cultures. Use of a broad-spectrum protease inhibitor improved B. vulgatus-induced barrier dysfunction in vitro, and prevented colitis in B. vulgatus monocolonized, IL10-deficient mice. Furthermore, transplantation of faeces from UC patients with a high abundance of B. vulgatus proteases into germfree mice induced colitis dependent on protease activity. These results, stemming from a multi-omics approach, improve understanding of functional microbiota alterations that drive UC and provide a resource for identifying other pathways that could be inhibited as a strategy to treat this disease.

Microbiome analyses can be challenging because microbial strains are numerous, and often, confounding factors in the data set are also numerous. Many tools reduce, summarize, and visualize these high-dimensional data to provide insight at the community level. However, they lose the detailed information about each taxon and can be misleading (for example, the well-known horseshoe effect in ordination plots). Thus, multiple methods at different levels of resolution are required to capture the full range of microbial patterns. Here we present Calour, a user-friendly data exploration tool for microbiome analyses. Calour provides a study-centric data model to store and manipulate sample-by-feature tables (with features typically being operational taxonomic units) and their associated metadata. It generates an interactive heatmap, allowing visualization of microbial patterns and exploration using microbial knowledge databases. We demonstrate the use of Calour by exploring publicly available data sets, including the gut and skin microbiota of habitat-switched fire salamander larvae, gut microbiota of Trichuris muris-infected mice, skin microbiota of different human body sites, gut microbiota of various ant species, and a metabolome study of mice exposed to intermittent hypoxia and hypercapnia. In these cases, Calour reveals novel patterns and potential contaminants of subgroups of microbes that are otherwise hard to find. Calour is open source under the Berkeley Software Distribution (BSD) license and available from https://github.com/biocore/calour. IMPORTANCE Calour allows us to identify interesting microbial patterns and generate novel biological hypotheses by interactively inspecting microbiome studies and incorporating annotation databases and convenient statistical tools. Calour can be used as a first-step tool for microbiome data exploration.