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The human gut microbiome is sensitive to disruptions by common stressors such as alcohol consumption and antibiotic treatment. In this study, we used an in vitro system modeling the gut microbiome to investigate whether treatment with a microbial synbiotic can help restore microbiome function after stress.

Background Oral microbial therapy has been studied as an intervention for a range of gastrointestinal disorders. Though research suggests that microbial exposure may affect the gastrointestinal system, motility, and host immunity in a pediatric population, data have been inconsistent, with most prior studies being in neither a randomized nor placebo-controlled setting. The aim of this randomized, placebo-controlled study was to evaluate the efficacy of a synbiotic on increasing weekly bowel movements (WBMs) in constipated children. Methods Sixty-four children (3–17 years of age) were randomized to receive a synbiotic (n = 33) comprising mixed-chain length oligosaccharides and nine microbial strains, or placebo (n = 31) for 84 days. Stool microbiota was analyzed on samples collected at baseline and completion. The primary outcome was a change from baseline of WBMs in the treatment group compared to placebo. Results Treatment increased (p p = 0.00074). Conclusions These findings suggest the potential for (i) multi-species-synbiotic interventions to improve digestive health in a pediatric population and (ii) bioinformatics-based methods to predict response to microbial interventions in children. Impact Synbiotic microbial treatment improved the number of spontaneous weekly bowel movements in children compared to placebo. Intervention induced an increased abundance of bifidobacteria in children, compared to placebo. All administered probiotic species were enriched in the gut microbiome of the intervention group compared to placebo. Baseline microbial richness demonstrated potential as a predictive biomarker for response to intervention.

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset – the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85-89% to 90-94%, respectively. We then used these results to develop Cassandra, a random-forest-based classifier that identifies indicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).

Measuring microbial diversity is traditionally based on microbe taxonomy. Here, in contrast, we aimed to quantify heterogeneity in microbial gene content across 14,183 metagenomic samples spanning 17 ecologies including -- 6 human-associated, 7 non-human-host-associated, and 4 in other non-human host environments. In total, we identified 117,629,181 non-redundant genes. The vast majority of genes (66%) occurred in only one sample (i.e. “singletons”). By contrast, we found 1,864 sequences present in every metagenome, but not necessarily every bacterial genome. Additionally, we report datasets of other ecology-associated genes (e.g. abundant in only gut ecosystems) and simultaneously demonstrated that prior microbiome gene catalogs are both incomplete and inaccurately cluster microbial genetic life (e.g. at gene-sequence identifies that are too restrictive). We provide our results and the sets of environmentally-differentiating genes described above at http://www.microbial-genes.bio.ImportanceThe amount of shared genetic elements has not been quantified between the human microbiome and other host and non-host associated microbiomes. Here we made a gene catalog of 17 different microbial ecosystems and compared them. We show that most species shared between environment and human gut microbiomes are pathogens and that prior gene catalogs described as “near-complete” are far from it. Additionally, over two-thirds of all genes only appear in a single sample and only 1,864 genes (0.001%) are found in all types of metagenomes. These results highlight the large diversity between metagenomes and reveal a new, rare class of genes, those found in every type of metagenome, but not every microbial genome.

BackgroundOral microbial therapy has been studied as an intervention for a range of gastrointestinal and immunological disorders. Though emerging research suggests microbial exposure may intimately affect the gastrointestinal system, motility, and host immunity in a pediatric population, data has been inconsistent and variable, with the majority of prior studies conducted in neither a randomized nor placebo-controlled setting. The aim of this placebo-controlled study was to evaluate efficacy of a synbiotic (a prebiotic and rationally-defined microbial consortia) on increasing weekly bowel movement frequency in constipated children.MethodsSixty-four children (3-17 years of age) were randomized to receive a synbiotic composition (n=33) comprised of mixed-chain length, prebiotic oligosaccharides and nine microbial strains or placebo (n=31) for 84 days. Stool microbiota was analyzed using shotgun metagenomic sequencing on samples collected at baseline (T1) and completion (T2). The primary outcome was change from baseline of Weekly Bowel Movements (WBMs) in children compared to placebo.ResultsTreatment with a multi-strain synbiotic significantly (p < 0.05) increased the number of WBMs in children with low bowel movement frequency (< 4 WBMs and < 5 WBMs), irrespective of broadly distinctive microbiome signatures at baseline. Metagenomic shotgun sequencing revealed that low baseline microbial richness in the treatment group significantly anticipated improvements in constipation (p = 0.00074).ConclusionsThese findings suggest the potential for (i) multi-species synbiotic interventions to improve digestive health in a pediatric population and (ii) bioinformatics-based methods to predict response to microbial interventions in children.ImpactSynbiotic microbial treatment exerted functional improvements in the number of spontaneous Weekly Bowel Movements in children compared to placeboIntervention induced a significant bifidogenic effect in children compared to placeboAll administered probiotic species were enriched in the gut microbiome of the intervention group compared to placeboBaseline microbial richness demonstrated potential as a predictive biomarker for response to intervention

More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct epidemic dynamics, and provide important insights into drug and vaccine development and disease control. Here, we discuss the tremendous opportunities that genomics offers to unlock the effective use of SARS-CoV-2 genomic data for efficient public health surveillance and guiding timely responses to COVID-19.

Background Lake Hillier is a hypersaline lake known for its distinctive bright pink color. The cause of this phenomenon in other hypersaline sites has been attributed to halophiles, Dunaliella, and Salinibacter, however, a systematic analysis of the microbial communities, their functional features, and the prevalence of pigment-producing-metabolisms has not been previously studied.Our results are evidence that Lake Hillier is composed of a diverse set of microorganisms including archaea, bacteria, algae, and viruses. Our data indicate a core microbiome in Lake Hillier composed of multiple pigment-producer microbes, many of which are cataloged as polyextremophiles. Additionally, we estimated the diversity of metabolic pathways in the lake and determined that many of these are related to pigment production. We reconstructed complete or partial genomes for 21 discrete bacteria (N = 14) and archaea (N = 7), only 2 of which could be taxonomically annotated to previously observed species.Our findings provide the first metagenomic study to decipher the source of the pink color of Australia’s Lake Hillier. The study of this pink hypersaline environment is evidence of a microbial consortium of pigment producers, a repertoire of polyextremophiles, a core microbiome and potentially novel species.

Background: The gut microbiome promotes specific immune responses, and in turn the immune system has a hand in shaping the microbiome. Cancer and autoimmune diseases are two major disease families that result from the contrasting manifestations of immune dysfunction. We hypothesized that the opposing immunological profiles between cancer and autoimmunity yield analogously inverted gut microbiome signatures. To test this, we conducted a systematic review and meta-analysis on gut microbiome signatures and their directionality in cancers and autoimmune conditionsMethodology: We searched PubMed, Web of Science, and EMBAS to identify relevant articles to be included in this study. The study was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements and PRISMA 2009 checklist. Study estimates were pooled by a generic inverse variance random-effects meta-analysis model. The relative abundance of microbiome features was converted to log fold-change and the standard error was calculated from the p-values, sample size and fold-change. Results: We screened 3,874 potentially relevant publications. A total of 82 eligible studies comprising 37 autoimmune and 45 cancer studies with 4,208 healthy human controls and 5,957 disease cases from 27 countries were included in this study. We identified a set of microbiome features that show consistent, opposite directionality between cancers and autoimmune diseases in multiple studies. Fusobacterium and Peptostreptococcus were the most consistently increased genera among the cancer cases which were found to be associated in a remarkable 13 (+0.54 log fold-change in 5 studies) and 11 studies (+3.75 log fold-change in 5 studies), respectively. Conversely, Bacteroides was the most prominent genus, which was found to be increased in 12 autoimmune studies (+0.24 log fold-change in 6 studies) and decreased in six cancer studies (-0.32 log fold-change in 4 studies). Sulfur-metabolism pathways were found to be the most frequent pathways among the member of cancer-increased genus and species.Conclusions: The surprising reproducibility of these associations across studies and geographies suggests a shared underlying mechanism shaping the microbiome across cancers and autoimmune diseases.

The technological leap of DNA sequencing generated a tension between modern metagenomics and historical microbiology. We are forcibly harmonizing the output of a modern tool with centuries of experimental knowledge derived from culture-based microbiology. As a thought experiment, we borrow the notion of Cartesian doubt from philosopher Rene Descartes, who used doubt to build a philosophical framework from his incorrigible statement that “I think therefore I am.” We aim to cast away preconceived notions and conceptualize microorganisms through the lens of metagenomic sequencing alone. Specifically, we propose funding and building analysis and engineering methods that neither search for nor rely on the assumption of independent genomes bound by lipid barriers containing discrete functional roles and taxonomies. We propose that a view of microbial communities based in sequencing will engender novel insights into metagenomic structure and may capture functional biology not reflected within the current paradigm.

Evaluating the relationship between the human gut microbiome and disease requires computing reliable statistical associations. Here, using millions of different association modeling strategies, we evaluated the consistency—or robustness—of microbiome-based disease indicators for 6 prevalent and well-studied phenotypes (across 15 public cohorts and 2,343 individuals). We were able to discriminate between analytically robust versus nonrobust results. In many cases, different models yielded contradictory associations for the same taxon–disease pairing, some showing positive correlations and others negative. When querying a subset of 581 microbe–disease associations that have been previously reported in the literature, 1 out of 3 taxa demonstrated substantial inconsistency in association sign. Notably, >90% of published findings for type 1 diabetes (T1D) and type 2 diabetes (T2D) were particularly nonrobust in this regard. We additionally quantified how potential confounders—sequencing depth, glucose levels, cholesterol, and body mass index, for example—influenced associations, analyzing how these variables affect the ostensible correlation betweenFaecalibacterium prausnitziiabundance and a healthy gut. Overall, we propose our approach as a method to maximize confidence when prioritizing findings that emerge from microbiome association studies.

The human gut microbiome is linked to many states of human health and disease1. The metabolic repertoire of the gut microbiome is vast, but the health implications of these bacterial pathways are poorly understood. In this study, we identify a link between members of the genus Veillonella and exercise performance. We observed an increase in Veillonella relative abundance in marathon runners postmarathon and isolated a strain of Veillonella atypica from stool samples. Inoculation of this strain into mice significantly increased exhaustive treadmill run time. Veillonella utilize lactate as their sole carbon source, which prompted us to perform a shotgun metagenomic analysis in a cohort of elite athletes, finding that every gene in a major pathway metabolizing lactate to propionate is at higher relative abundance postexercise. Using 13C3-labeled lactate in mice, we demonstrate that serum lactate crosses the epithelial barrier into the lumen of the gut. We also show that intrarectal instillation of propionate is sufficient to reproduce the increased treadmill run time performance observed with V. atypica gavage. Taken together, these studies reveal that V. atypica improves run time via its metabolic conversion of exercise-induced lactate into propionate, thereby identifying a natural, microbiome-encoded enzymatic process that enhances athletic performance.

We analyzed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 individuals that live in the United States. We estimated the contribution of environmental risk factors (socioeconomic status, air pollution, and climate) in each phenotype. Mean heritability (h2 = 0.311) and shared environmental variance (c2 = 0.088) were higher than variance attributed to specific environmental factors such as zip code-level socioeconomic status (SES) (varSES = 0.002), daily air quality (varAQI = 0.0004), and average temperature (vartemp = 0.001) overall, as well as for individual phenotypes. We found significant heritability and shared environment for a number of comorbidities (h2 = 0.433, c2 = 0.241) and average monthly cost (h2 = 0.290, c2 = 0.302). All results are available using our “Claims Analysis of Twin Correlation and Heritability (CaTCH)” web application., Editorial summary: Analysis of a health insurance dataset comprising over 44 million individuals allows for the estimation of genetic and environmental contributions in 560 phenotypes using twins and sibling pairs.

We propose microbiome disease “architectures”: linking >1 million microbial features (species, pathways, and genes) to 7 host phenotypes from 13 cohorts using a pipeline designed to identify associations that are robust to analytical model choice. Here, we quantify conservation and heterogeneity in microbiome-disease associations, using gene-level analysis to identify strain-specific, cross-disease, positive and negative associations. We find coronary artery disease, inflammatory bowel diseases, and liver cirrhosis to share gene-level signatures ascribed to the Streptococcus genus. Type 2 diabetes, by comparison, has a distinct metagenomic signature not linked to any one specific species or genus. We additionally find that at the species-level, the prior-reported connection between Solobacterium moorei and colorectal cancer is not consistently identified across models—however, our gene-level analysis unveils a group of robust, strain-specific gene associations. Finally, we validate our findings regarding colorectal cancer and inflammatory bowel diseases in independent cohorts and identify that features inversely associated with disease tend to be less reproducible than features enriched in disease. Overall, our work is not only a step towards gene-based, cross-disease microbiome diagnostic indicators, but it also illuminates the nuances of the genetic architecture of the human microbiome, including tension between gene- and species-level associations., Here, combing the massive gene-universe of the gut microbiome to identify strain-specific, cross-disease, associations across seven human diseases, the authors introduce the concept of microbiome architecture, defined as the complete set of positive and negative associations between microbial genes and human host disease, highlighting microbiome architectures as potential diagnostic indicators.

Loss of gut microbial diversity1–6 in industrial populations is associated with chronic diseases7, underscoring the importance of studying our ancestral gut microbiome. However, relatively little is known about the composition of pre-industrial gut microbiomes. Here we performed a large-scale de novo assembly of microbial genomes from palaeofaeces. From eight authenticated human palaeofaeces samples (1,000–2,000 years old) with well-preserved DNA from southwestern USA and Mexico, we reconstructed 498 medium- and high-quality microbial genomes. Among the 181 genomes with the strongest evidence of being ancient and of human gut origin, 39% represent previously undescribed species-level genome bins. Tip dating suggests an approximate diversification timeline for the key human symbiont Methanobrevibacter smithii. In comparison to 789 present-day human gut microbiome samples from eight countries, the palaeofaeces samples are more similar to non-industrialized than industrialized human gut microbiomes. Functional profiling of the palaeofaeces samples reveals a markedly lower abundance of antibiotic-resistance and mucin-degrading genes, as well as enrichment of mobile genetic elements relative to industrial gut microbiomes. This study facilitates the discovery and characterization of previously undescribed gut microorganisms from ancient microbiomes and the investigation of the evolutionary history of the human gut microbiota through genome reconstruction from palaeofaeces., Ancient microbiomes from palaeofaeces are more similar to non-industrialized than industrialized human gut microbiomes regardless of geography, but 39% of their de novo reconstructed genomes represent previously undescribed microbial species.

The microbiome is a new frontier for building predictors of human phenotypes. However, machine learning in the microbiome is fraught with issues of reproducibility, driven in large part by the wide range of analytic models and metagenomic data types available. We aimed to build robust metagenomic predictors of host phenotype by comparing prediction performances and biological interpretation across 8 machine learning methods and 4 different types of metagenomic data. Using 1,570 samples from 300 infants, we fit 7,865 models for 6 host phenotypes. We demonstrate the dependence of accuracy on algorithm choice and feature definition in microbiome data and propose a framework for building microbiome-derived indicators of host phenotype. We additionally identify biological features predictive of age, sex, breastfeeding status, historical antibiotic usage, country of origin, and delivery type. Our complete results can be viewed at http://apps.chiragjpgroup.org/ubiome_predictions/., Author summary The human microbiome is hypothesized to influence human phenotype. However, many published host-microbe associations may not be reproducible. A number of reasons could be behind irreproducible results, including a wide array of methods for measuring the microbiome through genetic sequence, annotation pipelines, and analytical models/prediction approaches. Therefore, there is a need to compare different modeling strategies and microbiome data types (i.e. species abundance versus metabolic pathway abundance) to determine how to build robust and reproducible host-microbiome predictions. In this work, we executed a broad comparison of different predictive methods as a function of microbiome data types to effectively predict host characteristics. Our pipeline was able uncover robust microbial associations with phenotype. We additionally recommended considerations for reproducible microbiome-host association pipeline development. We claim our work is a necessary stepping stone in increasing the utility of emerging cohort data and enabling the next generation of efficient microbiome association studies in human health.

Over the past decade, studies of the human genome and microbiome have deepened our understanding of the connections between human genes, environments, microbes, and disease. For example, the sheer number of indicators of the microbiome and human genetic common variants associated with disease has been immense, but clinical utility has been elusive. Here, we compared the predictive capabilities of the human microbiome versus human genomic common variants across 13 common diseases. We concluded that microbiomic indicators outperform human genetics in predicting host phenotype (overall Microbiome-Association-Study [MAS] area under the curve [AUC] = 0.79 [SE = 0.03], overall Genome-Wide-Association-Study [GWAS] AUC = 0.67 [SE = 0.02]). Our results, while preliminary and focused on a subset of the totality of disease, demonstrate the relative predictive ability of the microbiome, indicating that it may outperform human genetics in discriminating human disease cases and controls. They additionally motivate the need for population-level microbiome sequencing resources, akin to the UK Biobank, to further improve and reproduce metagenomic models of disease.

Over the past 350 years, Merck has developed science and technology especially in health care, life sciences, and performance materials. To celebrate so many productive years, Merck conducted a special expanded anniversary edition of the Innovation Cup in combination with the scientific conference Curious2018 – Future Insight in Darmstadt, Germany.

Motivation Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Results Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users’ existing HPC pipelines. Availability and implementation Data utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation and a tutorial are available at http://aether.kosticlab.org. Supplementary information Supplementary data are available at Bioinformatics online.

The V. cholerae phosphoenolpyruvate phosphotransferase system (PTS) is a well-conserved, multicomponent phosphotransfer cascade that regulates cellular physiology and virulence in response to nutritional signals. Glucose-specific enzyme IIA (EIIAGlc), a component of the PTS, is a master regulator that coordinates bacterial metabolism, nutrient uptake, and behavior by direct interactions with protein partners. We show that an amphipathic helix (AH) at the N terminus of V. cholerae EIIAGlc serves as a membrane association domain that is dispensable for interactions with cytoplasmic partners but essential for regulation of integral membrane protein partners. By removing this amphipathic helix, hidden, opposing roles for cytoplasmic partners of EIIAGlc in both biofilm formation and metabolism within the mammalian intestine are revealed. This study defines a novel paradigm for AH function in integrating opposing regulatory functions in the cytoplasm and at the bacterial cell membrane and highlights the PTS as a target for metabolic modulation of the intestinal environment., The Vibrio cholerae phosphoenolpyruvate phosphotransferase system (PTS) is a well-conserved, multicomponent phosphotransfer cascade that coordinates the bacterial response to carbohydrate availability through direct interactions of its components with protein targets. One such component, glucose-specific enzyme IIA (EIIAGlc), is a master regulator that coordinates bacterial metabolism, nutrient uptake, and behavior by direct interactions with cytoplasmic and membrane-associated protein partners. Here, we show that an amphipathic helix (AH) at the N terminus of V. cholerae EIIAGlc serves as a membrane association domain that is dispensable for interactions with cytoplasmic partners but essential for regulation of integral membrane protein partners. By deleting this AH, we reveal previously unappreciated opposing regulatory functions for EIIAGlc at the membrane and in the cytoplasm and show that these opposing functions are active in the laboratory biofilm and the mammalian intestine. Phosphotransfer through the PTS proceeds in the absence of the EIIAGlc AH, while PTS-dependent sugar transport is blocked. This demonstrates that the AH couples phosphotransfer to sugar transport and refutes the paradigm of EIIAGlc as a simple phosphotransfer component in PTS-dependent transport. Our findings show that Vibrio cholerae EIIAGlc, a central regulator of pathogen metabolism, contributes to optimization of bacterial physiology by integrating metabolic cues arising from the cytoplasm with nutritional cues arising from the environment. Because pathogen carbon metabolism alters the intestinal environment, we propose that it may be manipulated to minimize the metabolic cost of intestinal infection.

In the version of this article initially published, in Fig. 4b, the shared environmental variance (c2) values for all MaTCH functional domains except ‘all traits’ were erroneously estimated because of a coding error. Figure 4 has been revised to include corrected c2 estimates in the data in panel b as well as the number of phenotypes in CaTCH and MaTCH functional domains in the y axes of panels a and b; the Fig. 4 legend and the description of Fig. 4b in the Results section have also been revised to describe these changes. In addition, the erroneous term ‘depravity index’, appearing throughout the article’s main text, Fig. 1, Supplementary Fig. 10 and the Supplementary Note, should have read ‘deprivation index’. The errors have been corrected in the HTML and PDF versions of the article. Images of the original figure are shown in the correction notice.

Summary Despite substantial interest in the species diversity of the human microbiome and its role in disease, the scale of its genetic diversity, which is fundamental to deciphering human-microbe interactions, has not been quantified. Here, we conducted a cross-study meta-analysis of metagenomes from two human body niches, the mouth and gut, covering 3,655 samples from 13 studies. We found staggering genetic heterogeneity in the dataset, identifying a total of 45,666,334 non-redundant genes (23,961,508 oral and 22,254,436 gut) at the 95% identity level. Fifty percent of all genes were “singletons,” or unique to a single metagenomic sample. Singletons were enriched for different functions (compared with non-singletons) and arose from sub-population-specific microbial strains. Overall, these results provide potential bases for the unexplained heterogeneity observed in microbiome-derived human phenotypes. One the basis of these data, we built a resource, which can be accessed at https://microbial-genes.bio .