redbiom: a Rapid Sample Discovery and Feature Characterization System

Greengenes2 unifies microbial data in a single reference tree

Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.

Improving microbial phylogeny with citizen science within a mass-market video game

Citizen science video games are designed primarily for users already inclined to contribute to science, which severely limits their accessibility for an estimated community of 3 billion gamers worldwide. We created Borderlands Science (BLS), a citizen science activity that is seamlessly integrated within a popular commercial video game played by tens of millions of gamers. This integration is facilitated by a novel game-first design of citizen science games, in which the game design aspect has the highest priority, and a suitable task is then mapped to the game design. BLS crowdsources a multiple alignment task of 1 million 16S ribosomal RNA sequences obtained from human microbiome studies. Since its initial release on 7 April 2020, over 4 million players have solved more than 135 million science puzzles, a task unsolvable by a single individual. Leveraging these results, we show that our multiple sequence alignment simultaneously improves microbial phylogeny estimations and UniFrac effect sizes compared to state-of-the-art computational methods. This achievement demonstrates that hyper-gamified scientific tasks attract massive crowds of contributors and offers invaluable resources to the scientific community.

Medication Use is Associated with Distinct Microbial Features in Anxiety and Depression

This study investigated the relationship between gut microbiota and neuropsychiatric disorders (NPDs), specifically anxiety disorder (ANXD) and/or major depressive disorder (MDD), as defined by DSM-IV or V criteria. The study also examined the influence of medication use, particularly antidepressants and/or anxiolytics, classified through the Anatomical Therapeutic Chemical (ATC) Classification System, on the gut microbiota. Both 16S rRNA gene amplicon sequencing and shallow shotgun sequencing were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and NeuroMAP CoBRE cohorts. The results highlight the significant influence of medication use; antidepressant use is associated with significant differences in gut microbiota beta diversity and has a larger effect size than NPD diagnosis. Next, specific microbes were associated with ANXD and MDD, highlighting their potential for non-pharmacological intervention. Finally, the study demonstrated the capability of Random Forest classifiers to predict diagnoses of NPD and medication use from microbial profiles, suggesting a promising direction for the use of gut microbiota as biomarkers for NPD. The findings suggest that future research on the gut microbiota's role in NPD and its interactions with pharmacological treatments are needed.

A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables

Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to identify links between taxonomy and function. This universal network of microbial decomposers is characterized by cross-feeding to metabolize labile decomposition products. The key bacterial and fungal decomposers are rare across non-decomposition environments and appear unique to the breakdown of terrestrial decaying flesh, including humans, swine, mice and cattle, with insects as likely important vectors for dispersal. The observed lockstep of microbial interactions further underlies a robust microbial forensic tool with the potential to aid predictions of the time since death.

Achieving pan-microbiome biological insights via the dbBact knowledge base

16S rRNA amplicon sequencing provides a relatively inexpensive culture-independent method for studying microbial communities. Although thousands of such studies have examined diverse habitats, it is difficult for researchers to use this vast trove of experiments when interpreting their own findings in a broader context. To bridge this gap, we introduce dbBact - a novel pan-microbiome resource. dbBact combines manually curated information from studies across diverse habitats, creating a collaborative central repository of 16S rRNA amplicon sequence variants (ASVs), which are assigned multiple ontology-based terms. To date dbBact contains information from more than 1000 studies, which include 1500000 associations between 360000 ASVs and 6500 ontology terms. Importantly, dbBact offers a set of computational tools allowing users to easily query their own datasets against the database. To demonstrate how dbBact augments standard microbiome analysis we selected 16 published papers, and reanalyzed their data via dbBact. We uncovered novel inter-host similarities, potential intra-host sources of bacteria, commonalities across different diseases and lower host-specificity in disease-associated bacteria. We also demonstrate the ability to detect environmental sources, reagent-borne contaminants, and identify potential cross-sample contaminations. These analyses demonstrate how combining information across multiple studies and over diverse habitats leads to better understanding of underlying biological processes.

Global branches and local states of the human gut microbiome define associations with environmental and intrinsic factors

The gut microbiome is important for human health, yet modulation requires more insight into inter-individual variation. Here, we explored latent structures of the human gut microbiome across the human lifespan, applying partitioning, pseudotime, and ordination approaches to >35,000 samples. Specifically, three major gut microbiome branches were identified, within which multiple partitions were observed in adulthood, with differential abundances of species along branches. Different compositions and metabolic functions characterized the branches' tips, reflecting ecological differences. An unsupervised network analysis from longitudinal data from 745 individuals showed that partitions exhibited connected gut microbiome states rather than over-partitioning. Stability in the Bacteroides-enriched branch was associated with specific ratios of Faecalibacterium:Bacteroides. We also showed that associations with factors (intrinsic and extrinsic) could be generic, branch- or partition-specific. Our ecological framework for cross-sectional and longitudinal data allows a better understanding of overall variation in the human gut microbiome and disentangles factors associated with specific configurations.

Determination of Effect Sizes for Power Analysis for Microbiome Studies Using Large Microbiome Databases

Herein, we present a tool called Evident that can be used for deriving effect sizes for a broad spectrum of metadata variables, such as mode of birth, antibiotics, socioeconomics, etc., to provide power calculations for a new study. Evident can be used to mine existing databases of large microbiome studies (such as the American Gut Project, FINRISK, and TEDDY) to analyze the effect sizes for planning future microbiome studies via power analysis. For each metavariable, the Evident software is flexible to compute effect sizes for many commonly used measures of microbiome analyses, including α diversity, β diversity, and log-ratio analysis. In this work, we describe why effect size and power analysis are necessary for computational microbiome analysis and show how Evident can help researchers perform these procedures. Additionally, we describe how Evident is easy for researchers to use and provide an example of efficient analyses using a dataset of thousands of samples and dozens of metadata categories.

Age modifies the association between pet ownership and cardiovascular disease

Introduction

Studies examining associations between pet ownership and cardiovascular disease have yielded inconsistent results. These discrepancies may be partially explained by variations in age and sex across study populations. Our study included 6,632 American Gut Project participants who are US residents ≥40 years.

Methods

We first estimated the association of pet ownership with cardiovascular disease risk using multivariable-adjusted logistic regression, and further investigated effect modifications of age and sex.

Results

Cat but not dog ownership was significantly associated with lower cardiovascular disease risk (OR: 0.56 [0.42, 0.73] and OR: 1.17 [0.88, 1.39], respectively). Cat and dog ownership significantly interacted with age but not sex, indicating that cardiovascular risk varies by the age-by-pet ownership combination. Compared to the reference group (40-64 years, no cat or dog), participants 40-64 years with only a cat had the lowest cardiovascular disease risk (OR: 0.40 [0.26, 0.61]). Those ≥65 years with no pets had the highest risk (OR: 3.85 [2.85, 5.24]).

Discussion

This study supports the importance of pets in human cardiovascular health, suggesting optimal pet choice is age-dependent. Having both a cat and dog can be advantageous to people ≥65 years, while having only a cat may benefit those 40-64 years. Further studies are needed to assess causality.

Analysis of correlations between gut microbiota, stool short chain fatty acids, calprotectin and cardiometabolic risk factors in postmenopausal women with obesity: a cross-sectional study

BACKGROUND

Microbiota and its metabolites are known to regulate host metabolism. In cross-sectional study conducted in postmenopausal women we aimed to assess whether the microbiota, its metabolites and gut barrier integrity marker are correlated with cardiometabolic risk factors and if microbiota is different between obese and non-obese subjects.

METHODS

We analysed the faecal microbiota of 56 obese, postmenopausal women by means of 16S rRNA analysis. Stool short chain fatty acids, calprotectin and anthropometric, physiological and biochemical parameters were correlates to microbiome analyses.

RESULTS

Alpha-diversity was inversely correlated with lipopolysaccharide (Rho = - 0.43, FDR P (Q) = 0.004). Bray-Curtis distance based RDA revealed that visceral fat and waist circumference had a significant impact on metabolic potential (P = 0.003). Plasma glucose was positively correlated with the Coriobacteriaceae (Rho = 0.48, Q = 0.004) and its higher taxonomic ranks, up to phylum (Actinobacteria, Rho = 0.46, Q = 0.004). At the metabolic level, the strongest correlation was observed for the visceral fat (Q < 0.15), especially with the DENOVOPURINE2-PWY, PWY-841 and PWY0-162 pathways. Bacterial abundance was correlated with SCFAs, thus some microbiota-glucose relationships may be mediated by propionate, as indicated by the significant average causal mediation effect (ACME): Lachnospiraceae (ACME 1.25, 95%CI (0.10, 2.97), Firmicutes (ACME 1.28, 95%CI (0.23, 3.83)) and Tenericutes (ACME - 0.39, 95%CI (- 0.87, - 0.03)). There were significant differences in the distribution of phyla between this study and Qiita database (P < 0.0001).

CONCLUSIONS

Microbiota composition and metabolic potential are associated with some CMRF and fecal SCFAs concentration in obese postmenopausal women. There is no unequivocal relationship between fecal SCFAs and the marker of intestinal barrier integrity and CMRF. Further studies with appropriately matched control groups are warranted to look for causality between SCFAs and CMRF.

Enhancing untargeted metabolomics using metadata-based source annotation

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

Faecalibacterium prausnitzii Attenuates CKD via Butyrate-Renal GPR43 Axis

BACKGROUND

Despite available clinical management strategies, chronic kidney disease (CKD) is associated with severe morbidity and mortality worldwide, which beckons new solutions. Host-microbial interactions with a depletion of Faecalibacterium prausnitzii in CKD are reported. However, the mechanisms about if and how F prausnitzii can be used as a probiotic to treat CKD remains unknown.

METHODS

We evaluated the microbial compositions in 2 independent CKD populations for any potential probiotic. Next, we investigated if supplementation of such probiotic in a mouse CKD model can restore gut-renal homeostasis as monitored by its effects on suppression on renal inflammation, improvement in gut permeability and renal function. Last, we investigated the molecular mechanisms underlying the probiotic-induced beneficial outcomes.

RESULTS

We observed significant depletion of Faecalibacterium in the patients with CKD in both Western (n=283) and Eastern populations (n=75). Supplementation of F prausnitzii to CKD mice reduced renal dysfunction, renal inflammation, and lowered the serum levels of various uremic toxins. These are coupled with improved gut microbial ecology and intestinal integrity. Moreover, we demonstrated that the beneficial effects in kidney induced by F prausnitzii-derived butyrate were through the GPR (G protein-coupled receptor)-43.

CONCLUSIONS

Using a mouse CKD model, we uncovered a novel beneficial role of F prausnitzii in the restoration of renal function in CKD, which is, at least in part, attributed to the butyrate-mediated GPR-43 signaling in the kidney. Our study provides the necessary foundation to harness the therapeutic potential of F prausnitzii for ameliorating CKD.

Optimizing UniFrac with OpenACC Yields Greater Than One Thousand Times Speed Increase

UniFrac is an important tool in microbiome research that is used for phylogenetically comparing microbiome profiles to one another (beta diversity). Striped UniFrac recently added the ability to split the problem into many independent subproblems, exhibiting nearly linear scaling but suffering from memory contention. Here, we adapt UniFrac to graphics processing units using OpenACC, enabling greater than 1,000× computational improvement, and apply it to 307,237 samples, the largest 16S rRNA V4 uniformly preprocessed microbiome data set analyzed to date. IMPORTANCE UniFrac is an important tool in microbiome research that is used for phylogenetically comparing microbiome profiles to one another. Here, we adapt UniFrac to operate on graphics processing units, enabling a 1,000× computational improvement. To highlight this advance, we perform what may be the largest microbiome analysis to date, applying UniFrac to 307,237 16S rRNA V4 microbiome samples preprocessed with Deblur. These scaling improvements turn UniFrac into a real-time tool for common data sets and unlock new research questions as more microbiome data are collected.

The molecular impact of life in an indoor environment

The chemistry of indoor surfaces and the role of microbes in shaping and responding to that chemistry are largely unexplored. We found that, over 1 month, people's presence and activities profoundly reshaped the chemistry of a house. Molecules associated with eating/cooking, bathroom use, and personal care were found throughout the entire house, while molecules associated with medications, outdoor biocides, and microbially derived compounds were distributed in a location-dependent manner. The house and its microbial occupants, in turn, also introduced chemical transformations such as oxidation and transformations of foodborne molecules. The awareness of and the ability to observe the molecular changes introduced by people should influence future building designs.

A decade of GigaScience: 10 years of the evolving genomic and biomedical standards landscape

Standardization of omics data drives FAIR data practices through community-built genomic data standards and biomedical ontologies. Use of standards has progressed from a foreign concept to a sought-after solution, moving from efforts to coordinate data within individual research projects to research communities coming together to identify solutions to common challenges. Today we are seeing the benefits of this multidecade groundswell to coordinate, exchange, and reuse data; to compare data across studies; and to integrate data across previously siloed resources.

Turning the tide on sex and the microbiota in aquatic animals

Sex-based differences in animal microbiota are increasingly recognized as of biological importance. While most animal biomass is found in aquatic ecosystems and many water-dwelling species are of high economic and ecological value, biological sex is rarely included as an explanatory variable in studies of the aquatic animal microbiota. In this opinion piece, we argue for greater consideration of host sex in studying the microbiota of aquatic animals, emphasizing the many advancements that this information could provide in the life sciences, from the evolution of sex to aquaculture.

Clean room microbiome complexity impacts planetary protection bioburden

BACKGROUND

The Spacecraft Assembly Facility (SAF) at the NASA's Jet Propulsion Laboratory is the primary cleanroom facility used in the construction of some of the planetary protection (PP)-sensitive missions developed by NASA, including the Mars 2020 Perseverance Rover that launched in July 2020. SAF floor samples (n=98) were collected, over a 6-month period in 2016 prior to the construction of the Mars rover subsystems, to better understand the temporal and spatial distribution of bacterial populations (total, viable, cultivable, and spore) in this unique cleanroom.

RESULTS

Cleanroom samples were examined for total (living and dead) and viable (living only) microbial populations using molecular approaches and cultured isolates employing the traditional NASA standard spore assay (NSA), which predominantly isolated spores. The 130 NSA isolates were represented by 16 bacterial genera, of which 97% were identified as spore-formers via Sanger sequencing. The most spatially abundant isolate was Bacillus subtilis, and the most temporally abundant spore-former was Virgibacillus panthothenticus. The 16S rRNA gene-targeted amplicon sequencing detected 51 additional genera not found in the NSA method. The amplicon sequencing of the samples treated with propidium monoazide (PMA), which would differentiate between viable and dead organisms, revealed a total of 54 genera: 46 viable non-spore forming genera and 8 viable spore forming genera in these samples. The microbial diversity generated by the amplicon sequencing corresponded to ~86% non-spore-formers and ~14% spore-formers. The most common spatially distributed genera were Sphinigobium, Geobacillus, and Bacillus whereas temporally distributed common genera were Acinetobacter, Geobacilllus, and Bacillus. Single-cell genomics detected 6 genera in the sample analyzed, with the most prominent being Acinetobacter.

CONCLUSION

This study clearly established that detecting spores via NSA does not provide a complete assessment for the cleanliness of spacecraft-associated environments since it failed to detect several PP-relevant genera that were only recovered via molecular methods. This highlights the importance of a methodological paradigm shift to appropriately monitor bioburden in cleanrooms for not only the aeronautical industry but also for pharmaceutical, medical industries, etc., and the need to employ molecular sequencing to complement traditional culture-based assays. Video abstract.

Microbiome Analysis of Mucosal Ileoanal Pouch in Ulcerative Colitis Patients Revealed Impairment of the Pouches Immunometabolites

The pathogenesis of ulcerative colitis (UC) is unknown, although genetic loci and altered gut microbiota have been implicated. Up to a third of patients with moderate to severe UC require proctocolectomy with ileal pouch ano-anastomosis (IPAA). We aimed to explore the mucosal microbiota of UC patients who underwent IPAA.

METHODS

For microbiome analysis, mucosal specimens were collected from 34 IPAA individuals. Endoscopic and histological examinations of IPAA were normal in 21 cases, while pouchitis was in 13 patients. 19 specimens from the healthy control (10 from colonic and 9 from ileum) were also analyzed. Data were analyzed using an ensemble of software packages: QIIME2, coda-lasso, clr-lasso, PICRUSt2, and ALDEx2.

RESULTS

IPAA specimens had significantly lower bacterial diversity as compared to normal. The microbial composition of the normal pouch was also decreased also when compared to pouchitis. Faecalibacterium prausnitzii, Gemmiger formicilis, Blautia obeum, Ruminococcus torques, Dorea formicigenerans, and an unknown species from Roseburia were the most uncommon in pouch/pouchitis, while an unknown species from Enterobacteriaceae was over-represented. Propionibacterium acnes and Enterobacteriaceae were the species most abundant in the pouchitis and in the normal pouch, respectively. Predicted metabolic pathways among the IPAA bacterial communities revealed an important role of immunometabolites such as SCFA, butyrate, and amino acids.

CONCLUSIONS

Our findings showed specific bacterial signature hallmarks of dysbiosis and could represent bacterial biomarkers in IPAA patients useful to develop novel treatments in the future by modulating the gut microbiota through the administration of probiotic immunometabolites-producing bacterial strains and the addition of specific prebiotics and the faecal microbiota transplantation.

Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes

The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

Drinking Water Source and Intake Are Associated with Distinct Gut Microbiota Signatures in US and UK Populations

BACKGROUND

The microbiome of the digestive tract exerts fundamental roles in host physiology. Extrinsic factors including lifestyle and diet are widely recognized as key drivers of gut and oral microbiome compositions. Although drinking water is among the food items consumed in the largest amount, little is known about its potential impact on the microbiome.

OBJECTIVES

We explored the associations of plain drinking water source and intake with gut and oral microbiota compositions in a population-based cohort.

METHODS

Microbiota, health, lifestyle, and food intake data were extracted from the American Gut Project public database. Associations of drinking water source (bottled, tap, filtered, or well water) and intake with global microbiota composition were evaluated using linear and logistic models adjusted for anthropometric, diet, and lifestyle factors in 3413 and 3794 individuals, respectively (fecal samples; 56% female, median [IQR] age: 48 [36-59] y; median [IQR] BMI: 23.3 [20.9-26.3] kg/m2), and in 283 and 309 individuals, respectively (oral samples).

RESULTS

Drinking water source ranked among the key contributing factors explaining the gut microbiota variation, accounting for 13% [Faith's phylogenetic diversity (Faith's PD)] and 47% (Bray-Curtis dissimilarity) of the age effect size. Drinking water source was associated with differences in gut microbiota signatures, as revealed by β diversity analyses (P < 0.05; Bray-Curtis dissimilarity, weighted UniFrac distance). Subjects drinking mostly well water had higher fecal α diversity (P < 0.05; Faith's PD, observed amplicon sequence variants), higher Dorea, and lower Bacteroides, Odoribacter, and Streptococcus than the other groups. Low water drinkers also exhibited gut microbiota differences compared with high water drinkers (P < 0.05; Bray-Curtis dissimilarity, unweighted UniFrac distance) and a higher abundance of Campylobacter. No associations were found between oral microbiota composition and drinking water consumption.

CONCLUSIONS

Our results indicate that drinking water may be an important factor in shaping the human gut microbiome and that integrating drinking water source and intake as covariates in future microbiome analyses is warranted.

QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data

QIIME 2 is a completely re‐engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open‐source web‐based platform, to re‐use available data for meta‐analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug‐ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta‐analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://qiime2.org. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://forum.qiime2.org. © 2020 The Authors.

Basic Protocol: Using QIIME 2 with microbiome data

Support Protocol: Further microbiome analyses

A globally ubiquitous symbiont can drive experimental host evolution

Organisms harbour myriad microbes which can be parasitic or protective against harm. The costs and benefits resulting from these symbiotic relationships can be context-dependent, but the evolutionary consequences to hosts of these transitions remain unclear. Here, we mapped the Leucobacter genus across 13,715 microbiome samples (163 studies) to reveal a global distribution as a free-living microbe or a symbiont of animals and plants. We showed that across geographically distant locations (South Africa, France, Cape Verde), Leucobacter isolates vary substantially in their virulence to an associated animal host, Caenorhabditis nematodes. We further found that multiple Leucobacter sequence variants co-occur in wild Caenorhabditis spp. which combined with natural variation in virulence provides real-world potential for Leucobacter community composition to influence host fitness. We examined this by competing C. elegans genotypes that differed in susceptibility to different Leucobacter species in an evolution experiment. One Leucobacter species was found to be host-protective against another, virulent parasitic species. We tested the impact of host genetic background and Leucobacter community composition on patterns of host-based defence evolution. We found host genotypes conferring defence against the parasitic species were maintained during infection. However, when hosts were protected during coinfection, host-based defences were nearly lost from the population. Overall, our results provide insight into the role of community context in shaping host evolution during symbioses.

Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases

Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is a clinical need to further improve the diagnosis of IBD. As gut dysbiosis is reported in patients with IBD, we hypothesized that supervised machine learning (ML) could be used to analyze gut microbiome data for predictive diagnostics of IBD. To test our hypothesis, fecal 16S metagenomic data of 729 subjects with IBD and 700 subjects without IBD from the American Gut Project were analyzed using five different ML algorithms. Fifty differential bacterial taxa were identified [linear discriminant analysis effect size (LEfSe): linear discriminant analysis (LDA) score > 3] between the IBD and non-IBD groups, and ML classifications trained with these taxonomic features using random forest (RF) achieved a testing area under the receiver operating characteristic curves (AUC) of ∼0.80. Next, we tested if operational taxonomic units (OTUs), instead of bacterial taxa, could be used as ML features for diagnostic classification of IBD. Top 500 high-variance OTUs were used for ML training, and an improved testing AUC of ∼0.82 (RF) was achieved. Lastly, we tested if supervised ML could be used for differentiating Crohn's disease (CD) and ulcerative colitis (UC). Using 331 CD and 141 UC samples, 117 differential bacterial taxa (LEfSe: LDA score > 3) were identified, and the RF model trained with differential taxonomic features or high-variance OTU features achieved a testing AUC > 0.90. In summary, our study demonstrates the promising potential of artificial intelligence via supervised ML modeling for predictive diagnostics of IBD using gut microbiome data.NEW & NOTEWORTHY Our study demonstrates the promising potential of artificial intelligence via supervised machine learning modeling for predictive diagnostics of different types of inflammatory bowel diseases using fecal gut microbiome data.

High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing

High-throughput amplicon sequencing of large genomic regions remains challenging for short-read technologies. Here, we report a high-throughput amplicon sequencing approach combining unique molecular identifiers (UMIs) with Oxford Nanopore Technologies (ONT) or Pacific Biosciences circular consensus sequencing, yielding high-accuracy single-molecule consensus sequences of large genomic regions. We applied our approach to sequence ribosomal RNA operon amplicons (~4,500 bp) and genomic sequences (>10,000 bp) of reference microbial communities in which we observed a chimera rate <0.02%. To reach a mean UMI consensus error rate <0.01%, a UMI read coverage of 15× (ONT R10.3), 25× (ONT R9.4.1) and 3× (Pacific Biosciences circular consensus sequencing) is needed, which provides a mean error rate of 0.0042%, 0.0041% and 0.0007%, respectively.

Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease

Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not clearly established, the reported associations between alterations in gut microbiota and CVD are prominent. Therefore, we hypothesized that machine learning (ML) could be used for gut microbiome-based diagnostic screening of CVD. To test our hypothesis, fecal 16S ribosomal RNA sequencing data of 478 CVD and 473 non-CVD human subjects collected through the American Gut Project were analyzed using 5 supervised ML algorithms including random forest, support vector machine, decision tree, elastic net, and neural networks. Thirty-nine differential bacterial taxa were identified between the CVD and non-CVD groups. ML modeling using these taxonomic features achieved a testing area under the receiver operating characteristic curve (0.0, perfect antidiscrimination; 0.5, random guessing; 1.0, perfect discrimination) of ≈0.58 (random forest and neural networks). Next, the ML models were trained with the top 500 high-variance features of operational taxonomic units, instead of bacterial taxa, and an improved testing area under the receiver operating characteristic curves of ≈0.65 (random forest) was achieved. Further, by limiting the selection to only the top 25 highly contributing operational taxonomic unit features, the area under the receiver operating characteristic curves was further significantly enhanced to ≈0.70. Overall, our study is the first to identify dysbiosis of gut microbiota in CVD patients as a group and apply this knowledge to develop a gut microbiome-based ML approach for diagnostic screening of CVD.

Depression in Individuals Coinfected with HIV and HCV Is Associated with Systematic Differences in the Gut Microbiome and Metabolome

Depression is influenced by the structure, diversity, and composition of the gut microbiome. Although depression has been described previously in human immunodeficiency virus (HIV) and hepatitis C virus (HCV) monoinfections, and to a lesser extent in HIV-HCV coinfection, research on the interplay between depression and the gut microbiome in these disease states is limited. Here, we characterized the gut microbiome using 16S rRNA amplicon sequencing of fecal samples from 373 participants who underwent a comprehensive neuropsychiatric assessment and the gut metabolome on a subset of these participants using untargeted metabolomics with liquid chromatography-mass spectrometry. We observed that the gut microbiome and metabolome were distinct between HIV-positive and -negative individuals. HCV infection had a large association with the microbiome that was not confounded by drug use. Therefore, we classified the participants by HIV and HCV infection status (HIV-monoinfected, HIV-HCV coinfected, or uninfected). The three groups significantly differed in their gut microbiome (unweighted UniFrac distances) and metabolome (Bray-Curtis distances). Coinfected individuals also had lower alpha diversity. Within each of the three groups, we evaluated lifetime major depressive disorder (MDD) and current Beck Depression Inventory-II. We found that the gut microbiome differed between depression states only in coinfected individuals. Coinfected individuals with a lifetime history of MDD were enriched in primary and secondary bile acids, as well as taxa previously identified in people with MDD. Collectively, we observe persistent signatures associated with depression only in coinfected individuals, suggesting that HCV itself, or interactions between HCV and HIV, may drive HIV-related neuropsychiatric differences.IMPORTANCE The human gut microbiome influences depression. Differences between the microbiomes of HIV-infected and uninfected individuals have been described, but it is not known whether these are due to HIV itself, or to common HIV comorbidities such as HCV coinfection. Limited research has explored the influence of the microbiome on depression within these groups. Here, we characterized the microbial community and metabolome in the stools from 373 people, noting the presence of current or lifetime depression as well as their HIV and HCV infection status. Our findings provide additional evidence that individuals with HIV have different microbiomes which are further altered by HCV coinfection. In individuals coinfected with both HIV and HCV, we identified microbes and molecules that were associated with depression. These results suggest that the interplay of HIV and HCV and the gut microbiome may contribute to the HIV-associated neuropsychiatric problems.

Method development for cross-study microbiome data mining: Challenges and opportunities

During the past decade, tremendous amount of microbiome sequencing data has been generated to study on the dynamic associations between microbial profiles and environments. How to precisely and efficiently decipher large-scale of microbiome data and furtherly take advantages from it has become one of the most essential bottlenecks for microbiome research at present. In this mini-review, we focus on the three key steps of analyzing cross-study microbiome datasets, including microbiome profiling, data integrating and data mining. By introducing the current bioinformatics approaches and discussing their limitations, we prospect the opportunities in development of computational methods for the three steps, and propose the promising solutions to multi-omics data analysis for comprehensive understanding and rapid investigation of microbiome from different angles, which could potentially promote the data-driven research by providing a broader view of the "microbiome data space".

Consumption of Fermented Foods Is Associated with Systematic Differences in the Gut Microbiome and Metabolome

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.

Human Skin, Oral, and Gut Microbiomes Predict Chronological Age

Human gut microbiomes are known to change with age, yet the relative value of human microbiomes across the body as predictors of age, and prediction robustness across populations is unknown. In this study, we tested the ability of the oral, gut, and skin (hand and forehead) microbiomes to predict age in adults using random forest regression on data combined from multiple publicly available studies, evaluating the models in each cohort individually. Intriguingly, the skin microbiome provides the best prediction of age (mean ± standard deviation, 3.8 ± 0.45 years, versus 4.5 ± 0.14 years for the oral microbiome and 11.5 ± 0.12 years for the gut microbiome). This also agrees with forensic studies showing that the skin microbiome predicts postmortem interval better than microbiomes from other body sites. Age prediction models constructed from the hand microbiome generalized to the forehead and vice versa, across cohorts, and results from the gut microbiome generalized across multiple cohorts (United States, United Kingdom, and China). Interestingly, taxa enriched in young individuals (18 to 30 years) tend to be more abundant and more prevalent than taxa enriched in elderly individuals (>60 yrs), suggesting a model in which physiological aging occurs concomitantly with the loss of key taxa over a lifetime, enabling potential microbiome-targeted therapeutic strategies to prevent aging.IMPORTANCE Considerable evidence suggests that the gut microbiome changes with age or even accelerates aging in adults. Whether the age-related changes in the gut microbiome are more or less prominent than those for other body sites and whether predictions can be made about a person's age from a microbiome sample remain unknown. We therefore combined several large studies from different countries to determine which body site's microbiome could most accurately predict age. We found that the skin was the best, on average yielding predictions within 4 years of chronological age. This study sets the stage for future research on the role of the microbiome in accelerating or decelerating the aging process and in the susceptibility for age-related diseases.

Species abundance information improves sequence taxonomy classification accuracy

Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments.

Taxonomy classification of amplicon sequences is an important step in investigating microbial communities in microbiome analysis. Here, the authors show incorporating environment-specific taxonomic abundance information can lead to improved species-level classification accuracy across common sample types.

Species abundance information improves sequence taxonomy classification accuracy

Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments.

Challenges in the construction of knowledge bases for human microbiome-disease associations

The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these links, but individual studies produce disconnected pieces of knowledge bounded in context by the perspective of expert researchers reading full-text publications. Building a knowledge base (KB) consolidating these disconnected pieces is an essential first step to democratize and accelerate the process of accessing the collective discoveries of human disease connections to the human microbiome. In this article, we survey the existing tools and development efforts that have been produced to capture portions of the information needed to construct a KB of all known human microbiome-disease associations and highlight the need for additional innovations in natural language processing (NLP), text mining, taxonomic representations, and field-wide vocabulary standardization in human microbiome research. Addressing these challenges will enable the construction of KBs that help identify new insights amenable to experimental validation and potentially clinical decision support.