Metabolome of human gut microbiome is predictive of host dysbiosis

The increased tendency for anemia in traditional Chinese medicine deficient body constitution is associated with the gut microbiome

Background

Constitution is a valuable part of traditional Chinese medicine theory; it is defined as the internal foundation for the occurrence, development, transformation and outcome of diseases, and has its characteristic gut microbiota. Previous study showed that deficiency constitution was related to lower Hb counts. However, no research has examined how alterations in the gut microbiome induced by deficiency constitution may increase the tendency for anemia.

Methods

We used a multiomics strategy to identify and quantify taxonomies and compounds found under deficient constitution individuals and further explore the possible pathological factors that affect red blood cell indices.

Results

① People with deficient constitution showed lower hemoglobin (Hb), more Firmicutes, less Bacteroidetes, and higher α diversity. ② We identified Escherichia coli, Clostridium bolteae, Ruminococcus gnavus, Streptococcus parasanguinis and Flavonifractor plautii as potential biomarkers of deficient constitution. ③ Slackia piriformis, Clostridium_sp_L2_50 and Bacteroides plebeius were enriched in balanced-constitution individuals, and Parabacteroides goldsteinii was the key bacterial marker of balanced constitution. ④ Flavonifractor plautii may be a protective factor against the tendency for anemia among deficient individuals. ⑤ Ruminococcus gnavus may be the shared microbe base of deficiency constitution-related the tendency for anemia. ⑥ The microorganism abundance of the anaerobic phenotype was lower in deficient constitution group. ⑦ Alterations in the microbiome of deficient-constitution individuals were associated with worse health status and a greater risk of anemia, involving intestinal barrier function, metabolism and immune responses, regulated by short-chain fatty acids and bile acid production.

Conclusion

The composition of the gut microbiome was altered in people with deficient constitution, which may explain their poor health status and tendency toward anemia.

Gut microbiome-metabolome interactions predict host condition

BACKGROUND

The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment.

RESULTS

We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]).

CONCLUSION

These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.

Dietary intervention improves metabolic levels in patients with type 2 diabetes through the gut microbiota: a systematic review and meta-analysis

Background

Poor dietary structure plays a pivotal role in the development and progression of type 2 diabetes and is closely associated with dysbiosis of the gut microbiota. Thus, the objective of this systematic review was to assess the impact of dietary interventions on improving gut microbiota and metabolic levels in patients with type 2 diabetes.

Methods

We conducted a systematic review and meta-analysis following the PRISMA 2020 guidelines.

Results

Twelve studies met the inclusion criteria. In comparison to baseline measurements, the high-fiber diet produced substantial reductions in FBG (mean difference -1.15 mmol/L; 95% CI, -2.24 to -0.05; I2 = 94%; P = 0.04), HbA1c (mean difference -0.99%; 95% CI, -1.93 to -0.03; I2 = 89%; P = 0.04), and total cholesterol (mean difference -0.95 mmol/L; 95% CI, -1.57 to -0.33; I2 = 77%; P = 0.003); the high-fat and low-carbohydrate diet led to a significant reduction in HbA1c (mean difference -0.98; 95% CI, -1.50 to -0.46; I2 = 0%; P = 0.0002). Within the experimental group (intervention diets), total cholesterol (mean difference -0.69 mmol/L; 95% CI, -1.27 to -0.10; I2 = 52%; P = 0.02) and LDL-C (mean difference -0.45 mmol/L; 95% CI, -0.68 to -0.22; I2 = 0%; P < 0.0001) experienced significant reductions in comparison to the control group (recommended diets for type 2 diabetes). However, no statistically significant differences emerged in the case of FBG, HbA1c, HOMA-IR, and HDL-C between the experimental and control groups. The high dietary fiber diet triggered an augmented presence of short-chain fatty acid-producing bacteria in the intestines of individuals with T2DM. In addition, the high-fat and low-carbohydrate diet resulted in a notable decrease in Bacteroides abundance while simultaneously increasing the relative abundance of Eubacterium. Compared to a specific dietary pattern, personalized diets appear to result in the production of a greater variety of beneficial bacteria in the gut, leading to more effective blood glucose control in T2D patients.

Conclusion

Dietary interventions hold promise for enhancing metabolic profiles in individuals with T2D through modulation of the gut microbiota. Tailored dietary regimens appear to be more effective than standard diets in improving glucose metabolism. However, given the limited and highly heterogeneous nature of the current sample size, further well-designed and controlled intervention studies are warranted in the future.

Integrated de novo gene prediction and peptide assembly of metagenomic sequencing data

Metagenomics is the study of all genomic content contained in given microbial communities. Metagenomic functional analysis aims to quantify protein families and reconstruct metabolic pathways from the metagenome. It plays a central role in understanding the interaction between the microbial community and its host or environment. De novo functional analysis, which allows the discovery of novel protein families, remains challenging for high-complexity communities. There are currently three main approaches for recovering novel genes or proteins: de novo nucleotide assembly, gene calling and peptide assembly. Unfortunately, their information dependency has been overlooked, and each has been formulated as an independent problem. In this work, we develop a sophisticated workflow called integrated Metagenomic Protein Predictor (iMPP), which leverages the information dependencies for better de novo functional analysis. iMPP contains three novel modules: a hybrid assembly graph generation module, a graph-based gene calling module, and a peptide assembly-based refinement module. iMPP significantly improved the existing gene calling sensitivity on unassembled metagenomic reads, achieving a 92-97% recall rate at a high precision level (>85%). iMPP further allowed for more sensitive and accurate peptide assembly, recovering more reference proteins and delivering more hypothetical protein sequences. The high performance of iMPP can provide a more comprehensive and unbiased view of the microbial communities under investigation. iMPP is freely available from https://github.com/Sirisha-t/iMPP.

Modeling interaction networks between host, diet, and bacteria predicts obesogenesis in a mouse model

Host-microbiome interactions are known to have substantial effects on human health, but the diversity of the human microbiome makes it difficult to definitively attribute specific microbiome features to a host phenotype. One approach to overcoming this challenge is to use animal models of host-microbiome interaction, but it must be determined that relevant aspects of host-microbiome interactions are reflected in the animal model. One such experimental validation is an experiment by Ridura et al. In that experiment, transplanting a microbiome from a human into a mouse also conferred the human donor's obesity phenotype. We have aggregated a collection of previously published host-microbiome mouse-model experiments and combined it with thousands of sequenced and annotated bacterial genomes and metametabolomic pathways. Three computational models were generated, each model reflecting an aspect of host-microbiome interactions: 1) Predict the change in microbiome community structure in response to host diet using a community interaction network, 2) Predict metagenomic data from microbiome community structure, and 3) Predict host obesogenesis from modeled microbiome metagenomic data. These computationally validated models were combined into an integrated model of host-microbiome-diet interactions and used to replicate the Ridura experiment in silico. The results of the computational models indicate that network-based models are significantly more predictive than similar but non-network-based models. Network-based models also provide additional insight into the molecular mechanisms of host-microbiome interaction by highlighting metabolites and metabolic pathways proposed to be associated with microbiome-based obesogenesis. While the models generated in this study are likely too specific to the animal models and experimental conditions used to train our models to be of general utility in a broader understanding of obesogenesis, the approach detailed here is expected to be a powerful tool of investigating multiple types of host-microbiome interactions.

Cocoa seeds and chocolate products interaction with gut microbiota; mining microbial and functional biomarkers from mechanistic studies, clinical trials and 16S rRNA amplicon sequencing

In recent years, gut microbiome has evolved as a focal point of interest with growing recognition that a well-balanced gut microbiota is highly relevant to an individual's health status. The present review provides a mechanistic insight on the effects of cocoa chemicals on the gut microbiome and further reveals in silico biomarkers, taxonomic and functional features that distinguish gut microbiome of cocoa consumers and controls by using 16S rRNA gene sequencing data. The polyphenols in cocoa can change the gut microbiota either by inhibiting the growth of pathogenic bacteria in the gut such as Clostridium perfringens or by increasing the growth of beneficial microbiota in the gut such as Lactobacillus and Bifidobacterium. This paper demonstrates the holistic effect of gut microbiota on cocoa chemicals and how it impacts human health. We present herein the first comprehensive review and analysis of how raw and roasted cocoa and its products can specifically influence gut homeostasis, and likewise, how microbiota metabolizes cocoa chemicals. In addition to that, our 16S rRNA amplicon sequencing analysis revealed that the flavone and flavonols metabolism, aminobenzoate degradation and fatty acid elongation pathways represent the three most important signatures of microbial functions associated with cocoa consumption.

Axial spondyloarthritis patients have altered mucosal IgA response to oral and fecal microbiota

Axial spondyloarthritis (axSpA) is an inflammatory arthritis involving the spine and the sacroiliac joint with extra-articular manifestations in the eye, gut, and skin. The intestinal microbiota has been implicated as a central environmental component in the pathogenesis of various types of spondyloarthritis including axSpA. Additionally, alterations in the oral microbiota have been shown in various rheumatological conditions, such as rheumatoid arthritis (RA). Therefore, the aim of this study was to investigate whether axSpA patients have an altered immunoglobulin A (IgA) response in the gut and oral microbial communities. We performed 16S rRNA gene (16S) sequencing on IgA positive (IgA⁺) and IgA negative (IgA^-) fractions (IgA-SEQ) from feces (n=17 axSpA; n=14 healthy) and saliva (n=14 axSpA; n=12 healthy), as well as on IgA-unsorted fecal and salivary samples. PICRUSt2 was used to predict microbial metabolic potential in axSpA patients and healthy controls (HCs). IgA-SEQ analyses revealed enrichment of several microbes in the fecal (Akkermansia, Ruminococcaceae, Lachnospira) and salivary (Prevotellaceae, Actinobacillus) microbiome in axSpA patients as compared with HCs. Fecal microbiome from axSpA patients showed a tendency towards increased alpha diversity in IgA⁺ fraction and decreased diversity in IgA^- fraction in comparison with HCs, while the salivary microbiome exhibits a significant decrease in alpha diversity in both IgA⁺ and IgA^- fractions. Increased IgA coating of Clostridiales Family XIII in feces correlated with disease severity. Inferred metagenomic analysis suggests perturbation of metabolites and metabolic pathways for inflammation (oxidative stress, amino acid degradation) and metabolism (propanoate and butanoate) in axSpA patients. Analyses of fecal and salivary microbes from axSpA patients reveal distinct populations of immunoreactive microbes compared to HCs using the IgA-SEQ approach. These bacteria were not identified by comparing their relative abundance alone. Predictive metagenomic analysis revealed perturbation of metabolites/metabolic pathways in axSpA patients. Future studies on these immunoreactive microbes may lead to better understanding of the functional role of IgA in maintaining microbial structure and human health.

Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine

The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 days of age). Rectal swabs were taken from each animal at 158 ± 4 days of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including four kernel functions (Linear Kernel, LK; Polynomial Kernel, PK; Gaussian Kernel, GK; Arc-cosine Kernel with one hidden layer, AK1), two dissimilarity methods (Bray-Curtis, BC; Jaccard, JA), and two ordination methods (Metric Multidimensional Scaling, MDS; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine.

Profiling gut microbiota and bile acid metabolism in critically ill children

Broad-spectrum antimicrobial use during the treatment of critical illness influences gastrointestinal fermentation endpoints, host immune response and metabolic activity including the conversion of primary to secondary bile acids. We previously observed reduced fermentation capacity in the faecal microbiota of critically ill children upon hospital admission. Here, we further explore the timecourse of the relationship between the microbiome and bile acid profile in faecal samples collected from critically ill children. The microbiome was assayed by sequencing of the 16S rRNA gene, and faecal water bile acids were measured by liquid chromatography mass spectrometry. In comparison to admission faecal samples, members of the Lachnospiraceae recovered during the late-acute phase (days 8-10) of hospitalisation. Patients with infections had a lower proportion of Lachnospiraceae in their gut microbiota than controls and patients with primary admitting diagnoses. Keystone species linked to ecological recovery were observed to decline with the length of PICU admission. These species were further suppressed in patients with systemic infection, respiratory failure, and undergoing surgery. Bile acid composition recovers quickly after intervention for critical illness which may be aided by the compositional shift in Lachnospiraceae. Our findings suggest gut microbiota recovery can be readily assessed via measurement of faecal bile acids.

Explainable Machine Learning for Longitudinal Multi-Omic Microbiome

Over the years, research studies have shown there is a key connection between the microbial community in the gut, genes, and immune system. Understanding this association may help discover the cause of complex chronic idiopathic disorders such as inflammatory bowel disease. Even though important efforts have been put into the field, the functions, dynamics, and causation of dysbiosis state performed by the microbial community remains unclear. Machine learning models can help elucidate important connections and relationships between microbes in the human host. Our study aims to extend the current knowledge of associations between the human microbiome and health and disease through the application of dynamic Bayesian networks to describe the temporal variation of the gut microbiota and dynamic relationships between taxonomic entities and clinical variables. We develop a set of preprocessing steps to clean, filter, select, integrate, and model informative metagenomics, metatranscriptomics, and metabolomics longitudinal data from the Human Microbiome Project. This study accomplishes novel network models with satisfactory predictive performance (accuracy = 0.648) for each inflammatory bowel disease state, validating Bayesian networks as a framework for developing interpretable models to help understand the basic ways the different biological entities (taxa, genes, metabolites) interact with each other in a given environment (human gut) over time. These findings can serve as a starting point to advance the discovery of novel therapeutic approaches and new biomarkers for precision medicine.

Pivotal Dominant Bacteria Ratio and Metabolites Related to Healthy Body Index Revealed by Intestinal Microbiome and Metabolomics

Various body indexes, especially body fat percentage (BFP), are widely used as effective indicators to measure our health. BFP is used in medicine to assess obesity, which is a body fat mass disorder accompanied with changes of the gut microbiota. However, the relationship between BFP and the gut microbiota has not been studied so far. To address this problem, we examined how gut microbiota and metabolome associated with body indices in healthy people. Microbial and metabolomics data based on 16S rDNA sequencing and LC-MS were obtained from stool samples of 20 healthy adults. Bioinformatics analysis was performed to explore the correlations between the body indices and gut microbial characteristics. Significantly different microbes were further validated via qPCR. Differential characteristics were filtered by building machine learning models to predict body status. Our data showed that abundance of Prevotella and the Prevotella/Bacteroides (P/B) ratio in the gut were markedly higher in high-BFP individuals than in low-BFP individuals. Microbial and metabolomics data consistently suggested significant differences in fatty acid metabolism in stool samples from the two groups. The P/B ratio and fatty acids are discriminative for people with different index levels by cross validation tests with machine learning models. These results suggest using Prevotella and fecal fatty acids as predictors may offer an alternative method for evaluating health status or weight loss.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12088-021-00989-5.

Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment

Emerging evidence suggests that host-microbe interaction in the cervicovaginal microenvironment contributes to cervical carcinogenesis, yet dissecting these complex interactions is challenging. Herein, we performed an integrated analysis of multiple "omics" datasets to develop predictive models of the cervicovaginal microenvironment and identify characteristic features of vaginal microbiome, genital inflammation and disease status. Microbiomes, vaginal pH, immunoproteomes and metabolomes were measured in cervicovaginal specimens collected from a cohort (n = 72) of Arizonan women with or without cervical neoplasm. Multi-omics integration methods, including neural networks (mmvec) and Random Forest supervised learning, were utilized to explore potential interactions and develop predictive models. Our integrated analyses revealed that immune and cancer biomarker concentrations were reliably predicted by Random Forest regressors trained on microbial and metabolic features, suggesting close correspondence between the vaginal microbiome, metabolome, and genital inflammation involved in cervical carcinogenesis. Furthermore, we show that features of the microbiome and host microenvironment, including metabolites, microbial taxa, and immune biomarkers are predictive of genital inflammation status, but only weakly to moderately predictive of cervical neoplastic disease status. Different feature classes were important for prediction of different phenotypes. Lipids (e.g. sphingolipids and long-chain unsaturated fatty acids) were strong predictors of genital inflammation, whereas predictions of vaginal microbiota and vaginal pH relied mostly on alterations in amino acid metabolism. Finally, we identified key immune biomarkers associated with the vaginal microbiota composition and vaginal pH (MIF), as well as genital inflammation (IL-6, IL-10, MIP-1α).

Alteration of the Intestinal Permeability Are Reflected by Changes in the Urine Metabolome of Young Autistic Children: Preliminary Results

Several metabolomics-based studies have provided evidence that autistic subjects might share metabolic abnormalities with gut microbiota dysbiosis and alterations in gut mucosal permeability. Our aims were to explore the most relevant metabolic perturbations in a group of autistic children, compared with their healthy siblings, and to investigate whether the increased intestinal permeability may be mirrored by specific metabolic perturbations. We enrolled 13 autistic children and 14 unaffected siblings aged 2–12 years; the evaluation of the intestinal permeability was estimated by the lactulose:mannitol test. The urine metabolome was investigated by proton nuclear magnetic resonance (1H-NMR) spectroscopy. The lactulose:mannitol test unveiled two autistic children with altered intestinal permeability. Nine metabolites significantly discriminated the urine metabolome of autistic children from that of their unaffected siblings; however, in the autistic children with increased permeability, four additional metabolites—namely, fucose, phenylacetylglycine, nicotinurate, and 1-methyl-nicotinamide, strongly discriminated their urine metabolome from that of the remaining autistic children. Our preliminary data suggest the presence of a specific urine metabolic profile associated with the increase in intestinal permeability.

A Systematic Review of Metabolic Alterations Underlying IgE-Mediated Food Allergy in Children

SCOPE

Immunoglobulin E-mediated food allergies (IgE-FA) are characterized by an ever-increasing prevalence, currently reaching up to 10.4% of children in the European Union. Metabolomics has the potential to provide a deeper understanding of the pathogenic mechanisms behind IgE-FA.

METHODS AND RESULTS

In this work, literature is systematically searched using Web of Science, PubMed, Scopus, and Embase, from January 2010 until May 2021, including human and animal metabolomic studies on multiple biofluids (urine, blood, feces). In total, 15 studies on IgE-FA are retained and a dataset of 277 potential biomarkers is compiled for in-depth pathway mapping. Decreased indoleamine 2,3-dioxygenase-1 (IDO- 1) activity is hypothesized due to altered plasma levels of tryptophan and its metabolites in IgE-FA children. In feces of children prior to IgE-FA, aberrant metabolization of sphingolipids and histidine is noted. Decreased fecal levels of (branched) short chain fatty acids ((B)SCFAs) compel a shift towards aerobic glycolysis and suggest dysbiosis, associated with an immune system shift towards T-helper 2 (Th2) responses. During animal anaphylaxis, a similar switch towards glycolysis is observed, combined with increased ketogenic pathways. Additionally, altered histidine, purine, pyrimidine, and lipid pathways are observed.

CONCLUSION

To conclude, this work confirms the unprecedented opportunities of metabolomics and supports the in-depth pathophysiological qualification in the quest towards improved diagnostic and prognostic biomarkers for IgE-FA.

MiMeNet: Exploring microbiome-metabolome relationships using neural networks

The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through “Guilt by Association”. Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.

The microbiome has shown to functionally interact with its host or environment at a metabolic level, however the exact nature of these interactions is not well understood. In addition, metabolic dysregulation caused by the microbiome is believed to contribute to the development of diseases such as inflammatory bowel disease, diabetes mellitus, and obesity. In this manuscript, we introduce a computational framework to integrate microbiome and metabolome data to uncover microbe-metabolite interactions in a data-driven manner. Our model uses neural networks to predict metabolite abundances from microbe abundances. The trained models are then used to derive microbe-metabolite feature scores, which are used for clustering microbes and metabolites into functional modules. These module-based interactions are useful in generating biological insights and facilitating hypothesis generation for the investigation of their roles in various metabolic diseases. The software of our model is made freely available to interested researchers.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Impact of Appendectomy on Gut Microbiota

Background: Considered vestigial from the classic point of view, the vermiform appendix has long been the subject of intensive studies. The recent understanding of appendix function in the context of unique architecture and bacterial complexity and density allows considering it as a safehouse for intestinal biodiversity. Methods: This review analyzes and assesses the current state of scientific knowledge regarding the role of the vermiform appendix in normal gut microbiota maintenance as a crucial factor of host homeostasis. It also highlights the difference in microbial composition between the large bowel and the appendix, as well as the association between the surgical excision, appendectomy, and dysbiosis-induced diseases. In addition, the review discusses the results of epidemiologic studies on appendectomy as a risk factor for the initiation of gastrointestinal carcinogenesis. It also highlights the association between appendectomy and a series of chronic inflammatory and neurologic disorders, including inflammatory bowel disease.

Bacterial Microbiota-derived Extracellular Vesicles in Children With Allergic Airway Diseases: Compositional and Functional Features

PURPOSE

Bacterial extracellular vesicles (EVs) play crucial roles in bacteria-host interactions. Due to their cargo, EVs are considered fingerprints of the parent cell, which are detectable in body fluids. We studied the composition and function of bacterial microbiota-derived EVs genes in urine to evaluate whether they have specific characteristics concerning allergic airway disease.

METHODS

Subjects were from elementary school surveys and classified into 3 groups according to questionnaires and sensitization to aeroallergens: the allergic airway group (AA, n = 16), atopic controls (AC, n = 7) and healthy controls (HC, n = 26). The bacterial EVs were isolated from voided urine samples, their nucleic acid was extracted for 16S ribosomal RNA pyrosequencing and then characterized using α-diversity, β-diversity, network analysis, intergroup comparison of bacterial composition and predicted functions, and correlation with total immunoglobulin E (IgE), eosinophils% and fractional exhaled NO.

RESULTS

The compositional α-diversity was the highest in AA, while functional α-diversity was the highest in HC. AA had a distinct clustering with the least intersample variation. Klebsiella, Haemophilus, members from Lachnospiraceae and Ruminococcaceae, and the pathways of sphingolipid and glycerolipid metabolism, and biosynthesis of peptidoglycan and lysine were the highest in AA and positively correlated with total IgE or eosinophil%. Genetic information processing function contributed to 48% of the intergroup variance and was the highest in AA. Diaphorobacter, Acinetobacter, and the pathways of short-chain fatty acids and anti-oxidants metabolism, lysine and xenobiotic degradation, and lipopolysaccharide biosynthesis were the lowest in AA and negatively correlated with total IgE or eosinophil%. The bacterial composition and function in AC were closer to those in HC. The bacterial network was remarkably dense in HC.

CONCLUSIONS

The bacterial microbiota-derived EVs in urine possess characteristic features in allergic airway disease with a remarkable correlation with total IgE and eosinophil%. These findings suggest that they may play important roles in allergic airway diseases.

The Urine Metabolome of Young Autistic Children Correlates with Their Clinical Profile Severity

Autism diagnosis is moving from the identification of common inherited genetic variants to a systems biology approach. The aims of the study were to explore metabolic perturbations in autism, to investigate whether the severity of autism core symptoms may be associated with specific metabolic signatures; and to examine whether the urine metabolome discriminates severe from mild-to-moderate restricted, repetitive, and stereotyped behaviors. We enrolled 57 children aged 2–11 years; thirty-one with idiopathic autism and twenty-six neurotypical (NT), matched for age and ethnicity. The urine metabolome was investigated by gas chromatography-mass spectrometry (GC-MS). The urinary metabolome of autistic children was largely distinguishable from that of NT children; food selectivity induced further significant metabolic differences. Severe autism spectrum disorder core deficits were marked by high levels of metabolites resulting from diet, gut dysbiosis, oxidative stress, tryptophan metabolism, mitochondrial dysfunction. The hierarchical clustering algorithm generated two metabolic clusters in autistic children: 85–90% of children with mild-to-moderate abnormal behaviors fell in cluster II. Our results open up new perspectives for the more general understanding of the correlation between the clinical phenotype of autistic children and their urine metabolome. Adipic acid, palmitic acid, and 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid can be proposed as candidate biomarkers of autism severity.

Analysis of brain networks and fecal metabolites reveals brain-gut alterations in premenopausal females with irritable bowel syndrome

Alterations in brain-gut-microbiome (BGM) interactions have been implicated in the pathogenesis of irritable bowel syndrome (IBS). Here, we apply a systems biology approach, leveraging neuroimaging and fecal metabolite data, to characterize BGM interactions that are driving IBS pathophysiology. Fecal samples and resting state fMRI images were obtained from 138 female subjects (99 IBS, 39 healthy controls (HCs)). Partial least-squares discriminant analysis (PLS-DA) was conducted to explore group differences, and partial correlation analysis explored significantly changed metabolites and neuroimaging data. All correlational tests were performed controlling for age, body mass index, and diet; results are reported after FDR correction, with q < 0.05 as significant. Compared to HCs, IBS showed increased connectivity of the putamen with regions of the default mode and somatosensory networks. Metabolite pathways involved in nucleic acid and amino acid metabolism differentiated the two groups. Only a subset of metabolites, primarily amino acids, were associated with IBS-specific brain changes, including tryptophan, glutamate, and histidine. Histidine was the only metabolite positively associated with both IBS-specific alterations in brain connectivity. Our findings suggest a role for several amino acid metabolites in modulating brain function in IBS. These metabolites may alter brain connectivity directly, by crossing the blood-brain-barrier, or indirectly through peripheral mechanisms. This is the first study to integrate both neuroimaging and fecal metabolite data supporting the BGM model of IBS, building the foundation for future mechanistic studies on the influence of gut microbial metabolites on brain function in IBS.

Gut microbiome, big data and machine learning to promote precision medicine for cancer

The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The 'omics' technologies used for microbiome analysis continuously evolve and, although much of the research is still at an early stage, large-scale datasets of ever increasing size and complexity are being produced. However, there are varying levels of difficulty in realizing the full potential of these new tools, which limit our ability to critically analyse much of the available data. In this Perspective, we provide a brief overview on the role of gut microbiome in cancer and focus on the need, role and limitations of a machine learning-driven approach to analyse large amounts of complex health-care information in the era of big data. We also discuss the potential application of microbiome-based big data aimed at promoting precision medicine in cancer.

16S rRNA Sequencing and Metagenomics Study of Gut Microbiota: Implications of BDB on Type 2 Diabetes Mellitus

Gut microbiota has a critical role in metabolic diseases, including type 2 diabetes mellitus (T2DM). 3-bromo-4,5-bis(2,3-dibromo-4,5-dihydroxybenzyl)-1,2-benzenediol (BDB) is a natural bromophenol isolated from marine red alga Rhodomela confervoides. Our latest research showed that BDB could alleviate T2DM in diabetic BKS db mice. To find out whether BDB modulates the composition of the gut microbiota during T2DM treatment, 24 BKS db diabetic mice were randomly grouped to receive BDB (n = 6), metformin (n = 6), or the vehicle (n = 6) for 7 weeks in a blinded manner. Non-diabetic BKS mice (n = 6) were used as normal control. Diabetic mice treated with BDB or metformin demonstrated significant reductions in fasting blood glucose (FBG) levels compared with the vehicle-treated mice in the 7th week. Pyrosequencing of the V3–V4 regions of the 16S rRNA gene revealed the changes of gut microbiota in response to BDB treatment. The result demonstrated short-chain acid (SCFA) producing bacteria Lachnospiraceae and Bacteroides were found to be significantly more abundant in the BDB and metformin treated group than the vehicle-treatment diabetic group. Remarkably, at the genus levels, Akkermansia elevated significantly in the BDB-treatment group. Metagenomic results indicated that BDB may alleviate the metabolic disorder of diabetic mice by promoting propanoate metabolism and inhibiting starch and sucrose metabolism, amino sugar and nucleotide sugar metabolism. In conclusion, our study suggests that the anti-diabetic effect of BDB is closely related to the modulating structure of gut microbiota and the improvement of functional metabolism genes of intestinal microorganisms.

Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome

BACKGROUND

Technological advances in next-generation sequencing (NGS) and chromatographic assays [e.g., liquid chromatography mass spectrometry (LC-MS)] have made it possible to identify thousands of microbe and metabolite species, and to measure their relative abundance. In this paper, we propose a sparse neural encoder-decoder network to predict metabolite abundances from microbe abundances.

RESULTS

Using paired data from a cohort of inflammatory bowel disease (IBD) patients, we show that our neural encoder-decoder model outperforms linear univariate and multivariate methods in terms of accuracy, sparsity, and stability. Importantly, we show that our neural encoder-decoder model is not simply a black box designed to maximize predictive accuracy. Rather, the network's hidden layer (i.e., the latent space, comprised only of sparsely weighted microbe counts) actually captures key microbe-metabolite relationships that are themselves clinically meaningful. Although this hidden layer is learned without any knowledge of the patient's diagnosis, we show that the learned latent features are structured in a way that predicts IBD and treatment status with high accuracy.

CONCLUSIONS

By imposing a non-negative weights constraint, the network becomes a directed graph where each downstream node is interpretable as the additive combination of the upstream nodes. Here, the middle layer comprises distinct microbe-metabolite axes that relate key microbial biomarkers with metabolite biomarkers. By pre-processing the microbiome and metabolome data using compositional data analysis methods, we ensure that our proposed multi-omics workflow will generalize to any pair of -omics data. To the best of our knowledge, this work is the first application of neural encoder-decoders for the interpretable integration of multi-omics biological data.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches

In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in study design and confounding variables across studies often lead to inconsistent observations. In this narrative review, we have discussed the potential influence of study design and confounding variables on the current sequencing-based oral microbiome-systemic disease link studies. The current limitations of oral microbiome-systemic link studies on type 2 diabetes mellitus, rheumatoid arthritis, pregnancy, atherosclerosis, and pancreatic cancer are discussed in this review, followed by our perspective on how artificial intelligence (AI), particularly machine learning and deep learning approaches, can be employed for predicting systemic disease and host metadata from the oral microbiome. The application of AI for predicting systemic disease as well as host metadata requires the establishment of a global database repository with microbiome sequences and annotated host metadata. However, this task requires collective efforts from researchers working in the field of oral microbiome to establish more comprehensive datasets with appropriate host metadata. Development of AI-based models by incorporating consistent host metadata will allow prediction of systemic diseases with higher accuracies, bringing considerable clinical benefits.

Evaluation of Microbiome-Host Relationships in the Zebrafish Gastrointestinal System Reveals Adaptive Immunity Is a Target of Bis(2-ethylhexyl) Phthalate (DEHP) Exposure

To improve physical characteristics of plastics such as flexibility and durability, producers enrich materials with phthalates such as di-2-(ethylhexyl) phthalate (DEHP). DEHP is a high production volume chemical associated with metabolic and immune disruption in animals and humans. To reveal mechanisms implicated in phthalate-related disruption in the gastrointestinal system, male and female zebrafish were fed DEHP (3 ppm) daily for two months. At the transcriptome level, DEHP significantly upregulated gene networks in the intestine associated with helper T cells' (Th1, Th2, and Th17) specific pathways. The activation of gene networks associated with adaptive immunity was linked to the suppression of networks for tight junction, gap junctional intercellular communication, and transmembrane transporters, all of which are precursors for impaired gut integrity and performance. On a class level, DEHP exposure increased Bacteroidia and Gammaproteobacteria and decreased Verrucomicrobiae in both the male and female gastrointestinal system. Further, in males there was a relative increase in Fusobacteriia and Betaproteobacteria and a relative decrease in Saccharibacteria. Predictive algorithms revealed that the functional shift in the microbiome community, and the metabolites they produce, act to modulate intestinal adaptive immunity. This finding suggests that the gut microbiota may contribute to the adverse effects of DEHP on the host by altering metabolites sensed by both intestinal and immune Th cells. Our results suggest that the microbiome-gut-immune axis can be modified by DEHP and emphasize the value of multiomics approaches to study microbiome-host interactions following chemical perturbations.

Decoding the language of microbiomes using word-embedding techniques, and applications in inflammatory bowel disease

Microbiomes are complex ecological systems that play crucial roles in understanding natural phenomena from human disease to climate change. Especially in human gut microbiome studies, where collecting clinical samples can be arduous, the number of taxa considered in any one study often exceeds the number of samples ten to one hundred-fold. This discrepancy decreases the power of studies to identify meaningful differences between samples, increases the likelihood of false positive results, and subsequently limits reproducibility. Despite the vast collections of microbiome data already available, biome-specific patterns of microbial structure are not currently leveraged to inform studies. Here, we derive microbiome-level properties by applying an embedding algorithm to quantify taxon co-occurrence patterns in over 18,000 samples from the American Gut Project (AGP) microbiome crowdsourcing effort. We then compare the predictive power of models trained using properties, normalized taxonomic count data, and another commonly used dimensionality reduction method, Principal Component Analysis in categorizing samples from individuals with inflammatory bowel disease (IBD) and healthy controls. We show that predictive models trained using property data are the most accurate, robust, and generalizable, and that property-based models can be trained on one dataset and deployed on another with positive results. Furthermore, we find that properties correlate significantly with known metabolic pathways. Using these properties, we are able to extract known and new bacterial metabolic pathways associated with inflammatory bowel disease across two completely independent studies. By providing a set of pre-trained embeddings, we allow any V4 16S amplicon study to apply the publicly informed properties to increase the statistical power, reproducibility, and generalizability of analysis.

Incidental Prophylactic Appendectomy Is Associated with a Profound Microbial Dysbiosis in the Long-Term

Incidental prophylactic surgeries are performed in certain situations. Incidental prophylactic appendectomies were common practice within opened bariatric surgeries. The gut microbiota has emerged as an important actor within the homeostasis of the host. A new hypothesis has been formulated about the appendix function in relation to gut microbiota. Our objective was to study the gut microbiota profiles of patients that had suffered from an incidental prophylactic appendectomy during their bariatric surgeries, while comparing them to patients whose appendixes had remained intact. A case-control observational prospective study of 40 patients who underwent bariatric surgery, with or without an incidental prophylactic appendectomy, during 2004–2008 with an evaluation of their gut microbiota populations at the end of 2016 was conducted by sequencing the 16 S rRNA gene by Next Generation Sequencing of patients’ stools and appendix tissues. Patients with their appendix removed showed lower levels of richness and diversity of their gut microbiota populations. Odoribacter, Bilophila, Butyricimonas, and Faecalibacterium levels were increased in the Intact group, while Lachnobacterium suffered an expansion in the group without the appendix. Moreover, a linear regression model introduced the concept that Butyricimonas and Odoribacter may be implicated in insulin regulation. Thus, gut microbiota should be considered in the decisions of practical surgery, regarding the appendix as a mediator of homeostasis in the host. Butyricimonas and Odoribacter require further investigation as key bacteria implicated in insulin regulation.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

An Integrated Multi-Disciplinary Perspectivefor Addressing Challenges of the Human Gut Microbiome

Our understanding of the human gut microbiome has grown exponentially. Advances in genome sequencing technologies and metagenomics analysis have enabled researchers to study microbial communities and their potential function within the context of a range of human gut related diseases and disorders. However, up until recently, much of this research has focused on characterizing the gut microbiological community structure and understanding its potential through system wide (meta) genomic and transcriptomic-based studies. Thus far, the functional output of these microbiomes, in terms of protein and metabolite expression, and within the broader context of host-gut microbiome interactions, has been limited. Furthermore, these studies highlight our need to address the issues of individual variation, and of samples as proxies. Here we provide a perspective review of the recent literature that focuses on the challenges of exploring the human gut microbiome, with a strong focus on an integrated perspective applied to these themes. In doing so, we contextualize the experimental and technical challenges of undertaking such studies and provide a framework for capitalizing on the breadth of insight such approaches afford. An integrated perspective of the human gut microbiome and the linkages to human health will pave the way forward for delivering against the objectives of precision medicine, which is targeted to specific individuals and addresses the issues and mechanisms in situ.

The Connection and Disconnection Between Microbiome and Metabolome: A Critical Appraisal in Clinical Research

Big data-driven omics research has led to a steep rise in investigations involving two of the most functional omes, the metabolome and microbiome. The former is touted as the closest to the phenotype, and the latter is implicated in general well-being and a plethora of human diseases. Although some research publications have integrated the concepts of the two domains, most focus their analyses on evidence solely originating from one or the other. With a growing interest in connecting the microbiome and metabolome in the context of disease, researchers must also appreciate the disconnect between the two domains. In the present review, drawing examples from the current literature, tools, and resources, I discuss the connections between the microbiome and metabolome and highlight challenges and opportunities in linking them together for the basic, translational, clinical, and nursing research communities.

Analysis of free radical production capacity in mouse faeces and its possible application in evaluating the intestinal environment: a pilot study

Complex interplay between the intestinal environment and the host has attracted considerable attention and has been well studied with respect to the gut microbiome and metabolome. Oxygen free radicals such as superoxide and the hydroxyl radical (^•OH) are generated during normal cellular metabolism. They are toxic to both eukaryotic and prokaryotic cells and might thus affect intestinal homeostasis. However, the effect of oxygen free radicals on the intestinal environment has not been widely studied. Herein, we applied electron spin resonance spectroscopy with spin trapping reagents to evaluate oxygen free radical production capacity in the intestinal lumen and the faeces of mice. ^•OH was generated in faeces and lumens of the small and large intestines. There were no remarkable differences in ^•OH levels between faeces and the large intestine, suggesting that faeces can be used as alternative samples to estimate the ^•OH production capacity in the colonic contents. We then compared free radical levels in faecal samples among five different mouse strains (ddY, ICR, C57BL/6, C3H/HeJ, and BALB/c) and found that strain ddY had considerably higher levels than the other four strains. In addition, strain ddY was more susceptible to dextran sulphate sodium-induced colitis. These differences were possibly related to the relative abundance of the gut bacterial group Candidatus Arthromitus, which is known to modulate the host immune response. From these results, we suggest that the production capacity of oxygen free radicals in mouse faeces is associated with intestinal homeostasis.

AMON: annotation of metabolite origins via networks to integrate microbiome and metabolome data

Background

Untargeted metabolomics of host-associated samples has yielded insights into mechanisms by which microbes modulate health. However, data interpretation is challenged by the complexity of origins of the small molecules measured, which can come from the host, microbes that live within the host, or from other exposures such as diet or the environment.

Results

We address this challenge through development of AMON: Annotation of Metabolite Origins via Networks. AMON is an open-source bioinformatics application that can be used to annotate which compounds in the metabolome could have been produced by bacteria present or the host, to evaluate pathway enrichment of host verses microbial metabolites, and to visualize which compounds may have been produced by host versus microbial enzymes in KEGG pathway maps.

Conclusions

AMON empowers researchers to predict origins of metabolites via genomic information and to visualize potential host:microbe interplay. Additionally, the evaluation of enrichment of pathway metabolites of host versus microbial origin gives insight into the metabolic functionality that a microbial community adds to a host:microbe system. Through integrated analysis of microbiome and metabolome data, mechanistic relationships between microbial communities and host phenotypes can be better understood.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Equibiotic-GI Consumption Improves Intestinal Microbiota in Subjects with Functional Dyspepsia

Background:

At present, the interpretation of any dysfunction by pathogenic microbial colonization of the digestive tract can be considered as the rupture of the microbiotic balance in the injured or infected area. Phytodrugs with useful properties to balance the intestinal microbiota equibiotics represent an alternative recently proposed by the Medicinal Plant Research Company Phytomedicamenta S.A. The Equibiotic-GI® is a phytodrug developed as a combination of two plant extracts, obtained from the leaves of Psidium guajava L, (Myrtaceae) and the roots of Coptis chinensis Franch. (Racunculaceae). Both plants used traditionally for the treatment of several gastrointestinal disorders.

Objective:

The aim of the current study was to assess the effect of Equibiotic-GI® suspension on intestinal microbiota of subjects with functional dyspepsia.

Methods:

An open-label study performed in 8 adult subjects with functional dyspepsia receiving orally 20 mL of the suspension, daily for two weeks. Fecal samples were collected at baseline and the end of treatment for assessing gut microbiota composition by sequencing the V3-V4 region of the 16S rRNA gene.

Results:

Equibiotic-GI modified the Bacteriodetes/Firmicutes proportion increasing the richness of the microbiota composition and Rikenellaceae and Alistipes abundance.

Conclusion:

Together with the improvement in the gastrointestinal symptomatology after the consumption of the product, the present study is the first clinical demonstration of the capacity of the Equibiotic-GI® to restore and balance the intestinal microbiota.

Administration of Akkermansia muciniphila Ameliorates Dextran Sulfate Sodium-Induced Ulcerative Colitis in Mice

Inflammatory bowel diseases (IBDs) develop as a result of complex interactions among genes, innate immunity and environmental factors, which are related to the gut microbiota. Multiple clinical and animal data have shown that Akkermansia muciniphila is associated with a healthy mucosa. However, its precise role in colitis is currently unknown. Our study aimed to determine its protective effects and underlying mechanisms in a dextran sulfate sodium (DSS)-induced colitis mouse model. Twenty-four C57BL/6 male mice were administered A. muciniphila Muc^T or phosphate-buffered saline (PBS) once daily by oral gavage for 14 days. Colitis was induced by drinking 2% DSS from days 0 to 6, followed by 2 days of drinking normal water. Mice were weighed daily and then sacrificed on day 8. We found that A. muciniphila improved DSS-induced colitis, which was evidenced by reduced weight loss, colon length shortening and histopathology scores and enhanced barrier function. Serum and tissue levels of inflammatory cytokines and chemokines (TNF-α, IL1α, IL6, IL12A, MIP-1A, G-CSF, and KC) decreased as a result of A. muciniphila administration. Analysis of 16S rDNA sequences showed that A. muciniphila induced significant gut microbiota alterations. Furthermore, correlation analysis indicated that pro-inflammatory cytokines and other injury factors were negatively associated with Verrucomicrobia, Akkermansia, Ruminococcaceae, and Rikenellaceae, which were prominently abundant in A. muciniphila-treated mice. We confirmed that A. muciniphila treatment could ameliorate mucosal inflammation either via microbe-host interactions, which protect the gut barrier function and reduce the levels of inflammatory cytokines, or by improving the microbial community. Our findings suggest that A. muciniphila may be a potential probiotic agent for ameliorating colitis.

Novel Inter-omic Analysis Reveals Relationships Between Diverse Gut Microbiota and Host Immune Dysregulation in HLA-B27-Induced Experimental Spondyloarthritis

OBJECTIVE

To define inflammation-related host-microbe interactions in experimental spondyloarthritis (SpA) using novel inter-omic approaches.

METHODS

The relative frequency of gut microbes was determined by 16S ribosomal RNA (rRNA) gene sequencing, and gene expression using RNA-Seq of host tissue. HLA-B27/human β₂ -microglobulin-transgenic (HLA-B27-transgenic) and wild-type rats from dark agouti, Lewis, and Fischer backgrounds were used. Inter-omic analyses using Cytoscape were employed to identify relevant relationships. PICRUSt was used to predict microbial functions based on known metagenomic profiles.

RESULTS

Inter-omic analysis revealed several gut microbes that were strongly associated with dysregulated cytokines driving inflammatory response pathways, such as interleukin-17 (IL-17), IL-23, IL-17, IL-1, interferon-γ (IFNγ), and tumor necrosis factor (TNF). Many microbes were uniquely associated with inflammation in Lewis or Fischer rats, and one was relevant on both backgrounds. Several microbes that were strongly correlated with immune dysregulation were not differentially abundant in HLA-B27-transgenic compared to wild-type controls. A multi-omic network analysis revealed non-overlapping clusters of microbes in Lewis and Fischer rats that were strongly linked to overlapping dysregulated immune/inflammatory genes. Prevotella, Clostridiales, and Blautia were important in Lewis rats, while Akkermansia muciniphila and members of the Lachnospiraceae family dominated in Fischer rats. Inflammation-associated metabolic pathway perturbation (e.g., butanoate, propanoate, lipopolysaccharide, and steroid biosynthesis) was also predicted from both backgrounds.

CONCLUSION

Inter-omic and network analysis of gut microbes and the host immune response in experimental SpA provides an unprecedented view of organisms strongly linked to dysregulated IL-23, IL-17, IL-1, IFNγ, and TNF. Functional similarities between these organisms may explain why animals of different genetic backgrounds exhibit common patterns of immune dysregulation, possibly through perturbation of similar metabolic pathways. These results highlight the power of linking analyses of gut microbiota with the host immune response to gain insights into the role of dysbiotic microbes in SpA beyond taxonomic profiling.

Untargeted Metabolomics for Autism Spectrum Disorders: Current Status and Future Directions

Autism spectrum disorders (ASDs) are a group of neurodevelopment disorders characterized by childhood onset deficits in social communication and interaction. Although the exact etiology of most cases of ASDs is unknown, a portion has been proposed to be associated with various metabolic abnormalities including mitochondrial dysfunction, disorders of cholesterol metabolism, and folate abnormalities. Targeted biochemical testing like plasma amino acid and acylcarnitine profiles have demonstrated limited utility in helping to diagnose and manage such patients. Untargeted metabolomics has emerged, however, as a promising tool in screening for underlying biochemical abnormalities and managing treatment and as a means of investigating possible novel biomarkers for the disorder. Here, we review the principles and methodology behind untargeted metabolomics, recent pilot studies utilizing this technology, and areas in which it may be integrated into the care of children with this disorder in the future.

MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction

The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have been linked to a number of significant diseases. Additionally, revolutions in sequencing technology have led to a rapid increase in publicly-available sequencing data. Consequently, there have been growing efforts to predict disease status from metagenomic sequencing data, with a proliferation of new approaches in the last few years. Some of these efforts have explored utilizing a powerful form of machine learning called deep learning, which has been applied successfully in several biological domains. Here, we review some of these methods and the algorithms that they are based on, with a particular focus on deep learning methods. We also perform a deeper analysis of Type 2 Diabetes and obesity datasets that have eluded improved results, using a variety of machine learning and feature extraction methods. We conclude by offering perspectives on study design considerations that may impact results and future directions the field can take to improve results and offer more valuable conclusions. The scripts and extracted features for the analyses conducted in this paper are available via GitHub:https://github.com/nlapier2/metapheno.

Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences

Microbial community metabolomics, particularly in the human gut, are beginning to provide a new route to identify functions and ecology disrupted in disease. However, these data can be costly and difficult to obtain at scale, while amplicon or shotgun metagenomic sequencing data are readily available for populations of many thousands. Here, we describe a computational approach to predict potentially unobserved metabolites in new microbial communities, given a model trained on paired metabolomes and metagenomes from the environment of interest. Focusing on two independent human gut microbiome datasets, we demonstrate that our framework successfully recovers community metabolic trends for more than 50% of associated metabolites. Similar accuracy is maintained using amplicon profiles of coral-associated, murine gut, and human vaginal microbiomes. We also provide an expected performance score to guide application of the model in new samples. Our results thus demonstrate that this 'predictive metabolomic' approach can aid in experimental design and provide useful insights into the thousands of community profiles for which only metagenomes are currently available.

Current understanding of the human microbiome

Our understanding of the link between the human microbiome and disease, including obesity, inflammatory bowel disease, arthritis and autism, is rapidly expanding. Improvements in the throughput and accuracy of DNA sequencing of the genomes of microbial communities associated with human samples, complemented by analysis of transcriptomes, proteomes, metabolomes and immunomes, and mechanistic experiments in model systems, have vastly improved our ability to understand the structure and function of the microbiome in both diseased and healthy states. However, many challenges remain. In this Review, we focus on studies in humans to describe these challenges, and propose strategies that leverage existing knowledge to move rapidly from correlation to causation, and ultimately to translation.

Fecal microbiota transplantation donation: the gift that keeps on giving

Fecal microbiota transplantation (FMT) is being studied and utilized for various medical conditions including Clostridium difficile colitis, inflammatory bowel diseases (IBD), obesity, myasthenia gravis, and so on. Yet, FMT donation, whether from an individual or a stool bank, can be challenging given the numerous requirements and donor costs. Furthermore, data outcomes on recipients of FMT regarding donor's health co-morbidities, age, and weight are limited but emerging. The purpose of this review is to evaluate cost, safety, and accessibility in FMT donation.

Leveraging Human Microbiome Features to Diagnose and Stratify Children with Irritable Bowel Syndrome

Accurate diagnosis and stratification of children with irritable bowel syndrome (IBS) remain challenging. Given the central role of recurrent abdominal pain in IBS, we evaluated the relationships of pediatric IBS and abdominal pain with intestinal microbes and fecal metabolites using a comprehensive clinical characterization and multiomics strategy. Using rigorous clinical phenotyping, we identified preadolescent children (aged 7 to 12 years) with Rome III IBS (n = 23) and healthy controls (n = 22) and characterized their fecal microbial communities using whole-genome shotgun metagenomics and global unbiased fecal metabolomic profiling. Correlation-based approaches and machine learning algorithms identified associations between microbes, metabolites, and abdominal pain. IBS cases differed from controls with respect to key bacterial taxa (eg, Flavonifractor plautii and Lachnospiraceae bacterium 7_1_58FAA), metagenomic functions (eg, carbohydrate metabolism and amino acid metabolism), and higher-order metabolites (eg, secondary bile acids, sterols, and steroid-like compounds). Significant associations between abdominal pain frequency and severity and intestinal microbial features were identified. A random forest classifier built on metagenomic and metabolic markers successfully distinguished IBS cases from controls (area under the curve, 0.93). Leveraging multiple lines of evidence, intestinal microbes, genes/pathways, and metabolites were associated with IBS, and these features were capable of distinguishing children with IBS from healthy children. These multi-omics features, and their links to childhood IBS coupled with nutritional interventions, may lead to new microbiome-guided diagnostic and therapeutic strategies.

Oxidative stress, DNA damage, and mutagenicity induced by the extractable organic matter of airborne particulates on bacterial models

The biological activity induced by the extractable organic matter (EOM) of size-segregated airborne Particulate Matter (PM) from two urban sites, urban traffic (UT) and urban background (UB), was assessed by using bacterial assays. The Gram-negative Escherichia coli (E. coli) coliform bacterium was used to measure the intracellular formation of Reactive Oxygen Species (ROS) by employing the Nitroblue tetrazolium (NBT) reduction assay and the lipid peroxidation by malondialdehyde (MDA) measurement. To the best of our knowledge, this is the first study using E. coli for assessing the bioactivity of ambient air in term of oxidative mechanism studies. E. coli BL21 cells were further used for DNA damage assessment by employing the reporter (β-galactosidase) gene expression assay. The bacterial strain S. typhimurium TA100 was used to assess the mutagenic potential of PM by employing the well-known mutation assay (Ames test). Four PM size fractions were assessed for bioactivity, specifically the quasi-ultrafine mode (<0.49 μm), the upper accumulation mode (0.49-0.97 μm), the upper fine mode (0.97-3 μm), and the coarse mode (>3.0 μm). The EOM of each PM sample included three organic fractions of successively increased polarity: the non-polar organic fraction (NPOF), the moderately polar organic fraction (MPOF), and the polar organic fraction (POF). The toxicological endpoints induced by each organic fraction were correlated with the concentrations of various organic chemical components determined in previous studies in an attempt to identify the chemical classes involved.

A decrease in iron availability to human gut microbiome reduces the growth of potentially pathogenic gut bacteria; an in vitro colonic fermentation study

Iron supplements are widely consumed; however most of the iron is not absorbed and enters the colon where potentially pathogenic bacteria can utilise it for growth. This study investigated the effect of iron availability on human gut microbial composition and function using an in vitro colonic fermentation model inoculated with faecal microbiota from healthy adult donors, as well as examining the effect of iron on the growth of individual gut bacteria_. Batch fermenters were seeded with fresh faecal material and supplemented with the iron chelator, bathophenanthroline disulphonic acid (BPDS). Samples were analysed at regular intervals to assess impact on the gut bacterial communities. The growth of Escherichia coli and Salmonella typhimurium was significantly impaired when cultured independently in iron-deficient media. In contrast, depletion of iron did not affect the growth of the beneficial species, Lactobacillus rhamnosus, when cultured independently. Analysis of the microbiome composition via 16S-based metataxonomics indicated that under conditions of iron chelation, the relative abundance decreased for several taxa, including a 10% decrease in Escherichia and a 15% decrease in Bifidobacterium. Metabolomics analysis using ¹ H-NMR indicated that the production of SCFAs was reduced under iron-limited conditions. These results support previous studies demonstrating the essentiality of iron for microbial growth and metabolism, but, in addition, they indicate that iron chelation changes the gut microbiota profile and influences human gut microbial homeostasis through both compositional and functional changes.

Methods for Studying Gut Microbiota: A Primer for Physicians

Human gastrointestinal tract contains a large variety of microbes, in particular bacteria. Studies in recent years have strongly suggested a role for these microbes, collectively referred to as gut microbiota, in the maintenance of homeostasis during health. In addition, alterations in gut microbiota have been reported in several diseases, including those related to the gastrointestinal tract and several systemic conditions, and are believed to play a pathogenetic role in at least some of these. Given the close association between the human gut and liver, the association with gut microbiota appears to be particularly strong for a wide variety of liver diseases. This piece, aimed primarily at physicians, reviews in brief the methods used to study gut microbiota, with particular emphasis on those that use sequences of bacterial 16S rRNA gene or its components.

Taxonomic classification for microbiome analysis, which correlates well with the metabolite milieu of the gut

BACKGROUND

16S rRNA gene amplicon sequencing analysis (16S amplicon sequencing) has provided considerable information regarding the ecology of the intestinal microbiome. Recently, metabolomics has been used for investigating the crosstalk between the intestinal microbiome and the host via metabolites. In the present study, we determined the accuracy with which 16S rRNA gene data at different classification levels correspond to the metabolome data for an in-depth understanding of the intestinal environment.

RESULTS

Over 200 metabolites were identified using capillary electrophoresis and time-of-flight mass spectrometry (CE-TOFMS)-based metabolomics in the feces of antibiotic-treated and untreated mice. 16S amplicon sequencing, followed by principal component analysis (PCA) of the intestinal microbiome at each taxonomic rank, revealed differences between the antibiotic-treated and untreated groups in the first principal component in the family-, genus, and species-level analyses. These differences were similar to those observed in the PCA of the metabolome. Furthermore, a strong correlation between principal component (PC) scores of the metabolome and microbiome was observed in family-, genus-, and species-level analyses.

CONCLUSIONS

Lower taxonomic ranks such as family, genus, or species are preferable for 16S amplicon sequencing to investigate the correlation between the microbiome and metabolome. The correlation of PC scores between the microbiome and metabolome at lower taxonomic levels yield a simple method of integrating different "-omics" data, which provides insights regarding crosstalk between the intestinal microbiome and the host.

Altered Gut Microbiota and Compositional Changes in Firmicutes and Proteobacteria in Mexican Undernourished and Obese Children

Mexico is experiencing an epidemiological and nutritional transition period, and Mexican children are often affected by the double burden of malnutrition, which includes undernutrition (13.6% of children) and obesity (15.3%). The gut microbiome is a complex and metabolically active community of organisms that influences the host phenotype. Although previous studies have shown alterations in the gut microbiota in undernourished children, the affected bacterial communities remain unknown. The present study investigated and compared the bacterial richness and diversity of the fecal microbiota in groups of undernourished (n = 12), obese (n = 12), and normalweight (control) (n = 12) Mexican school-age children. We used next-generation sequencing to analyze the V3–V4 region of the bacterial 16S rRNA gene, and we also investigated whether there were correlations between diet and relevant bacteria. The undernourished and obese groups showed lower bacterial richness and diversity than the normalweight group. Enterotype 1 correlated positively with dietary fat intake in the obese group and with carbohydrate intake in the undernourished group. The results showed that undernourished children had significantly higher levels of bacteria in the Firmicutes phylum and in the Lachnospiraceae family than obese children, while the Proteobacteria phylum was overrepresented in the obese group. The level of Lachnospiraceae correlated negatively with energy consumption and positively with leptin level. This is the first study to examine the gut microbial community structure in undernourished and obese Mexican children living in low-income neighborhoods. Our analysis revealed distinct taxonomic profiles for undernourished and obese children.