Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences

Specific microbiome profile in Takayasu's arteritis and giant cell arteritis

Recent studies have provided evidence of a close link between specific microbiota and inflammatory disorders. While the vessel wall microbiota has been recently described in large vessel vasculitis (LVV) and controls, the blood microbiome in these diseases has not been previously reported (LVV). We aimed to analyse the blood microbiome profile of LVV patients (Takayasu’s arteritis [TAK], giant cell arteritis [GCA]) and healthy blood donors (HD). We studied the blood samples of 13 patients with TAK (20 samples), 9 patients with GCA (11 samples) and 15 HD patients. We assessed the blood microbiome profile by sequencing the 16S rDNA blood bacterial DNA. We used linear discriminant analysis (LDA) coupled with linear discriminant effect size measurement (LEfSe) to investigate the differences in the blood microbiome profile between TAK and GCA patients. An increase in the levels of Clostridia, Cytophagia and Deltaproteobacteria and a decrease in Bacilli at the class level were found in TAK patients compared with HD patients (LDA > 2, p < 0.05). Active TAK patients had significantly lower levels of Staphylococcus compared with inactive TAK patients. Samples of GCA patients had an increased abundance of Rhodococcus and an unidentified member of the Cytophagaceae family. Microbiota of TAK compared with GCA patients was found to show higher levels of Candidatus Aquiluna and Cloacibacterium (LDA > 2; p < 0.05). Differences highlighted in the blood microbiome were also associated with a shift of bacterial predicted metabolic functions in TAK in comparison with HD. Similar results were also found in patients with active versus inactive TAK. In conclusion, patients with TAK were found to present a specific blood microbiome profile in comparison with healthy donors and GCA subjects. Significant changes in the blood microbiome profiles of TAK patients were associated with specific metabolic functions.

Linking Plant Secondary Metabolites and Plant Microbiomes: A Review

Plant secondary metabolites (PSMs) play many roles including defense against pathogens, pests, and herbivores; response to environmental stresses, and mediating organismal interactions. Similarly, plant microbiomes participate in many of the above-mentioned processes directly or indirectly by regulating plant metabolism. Studies have shown that plants can influence their microbiome by secreting various metabolites and, in turn, the microbiome may also impact the metabolome of the host plant. However, not much is known about the communications between the interacting partners to impact their phenotypic changes. In this article, we review the patterns and potential underlying mechanisms of interactions between PSMs and plant microbiomes. We describe the recent developments in analytical approaches and methods in this field. The applications of these new methods and approaches have increased our understanding of the relationships between PSMs and plant microbiomes. Though the current studies have primarily focused on model organisms, the methods and results obtained so far should help future studies of agriculturally important plants and facilitate the development of methods to manipulate PSMs-microbiome interactions with predictive outcomes for sustainable crop productions.

Designing bugs as drugs: exploiting the gut microbiome

The extensive investigation of the human microbiome and the accumulating evidence regarding its critical relationship to human health and disease has advanced recognition of its potential as the next frontier of drug development. The rapid development of technologies, directed at understanding the compositional and functional dynamics of the human microbiome, and the ability to mine for novel therapeutic targets and biomarkers are leading innovative efforts to develop microbe-derived drugs that can prevent and treat autoimmune, metabolic, and infectious diseases. Increasingly, academics, biotechs, investors, and large pharmaceutical companies are partnering to collectively advance various therapeutic modalities ranging from live bacteria to small molecules. We review the leading platforms in current development focusing on live microbial consortia, engineered microbes, and microbial-derived metabolites. We will also touch on how the field is addressing and challenging the traditional definitions of pharmacokinetics and pharmacodynamics, dosing, toxicity, and safety to advance the development of these novel and cutting-edge therapeutics into the clinic.

Whole genome strategies and bioremediation insight into dehalogenase-producing bacteria

An integral approach to decoding both culturable and uncultured microorganisms' metabolic activity involves the whole genome sequencing (WGS) of individual/complex microbial communities. WGS of culturable microbes, amplicon sequencing, metagenomics, and single-cell genome analysis are selective techniques integrating genetic information and biochemical mechanisms. These approaches transform microbial biotechnology into a quick and high-throughput culture-independent evaluation and exploit pollutant-degrading microbes. They are windows into enzyme regulatory bioremediation pathways (i.e., dehalogenase) and the complete bioremediation process of organohalide pollutants. While the genome sequencing technique is gaining the scientific community's interest, it is still in its infancy in the field of pollutant bioremediation. The techniques are becoming increasingly helpful in unraveling and predicting the enzyme structure and explore metabolic and biodegradation capabilities.

Australia IBD Microbiome (AIM) Study: protocol for a multicentre longitudinal prospective cohort study

INTRODUCTION

Crohn's disease and ulcerative colitis are common chronic idiopathic inflammatory bowel diseases (IBD), which cause considerable morbidity. Although the precise mechanisms of disease remain unclear, evidence implicates a strong multidirectional interplay between diet, environmental factors, genetic determinants/immune perturbations and the gut microbiota. IBD can be brought into remission using a number of medications, which act by suppressing the immune response. However, none of the available medications address any of the underlying potential mechanisms. As we understand more about how the microbiota drives inflammation, much interest has focused on identifying microbial signals/triggers in the search for effective therapeutic targets. We describe the establishment of the Australian IBD Microbiota (AIM) Study, Australia's first longitudinal IBD bioresource, which will identify and correlate longitudinal microbial and metagenomics signals to disease activity as evaluated by validated clinical instruments, patient-reported surveys, as well as biomarkers. The AIM Study will also gather extensive demographic, clinical, lifestyle and dietary data known to influence microbial composition in order to generate a more complete understanding of the interplay between patients with IBD and their microbiota.

METHODS

The AIM Study is an Australian multicentre longitudinal prospective cohort study, which will enrol 1000 participants; 500 patients with IBD and 500 healthy controls over a 5-year period. Assessment occurs at 3 monthly intervals over a 24-month period. At each assessment oral and faecal samples are self-collected along with patient-reported outcome measures, with clinical data also collected at baseline, 12 and 24 months. Intestinal tissue will be sampled whenever a colonoscopy is performed. Dietary intake, general health and psychological state will be assessed using validated self-report questionnaires. Samples will undergo metagenomic, transcriptomic, proteomic, metabolomic and culturomic analyses. Omics data will be integrated with clinical data to identify predictive biomarkers of response to therapy, disease behaviour and environmental factors in patients with IBD.

ETHICS AND DISSEMINATION

Ethical approval for this study has been obtained from the South Eastern Sydney Local Health District Research Ethics Committee (HREC 2019/ETH11443). Findings will be reported at national and international gastroenterology meetings and published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

ACTRN12619000911190.

Recent Advanced Technologies for the Characterization of Xenobiotic-Degrading Microorganisms and Microbial Communities

Global environmental contamination with a complex mixture of xenobiotics has become a major environmental issue worldwide. Many xenobiotic compounds severely impact the environment due to their high toxicity, prolonged persistence, and limited biodegradability. Microbial-assisted degradation of xenobiotic compounds is considered to be the most effective and beneficial approach. Microorganisms have remarkable catabolic potential, with genes, enzymes, and degradation pathways implicated in the process of biodegradation. A number of microbes, including Alcaligenes, Cellulosimicrobium, Microbacterium, Micrococcus, Methanospirillum, Aeromonas, Sphingobium, Flavobacterium, Rhodococcus, Aspergillus, Penecillium, Trichoderma, Streptomyces, Rhodotorula, Candida, and Aureobasidium, have been isolated and characterized, and have shown exceptional biodegradation potential for a variety of xenobiotic contaminants from soil/water environments. Microorganisms potentially utilize xenobiotic contaminants as carbon or nitrogen sources to sustain their growth and metabolic activities. Diverse microbial populations survive in harsh contaminated environments, exhibiting a significant biodegradation potential to degrade and transform pollutants. However, the study of such microbial populations requires a more advanced and multifaceted approach. Currently, multiple advanced approaches, including metagenomics, proteomics, transcriptomics, and metabolomics, are successfully employed for the characterization of pollutant-degrading microorganisms, their metabolic machinery, novel proteins, and catabolic genes involved in the degradation process. These technologies are highly sophisticated, and efficient for obtaining information about the genetic diversity and community structures of microorganisms. Advanced molecular technologies used for the characterization of complex microbial communities give an in-depth understanding of their structural and functional aspects, and help to resolve issues related to the biodegradation potential of microorganisms. This review article discusses the biodegradation potential of microorganisms and provides insights into recent advances and omics approaches employed for the specific characterization of xenobiotic-degrading microorganisms from contaminated environments.

Probiotic consumption relieved human stress and anxiety symptoms possibly via modulating the neuroactive potential of the gut microbiota

Stress has been shown to disturb the balance of human intestinal microbiota and subsequently causes mental health problems like anxiety and depression. Our previous study showed that ingesting the probiotic strain, Lactobacillus (L.) plantarum P-8, for 12 weeks could alleviate stress and anxiety of stressed adults. The current study was a follow-up work aiming to investigate the functional role of the gut metagenomes in the observed beneficial effects. The fecal metagenomes of the probiotic (n = 43) and placebo (n = 36) receivers were analyzed in depth. The gut microbiomes of the placebo group at weeks 0 and 12 showed a significantly greater Aitchison distance (P < 0.001) compared with the probiotic group. Meanwhile, the Shannon diversity index of the placebo group (P < 0.05) but not the probiotic group decreased significantly at week 12. Additionally, significantly more species-level genome bins (SGBs) of Bifidobacterium adolescentis, Bifidobacterium longum, and Fecalibacterium prausnitzii (P < 0.01) were identified in the fecal metagenomes of the probiotic group, while the abundances of SGBs representing the species Roseburia faecis and Fusicatenibacter saccharivorans decreased significantly (P < 0.05). Furthermore, the 12-week probiotic supplementation enhanced the diversity of neurotransmitter-synthesizing/consuming SGBs and the levels of some predicted microbial neuroactive metabolites (e.g., short-chain fatty acids, gamma-aminobutyric acid, arachidonic acid, and sphingomyelin). Our results showed a potential link between probiotic-induced gut microbiota modulation and stress/anxiety alleviation in stressed adults, supporting that the gut-brain axis was involved in relieving stress-related symptoms. The beneficial effect relied not only on microbial diversity changes but more importantly gut metagenome modulations at the SGB and functional gene levels.

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Stress and anxiety are related to gut dysbiosis.

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Ingesting Lactobacillus plantarum P-8 (P-8) improved stress/anxiety symptoms.

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Symptoms were relieved by modulating gut microbiota and neuroactive potential.

The role of faecal microbiota transplantation: looking beyond Clostridioides difficile infection

Faecal microbiota transplantation (FMT) is the transfer of screened and minimally processed faecal material from a 'healthy' donor to 'diseased' recipient. It has an established role, and is recommended as a therapeutic strategy, in the management of recurrent Clostridioides difficile infection (CDI). Recognition that gut dysbiosis is associated with, and may contribute to, numerous disease states has led to interest in exploiting FMT to 'correct' this microbial imbalance. Conditions for which it is proposed to be beneficial include inflammatory bowel disease, irritable bowel syndrome, liver disease and hepatic encephalopathy, neuropsychiatric conditions such as depression and anxiety, systemic inflammatory states like sepsis, and even coronavirus disease 2019. To understand what role, if any, FMT may play in the management of these conditions, it is important to consider the potential risks and benefits of the therapy. Regardless, there are several barriers to its more widespread adoption, which include incompletely understood mechanism of action (especially outside of CDI), inability to standardise treatment, disagreement on its active ingredients and how it should be regulated, and lack of long-term outcome and safety data. Whilst the transfer of faecal material from one individual to another to treat ailments or improve health has a history dating back thousands of years, there are fewer than 10 randomised controlled trials supporting its use. Moving forward, it will be imperative to gather as much data from FMT donors and recipients over as long a timeframe as possible, and for trials to be conducted with rigorous methodology, including appropriate control groups, in order to best understand the utility of FMT for indications beyond CDI. This review discusses the history of FMT, its appreciable mechanisms of action with reference to CDI, indications for FMT with an emerging evidence base above and beyond CDI, and future perspectives on the field.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

Experimental and computational approaches to unravel microbial community assembly

Microbial communities have a preponderant role in the life support processes of our common home planet Earth. These extremely diverse communities drive global biogeochemical cycles, and develop intimate relationships with most multicellular organisms, with a significant impact on their fitness. Our understanding of their composition and function has enjoyed a significant thrust during the last decade thanks to the rise of high-throughput sequencing technologies. Intriguingly, the diversity patterns observed in nature point to the possible existence of fundamental community assembly rules. Unfortunately, these rules are still poorly understood, despite the fact that their knowledge could spur a scientific, technological, and economic revolution, impacting, for instance, agricultural, environmental, and health-related practices. In this minireview, I recapitulate the most important wet lab techniques and computational approaches currently employed in the study of microbial community assembly, and briefly discuss various experimental designs. Most of these approaches and considerations are also relevant to the study of microbial microevolution, as it has been shown that it can occur in ecological relevant timescales. Moreover, I provide a succinct review of various recent studies, chosen based on the diversity of ecological concepts addressed, experimental designs, and choice of wet lab and computational techniques. This piece aims to serve as a primer to those new to the field, as well as a source of new ideas to the more experienced researchers.

Functional Deficits in Gut Microbiome of Young and Middle-Aged Adults with Prediabetes Apparent in Metabolizing Bioactive (Poly)phenols

Background: Gut microbiota metabolize select dietary (poly)phenols to absorbable metabolites that exert biological effects important in metabolic health. Microbiota composition associated with health/disease status may affect its functional capacity to yield bioactive metabolites from dietary sources. Therefore, this study assessed gut microbiome composition and its related functional capacity to metabolize fruit (poly)phenols in individuals with prediabetes and insulin resistance (PreDM-IR, n = 26) compared to a metabolically healthy Reference group (n = 10). Methods: Shotgun sequencing was used to characterize gut microbiome composition. Targeted quantitative metabolomic analyses of plasma and urine collected over 24 h were used to assess microbial-derived metabolites in response to a (poly)phenol-rich raspberry test drink. Results: PreDM-IR compared to the Reference group: (1) enriched Blautia obeum and Blautia wexlerae and depleted Bacteroides dorei and Coprococcus eutactus. Akkermansia muciniphila and Bacteroides spp. were depleted in the lean PreDM-IR subset; and (2) impaired microbial catabolism of select (poly)phenols resulting in lower 3,8-dihydroxy-urolithin (urolithin A), phenyl-γ-valerolactones and various phenolic acids concentrations (p < 0.05). Controlling for obesity revealed relationships with microbial species that may serve as metagenomic markers of diabetes development and therapeutic targets. Conclusions: Data provide insight from multi-omics approaches to advance knowledge at the diet–gut–disease nexus serving as a platform for devising dietary strategies to improve metabolic health.

Partners for life: building microbial consortia for the future

New technologies have allowed researchers to better design, build, and analyze complex consortia. These developments are fueling a wider implementation of consortium-based bioprocessing by leveraging synthetic biology, delivering on the field's multitudinous promises of higher efficiencies, superior resiliency, augmented capabilities, and modular bioprocessing. Here we chronicle current progress by presenting a range of screening, computational, and biomolecular tools enabling robust population control, efficient division of labor, and programmatic spatial organization; furthermore, we detail corresponding advancements in areas including machine learning, biocontainment, and standardization. Additionally, we show applications in myriad sectors, including medicine, energy and waste sustainability, chemical production, agriculture, and biosensors. Concluding remarks outline areas of growth that will promote the utilization of complex community structures across the biotechnology spectrum.

Chemical Profiling Provides Insights into the Metabolic Machinery of Hydrocarbon-Degrading Deep-Sea Microbes

Marine microbes are known to degrade hydrocarbons; however, microbes inhabiting deep-sea sediments remain largely unexplored. Previous studies into the classical pathways of marine microbial metabolism reveal diverse chemistries; however, metabolic profiling of marine microbes cultured with hydrocarbons is limited. In this study, taxonomic (amplicon sequencing) profiles of two environmental deep-sea sediments (>1,200 m deep) were obtained, along with taxonomic and metabolomic (mass spectrometry-based metabolomics) profiles of microbes harbored in deep-sea sediments cultured with hydrocarbons as the sole energy source. Samples were collected from the Gulf of México (GM) and cultured for 28 days using simple (toluene, benzene, hexadecane, and naphthalene) and complex (petroleum API 40) hydrocarbon mixtures as the sole energy sources. The sediment samples harbored diverse microbial communities predominantly classified into Woeseiaceae and Kiloniellaceae families, whereas Pseudomonadaceae and Enterobacteriaceae families prevailed after sediments were cultured with hydrocarbons. Chemical profiling of microbial metabolomes revealed diverse chemical groups belonging primarily to the lipids and lipid-like molecules superclass, as well as the organoheterocyclic compound superclass (ClassyFire annotation). Metabolomic data and prediction of functional profiles indicated an increase in aromatic and alkane degradation in samples cultured with hydrocarbons. Previously unreported metabolites, identified as intermediates in the degradation of hydrocarbons, were annotated as hydroxylated polyunsaturated fatty acids and carboxylated benzene derivatives. In summary, this study used mass spectrometry-based metabolomics coupled to chemoinformatics to demonstrate how microbes from deep-sea sediments could be cultured in the presence of hydrocarbons. This study also highlights how this experimental approach can be used to increase the understanding of hydrocarbon degradation by deep-sea sediment microbes.IMPORTANCE High-throughput technologies and emerging informatics tools have significantly advanced knowledge of hydrocarbon metabolism by marine microbes. However, research into microbes inhabiting deep-sea sediments (>1,000 m) is limited compared to those found in shallow waters. In this study, a nontargeted and nonclassical approach was used to examine the diversity of bacterial taxa and the metabolic profiles of hydrocarbon-degrading deep-sea microbes. In conclusion, this study used metabolomics and chemoinformatics to demonstrate that microbes from deep-sea sediment origin thrive in the presence of toxic and difficult-to-metabolize hydrocarbons. Notably, this study provides evidence of previously unreported metabolites and the global chemical repertoire associated with the metabolism of hydrocarbons by deep-sea microbes.

M2IA: a web server for microbiome and metabolome integrative analysis

MOTIVATION

Microbiome-metabolome association studies have experienced exponential growth for an in-depth understanding of the impact of microbiota on human health over the last decade. However, analyzing the resulting multi-omics data and their correlations remains a significant challenge due to the lack of a comprehensive computational tool that can facilitate data integration and interpretation. In this study, an automated microbiome and metabolome integrative analysis pipeline (M2IA) has been developed to meet the urgent needs for tools that can effectively integrate microbiome and metabolome data to derive biological insights.

RESULTS

M2IA streamlines the integrative data analysis between metabolome and microbiome, from data preprocessing, univariate and multivariate statistical analyses, advanced functional analysis for biological interpretation, to a summary report. The functionality of M2IA was demonstrated using TwinsUK cohort datasets consisting of 1116 fecal metabolites and 16s rRNA microbiome from 786 individuals. Moreover, two important metabolic pathways, i.e. benzoate degradation and phosphotransferase system, were identified to be closely associated with obesity.

AVAILABILITY AND IMPLEMENTATION

M2IA is public available at http://m2ia.met-bioinformatics.cn.

CONTACT

yanni617@zju.edu.cn or fjf68@zju.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Alterations of the Gut Microbiome Composition and Lipid Metabolic Profile in Radiation Enteritis

Radiation enteritis (RE) is a common complication in cancer patients receiving radiotherapy. Although studies have shown the changes of this disease at clinical, pathological and other levels, the dynamic characteristics of local microbiome and metabolomics are hitherto unknown. We aimed to examine the multi-omics features of the gut microecosystem, determining the functional correlation between microbiome and lipid metabolites during RE activity. By delivering single high-dose irradiation, a RE mouse model was established. High-throughput 16S rDNA sequencing and global lipidomics analysis were performed to examine microbial and lipidomic profile changes in the gut microecosystem. Spearman correlation analysis was used to determine the functional correlation between bacteria and metabolites. Clinical samples were collected to validate the above observations. During RE activity, the intestinal inflammation of the mice was confirmed by typical signs, symptoms, imaging findings and pathological evidences. 16S datasets revealed that localized irradiation dramatically altered the gut microbial composition, resulting in a decrease ratio of Bacteroidetes to Firmicutes. Lipidomics analysis indicated the remarkable lipidomic profile changes in enteric epithelial barrier, determining that glycerophospholipids metabolism was correlated to RE progression with the highest relevance. Spearman correlation analysis identified that five bacteria-metabolite pairs showed the most significant functional correlation in RE, including Alistipes-PC(36:0e), Bacteroides-DG(18:0/20:4), Dubosiella-PC(35:2), Eggerthellaceae-PC(35:6), and Escherichia-Shigella-TG(18:2/18:2/20:4). These observations were partly confirmed in human specimens. Our study provided a comprehensive description of microbiota dysbiosis and lipid metabolic disorders in RE, suggesting strategies to change local microecosystem to relieve radiation injury and maintain homeostasis.

Multi-Omic Analysis Reveals Different Effects of Sulforaphane on the Microbiome and Metabolome in Old Compared to Young Mice

Dietary factors modulate interactions between the microbiome, metabolome, and immune system. Sulforaphane (SFN) exerts effects on aging, cancer prevention and reducing insulin resistance. This study investigated effects of SFN on the gut microbiome and metabolome in old mouse model compared with young mice. Young (6–8 weeks) and old (21–22 months) male C57BL/6J mice were provided regular rodent chow ± SFN for 2 months. We collected fecal samples before and after SFN administration and profiled the microbiome and metabolome. Multi-omics datasets were analyzed individually and integrated to investigate the relationship between SFN diet, the gut microbiome, and metabolome. The SFN diet restored the gut microbiome in old mice to mimic that in young mice, enriching bacteria known to be associated with an improved intestinal barrier function and the production of anti-inflammatory compounds. The tricarboxylic acid cycle decreased and amino acid metabolism-related pathways increased. Integration of multi-omic datasets revealed SFN diet-induced metabolite biomarkers in old mice associated principally with the genera, Oscillospira, Ruminococcus, and Allobaculum. Collectively, our results support a hypothesis that SFN diet exerts anti-aging effects in part by influencing the gut microbiome and metabolome. Modulating the gut microbiome by SFN may have the potential to promote healthier aging.

Clinically adaptable polymer enables simultaneous spatial analysis of colonic tissues and biofilms

Microbial influences on host cells depend upon the identities of the microbes, their spatial localization, and the responses they invoke on specific host cell populations. Multimodal analyses of both microbes and host cells in a spatially resolved fashion would enable studies into these complex interactions in native tissue environments, potentially in clinical specimens. While techniques to preserve each of the microbial and host cell compartments have been used to examine tissues and microbes separately, we endeavored to develop approaches to simultaneously analyze both compartments. Herein, we established an original method for mucus preservation using Poloxamer 407 (also known as Pluronic F-127), a thermoreversible polymer with mucus-adhesive characteristics. We demonstrate that this approach can preserve spatially-defined compartments of the mucus bi-layer in the colon and the bacterial communities within, compared with their marked absence when tissues were processed with traditional formalin-fixed paraffin-embedded (FFPE) pipelines. Additionally, antigens for antibody staining of host cells were preserved and signal intensity for 16S rRNA fluorescence in situ hybridization (FISH) was enhanced in poloxamer-fixed samples. This in turn enabled us to integrate multimodal analysis using a modified multiplex immunofluorescence (MxIF) protocol. Importantly, we have formulated Poloxamer 407 to polymerize and cross-link at room temperature for use in clinical workflows. These results suggest that the fixative formulation of Poloxamer 407 can be integrated into biospecimen collection pipelines for simultaneous analysis of microbes and host cells.

Microbiome-Based Biomarkers for IBD

Crohn disease and ulcerative colitis are complex immune-mediated diseases that are characterized by a heterogeneity in presentation and clinical course. Although various clinical covariates predict adverse outcomes in these patients, they are insufficiently informative. The gut microbiome likely plays a central role in the pathogenesis of these diseases. Consequently, microbiome-based biomarkers may play an important role in risk stratification and disease prediction. Initial cross-sectional studies showed a reduced gut microbial diversity in patients with Crohn disease or ulcerative colitis, a depletion of phyla with anti-inflammatory effects such as those belonging to Firmicutes, and an increased abundance of potentially pathogenic bacteria in specific disease phenotypes. Subsequent studies longitudinally tracking microbial changes and clinical outcomes have shown dynamic changes correlating with or predictive of disease activity and resistance to therapy. The development of multicenter cohorts using harmonized protocols is essential to robustly validate these biomarkers and facilitate the integration of their evaluation into clinical practice.  .

Metagenomic analysis of the gut microbiome in atherosclerosis patients identify cross-cohort microbial signatures and potential therapeutic target

The gut microbiota is associated with cardiovascular diseases, including atherosclerosis. However, the composition, functional capacity, and metabolites of the gut microbiome about atherosclerosis have not been comprehensively studied. Here, we reanalyzed 25 metagenomic stool samples from Sweden and 385 metagenomic stool samples from China using HUMAnN2, PanPhlAn, and MelonnPan to obtain more sufficient information. We found that the samples from atherosclerotic patients in both cohorts were depleted in Bacteroides xylanisolvens, Odoribacter splanchnicus, Eubacterium eligens, Roseburia inulinivorans, and Roseburia intestinalis. At the functional level, healthy metagenomes were both enriched in pathways of starch degradation V, glycolysis III (from glucose), CDP-diacylglycerol biosynthesis, and folate transformations. R inulinivorans and R intestinalis are major contributors to starch degradation V, while E eligens greatly contribute to the pathway CDP-diacylglycerol biosynthesis, and B xylanisolvens and B uniformis contribute to folate transformations II. The 11 marker species selected from the Chinese cohort distinguish patients from controls with an area under the receiver operating characteristics curve (AUC) of 0.86. Strain-level microbial analysis revealed a geographically associated adaptation of the strains from E eligens, B uniformis, and E coli. Two gut microbial metabolites, nicotinic acid and hydrocinnamic acid, had significantly higher predicted abundance in the control samples compared to the patients in the Chinese cohort, and interestinglynicotinic acid is already an effective lipid-lowering drug to reducing cardiovascular risk. Our results indicate intestinal bacteria such as B xylanisolvens, E eligens, and R inulinivorans could be promising probiotics and potential therapeutic target for atherosclerosis.

A gene prioritization method based on a swine multi-omics knowledgebase and a deep learning model

The analyses of multi-omics data have revealed candidate genes for objective traits. However, they are integrated poorly, especially in non-model organisms, and they pose a great challenge for prioritizing candidate genes for follow-up experimental verification. Here, we present a general convolutional neural network model that integrates multi-omics information to prioritize the candidate genes of objective traits. By applying this model to Sus scrofa, which is a non-model organism, but one of the most important livestock animals, the model precision was 72.9%, recall 73.5%, and F1-Measure 73.4%, demonstrating a good prediction performance compared with previous studies in Arabidopsis thaliana and Oryza sativa. Additionally, to facilitate the use of the model, we present ISwine (http://iswine.iomics.pro/), which is an online comprehensive knowledgebase in which we incorporated almost all the published swine multi-omics data. Overall, the results suggest that the deep learning strategy will greatly facilitate analyses of multi-omics integration in the future.

Yuhua Fu et al. develop a CNN model that integrates multi-omics information to prioritize candidate genes of objective traits. Their model performs well when applied to important livestock non-model animals like Sus scrofa. Finally, the authors present ISwine, an online comprehensive knowledgebase which includes all published swine omics data to facilitate the integration of heterogeneous data.

Metagenomic applications in exploration and development of novel enzymes from nature: a review

BACKGROUND

Microbial community has an essential role in various fields, especially the industrial sector. Microbes produce metabolites in the form of enzymes, which are one of the essential compounds for industrial processes. Unfortunately, there are still numerous microbes that cannot be identified and cultivated because of the limitations of the culture-based method. The metagenomic approach is a solution for researchers to overcome these problems. Metagenomics is a strategy used to analyze the genomes of microbial communities in the environment directly. Metagenomics application used to explore novel enzymes is essential because it allows researchers to obtain data on microbial diversity, reaching of 99% and various types of genes encoding an enzyme that has not yet been identified. Basic methods in metagenomics have been developed and are commonly used in various studies. A basic understanding of metagenomics for researchers is needed, especially young researchers to support the success of the research.

SHORT CONCLUSION

Therefore, this review was done in order to provide a deep understanding of metagenomics. It also discussed the application and basic methods of metagenomics in the exploration of novel enzymes, especially in the latest research. Several types of enzymes, such as cellulases, proteases, and lipases, which have been explored using metagenomics, were reviewed in this article.

Metabolome responses of Enterococcus faecium to acid shock and nitrite stress

Enterococcus faecium is gaining increasing interest due to its virulence and tolerance to a range of stresses (e.g., acid shock and nitrite stress in human stomach). The chemical taxonomy and basic structural features of cellular metabolite can provide us a deeper understanding of bacterial tolerance at molecular level. Here, we used hierarchical classification and molecular composition analysis to investigate the metabolome responses of E. faecium to acid shock and nitrite stress. Our results showed that considerable high biodegradable compounds (e.g., dipeptides) were produced by E. faecium under acid shock, while nitrite stress induced the accumulations of some low biodegradable compounds (e.g., organoheterocyclic compounds and benzenoids). Complete genome analysis and metabolic pathway profiling suggested that E. faecium produced high biodegradable metabolites responsible for the proton-translocation and biofilm formation, which increase its tolerance to acid shock. Yet, the presence of low biodegradable metabolites due to the nitrite exposure could disturb the bacterial productions of surface proteins, and thus inhibiting biofilm formation. Our approach uncovered the hidden interactions between intracellular metabolites and exogenous stress, and will improve the understanding of host-microbe interactions.

What Is Metagenomics Teaching Us, and What Is Missed?

Shotgun metagenomic sequencing has revolutionized our ability to detect and characterize the diversity and function of complex microbial communities. In this review, we highlight the benefits of using metagenomics as well as the breadth of conclusions that can be made using currently available analytical tools, such as greater resolution of species and strains across phyla and functional content, while highlighting challenges of metagenomic data analysis. Major challenges remain in annotating function, given the dearth of functional databases for environmental bacteria compared to model organisms, and the technical difficulties of metagenome assembly and phasing in heterogeneous environmental samples. In the future, improvements and innovation in technology and methodology will lead to lowered costs. Data integration using multiple technological platforms will lead to a better understanding of how to harness metagenomes. Subsequently, we will be able not only to characterize complex microbiomes but also to manipulate communities to achieve prosperous outcomes for health, agriculture, and environmental sustainability.

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.

Housefly (Musca domestica L.) associated microbiota across different life stages

The housefly (Musca domestica L.) lives in close association with its microbiota and its symbionts are suggested to have pivotal roles in processes such as metabolism and immune response, but it is unclear how the profound physiological changes during ontogeny affect the housefly’s associated microbiota and their metabolic capabilities. The present study applies 16S rRNA gene amplicon sequencing to investigate the development of the host-associated microbiota during ontogeny. The potential for microbiota transfer between developmental stages, and the metabolic potential of these microbiota were evaluated. Representatives of Firmicutes were observed as early colonisers during the larval stages, followed by colonisation by organisms affiliating with Proteobacteria and Bacteroidetes as the flies matured into adults. Microbiota observed across all the developmental stages included Lactococcus, Lactobacillus and Enterococcus, while Weissella and Chishuiella were associated with newly hatched larvae and adults, respectively. Predictive metabolic profiling of the identified microorganisms further suggested that the microbiota and their functional profile mature alongside their host and putative host-microbe relationships are established at different stages of development. The predicted metabolic capability of the microbiota developed from primarily simple processes including carbohydrate and nucleotide metabolisms, to more complex metabolic pathways including amino acid metabolisms and processes related to signal transduction.

Computational genomic discovery of diverse gene clusters harbouring Fe-S flavoenzymes in anaerobic gut microbiota

The gut contains an enormous diversity of simple as well as complex molecules from highly diverse food sources, together with host-secreted molecules. This presents a large metabolic opportunity for the gut microbiota, but little is known about how gut microbes are able to catabolize this large chemical diversity. Recently, Fe-S flavoenzymes were found to be key in the transformation of bile acids, catalysing the key step in the 7α-dehydroxylation pathway that allows gut bacteria to transform cholic acid into deoxycholic acid, an exclusively microbe-derived molecule with major implications for human health. While this enzyme family has also been implicated in a limited number of other catalytic transformations, little is known about the extent to which it is of more global importance in gut microbial metabolism. Here, we perform a large-scale computational genomic analysis to show that this enzyme superfamily has undergone a remarkable expansion in Clostridiales, and occurs throughout a diverse array of >1000 different families of putative metabolic gene clusters. Analysis of the enzyme content of these gene clusters suggests that they encode pathways with a wide range of predicted substrate classes, including saccharides, amino acids/peptides and lipids. Altogether, these results indicate a potentially important role of this protein superfamily in the human gut, and our dataset provides significant opportunities for the discovery of novel pathways that may have significant effects on human health.

Linking genomics and metabolomics to chart specialized metabolic diversity

Microbial and plant specialized metabolites constitute an immense chemical diversity, and play key roles in mediating ecological interactions between organisms. Also referred to as natural products, they have been widely applied in medicine, agriculture, cosmetic and food industries. Traditionally, the main discovery strategies have centered around the use of activity-guided fractionation of metabolite extracts. Increasingly, omics data is being used to complement this, as it has the potential to reduce rediscovery rates, guide experimental work towards the most promising metabolites, and identify enzymatic pathways that enable their biosynthetic production. In recent years, genomic and metabolomic analyses of specialized metabolic diversity have been scaled up to study thousands of samples simultaneously. Here, we survey data analysis technologies that facilitate the effective exploration of large genomic and metabolomic datasets, and discuss various emerging strategies to integrate these two types of omics data in order to further accelerate discovery.

Software tools, databases and resources in metabolomics: updates from 2018 to 2019

Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace with data analytics is challenging, to say the least. This review introduces and briefly presents around 100 metabolomics software resources, tools, databases, and other utilities that have surfaced or have improved in 2019. Table 1 provides the computational dependencies of the tools, categorizes the resources based on utility and ease of use, and provides hyperlinks to webpages where the tools can be downloaded or used. This review intends to keep the community of metabolomics researchers up to date with all the software tools, resources, and databases developed in 2019, in one place.

Correlations between Microbiota Bioactivity and Bioavailability of Functional Compounds: A Mini-Review

Numerous studies have demonstrated the role of the microbiota in supporting the physiological functions, owing to its metabolomic component. The presence of biocomponents generally leads to the correction of the microbial pattern correlated with the reduction of oxidative pressure. This study aims to present the main processes that correlate the bioavailability and bioactivity of some functional components through the action of the human microbiota. The use of probiotics and prebiotics is an innovative manner involving alternatives that increase the bioavailability of certain natural or metabolic components has been proposed. Probiotic strains (Saccharomyces cerevisiae or Lactobacillus (L.) plantarum) may represent an intermediary for increasing the antioxidant bioactivity, and they may be administered in the form of a biomass enriched with functional compounds, such as phenolic acids. The limiting effect of gastrointestinal transit is, in several cases, the key to the biopharmaceutical value of new products (or supplements). The identification of newer ways of formulating supplements also involves the compatibility of different types of products, the testing of bioaccessibility, and the elimination of biotransformations.

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.

Metabolomic Analysis of the Liver of a Dextran Sodium Sulfate-Induced Acute Colitis Mouse Model: Implications of the Gut-Liver Connection

The incidence of ulcerative colitis (UC) is increasing worldwide, and it has become a growing problem in Asia. Previous research on UC has focused on serum, plasma, urine, gut tissues, and fecal metabolic profiling, but a comprehensive investigation into the correlation between the severity of colitis and changes in liver metabolism is still lacking. Since the liver and gut exchange nutrients and metabolites through a complex network, intestinal diseases can affect both the liver and other organs. In the present study, concentration-dependent dextran sodium sulfate (DSS)-induced ulcerative colitis was employed to examine changes in liver metabolism using a proton nuclear magnetic resonance spectroscopy (¹H-NMR)-and ultra-performance liquid chromatography time of flight mass spectroscopy (UPLC-TOF MS)-based metabolomics study. Using the multivariate statistical analysis method orthogonal projections to latent structures discriminant analysis (OPLS-DA), changes in metabolites depending on the DSS dose could be clearly distinguished. Specifically, hepatic metabolites involved in one-carbon metabolism, carnitine-related metabolism, and nucleotide synthesis were found to be affected by intestinal inflammation, implying the existence of a metabolic connection between the gut and liver. We are currently investigating the significance of this metabolic condition in UC.

Learning representations of microbe-metabolite interactions

Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.

Beneficial effect of a mixture of vitamin-producing and immune-modulating lactic acid bacteria as adjuvant for therapy in a recurrent mouse colitis model

Inflammatory bowel diseases are chronic and relapsing-remitting disorders that affect the gastrointestinal tract. Previously, the administration of folate and riboflavin-producing lactic acid bacteria (LAB) or an immune-modulating strain showed beneficial effects as they were able to reduce the acute inflammation in mouse models. The aim of this work was to evaluate a mixture of vitamin-producing and immune-modulating LAB administering together with an anti-inflammatory drug during the remission period of a mouse model of recurrent colitis. BALB/c mice were intrarectally instilled with trinitrobenzene sulfonic acid (TNBS) and those who recovered from this acute challenge were given the LAB mixture, mesalazine, or the combination of both (mesalazine + LAB) during 21 days, followed by a second challenge with TNBS. Control mice instilled with ethanol (vehicle of TNBS) and receiving the different treatments were also evaluated in order to study the effect of chronic anti-inflammatory therapy. The combination of mesalazine and LAB mixture was the most effective to decrease the intestinal damage at macroscopic and histological levels and to reduce pro-inflammatory cytokines (IL-6 and TNF-α) in intestinal fluids. In animals instilled with ethanol, mesalazine produced a loss of body weight and intestinal damages with increased IL-6. These side effects were prevented by the co-administration of mesalazine and the LAB mixture. The LAB blend did not affect the primary anti-inflammatory treatment, was able to improve it, and also prevented the side effects of this therapy.

Chronic kidney disease: Biomarker diagnosis to therapeutic targets

Chronic kidney disease (CKD), characterized as renal dysfunction, is recognized as a major public health problem with high morbidity and mortality worldwide. Unfortunately, there are no obvious clinical symptoms in early stage disease until severe damage has occurred. Further complicating early diagnosis and treatment is the lack of sensitive and specific biomarkers. As such, novel biomarkers are urgently needed. Metabolomics has shown an increasing potential for identifying underlying disease mechanisms, facilitating clinical diagnosis and developing pharmaceutical treatments for CKD. Recent advances in metabolomics revealed that CKD was closely associated with the dysregulation of numerous metabolites, such as amino acids, lipids, nucleotides and glycoses, that might be exploited as potential biomarkers. In this review, we summarize recent metabolomic applications based on animal model studies and in patients with CKD and highlight several biomarkers that may play important roles in diagnosis, intervention and development of new therapeutic strategies.

The Integrative Human Microbiome Project

The NIH Human Microbiome Project (HMP) has been carried out over ten years and two phases to provide resources, methods, and discoveries that link interactions between humans and their microbiomes to health-related outcomes. The recently completed second phase, the Integrative Human Microbiome Project, comprised studies of dynamic changes in the microbiome and host under three conditions: pregnancy and preterm birth; inflammatory bowel diseases; and stressors that affect individuals with prediabetes. The associated research begins to elucidate mechanisms of host-microbiome interactions under these conditions, provides unique data resources (at the HMP Data Coordination Center), and represents a paradigm for future multi-omic studies of the human microbiome.

The Integrative Human Microbiome Project

The NIH Human Microbiome Project (HMP) has been carried out over ten years and two phases to provide resources, methods, and discoveries that link interactions between humans and their microbiomes to health-related outcomes. The recently completed second phase, the Integrative Human Microbiome Project, comprised studies of dynamic changes in the microbiome and host under three conditions: pregnancy and preterm birth; inflammatory bowel diseases; and stressors that affect individuals with prediabetes. The associated research begins to elucidate mechanisms of host-microbiome interactions under these conditions, provides unique data resources (at the HMP Data Coordination Center), and represents a paradigm for future multi-omic studies of the human microbiome.