Human Microbiome Project Research Articles

Metagenomic discovery of microbial eukaryotes in stool microbiomes.

Host-associated microbiota form complex microbial communities that are increasingly associated with host behavior and disease. While these microbes include bacterial, archaeal, viral, and eukaryotic constituents, most studies have focused on bacteria due to their dominance in the human host and available tools for investigation. Accumulating evidence suggests microbial eukaryotes in the microbiome play pivotal roles in host health, but our understandings of these interactions are limited to a few readily identifiable taxa because of technical limitations in unbiased eukaryote exploration. Here, we combined cell sorting, optimized eukaryotic cell lysis, and shotgun sequencing to accelerate metagenomic discovery and analysis of host-associated microbial eukaryotes. Using synthetic communities with a 1% microbial eukaryote representation, the eukaryote-optimized cell lysis and DNA recovery method alone yielded a 38-fold increase in eukaryotic DNA. Automated sorting of eukaryotic cells from stool samples of healthy adults increased the number of microbial eukaryote reads in metagenomic pools by up to 28-fold compared to commercial kits. Read frequencies for identified fungi increased by 10,000× on average compared to the Human Microbiome Project and allowed for the identification of novel taxa, de novo assembly of contigs from previously unknown microbial eukaryotes, and gene prediction from recovered genomic segments. These advances pave the way for the unbiased inclusion of microbial eukaryotes in deciphering determinants of health and disease in the host-associated microbiome.IMPORTANCEMicrobial eukaryotes are common constituents of the human gut where they can contribute to local ecology and host health, but they are often overlooked in microbiome studies. The lack of attention is due to current technical limitations that are heavily biased or poorly recovered DNA from microbial eukaryotes. We developed a method to increase the representation of these eukaryotes in metagenomic sequencing of microbiome samples that allows to improve their detection compared to prior methods and allows for the identification of new species. Application of the technique to gut microbiome samples improved detection of fungi, protists, and helminths. New eukaryotic taxa and their encoded genes could be identified by sequencing a small number of samples. This approach can improve the inclusion of eukaryotes into microbiome research.

Comprehensive profile of the companion animal gut microbiome integrating reference-based and reference-free methods.

The gut microbiome of companion animals is relatively underexplored, despite its relevance to animal health, pet owner health, and basic microbial community biology. Here, we provide the most comprehensive analysis of the canine and feline gut microbiomes to date, incorporating 2639 stool shotgun metagenomes (2272 dog and 367 cat) spanning 14 publicly available datasets (n = 730) and 8 new study populations (n = 1909). These are compared with 238 and 112 baseline human gut metagenomes from the Human Microbiome Project 1-II and a traditionally living Malagasy cohort, respectively, processed in a manner identical to the animal metagenomes. All microbiomes were characterized using reference-based taxonomic and functional profiling, as well as de novo assembly yielding metagenomic assembled genomes clustered into species-level genome bins. Companion animals shared 184 species-level genome bins not found in humans, whereas 198 were found in all three hosts. We applied novel methodology to distinguish strains of these shared organisms either transferred or unique to host species, with phylogenetic patterns suggesting host-specific adaptation of microbial lineages. This corresponded with functional divergence of these lineages by host (e.g., differences in metabolic and antibiotic resistance genes) likely important to companion animal health. This study provides the largest resource to date of companion animal gut metagenomes and greatly contributes to our understanding of the "One Health" concept of a shared microbial environment among humans and companion animals, affecting infectious diseases, immune response, and specific genetic elements.

Identification of bio-markers for insulin resistance and sensitivity through multi-omics analysis

Aims: This study aims to identify multi-omics bio-markers for insulin resistance and sensitivity using machine learning approaches on a dataset integrated from several omics. Methods: The study included 362 patients with Insulin Resistance and Insulin Sensitivity from the Integrative Personal Omics Profiling (iPOP) database. Combining the multi-omics data from the Integrative Human Microbiome Project, this study used machine learning to reveal the relationship between insulin resistance and insulin sensitivity. Results: Of 362 patients 186 were insulin resistance and 176 were insulin sensitivity. 11,585 features were used, including clinical features, RNA transcripts, gut microbiota, cytokines, proteins, and metabolomics. We found 21 features capable of distinguishing insulin resistance from insulin sensitivity using a well-known artificial neural network (ANN) method. The model had an area under the receiver operating characteristic (AUC) of 0.97 in the validation dataset and 0.89 in the test dataset. The ANN model’s performance was compared with Random Forest model. Of the 21 new findings, two metabolites (methyl-uric acid and methylxanthine) are xenobiotics, and three RNA transcripts (SERPINF1, SLC2A2, and CHL1). Conclusion: A small number of multi-omics features identified from 11,585 potential candidates for a machine learning model can accurately predict insulin resistance and sensitivity.

Multicohort study testing the generalisability of the SASKit-ML stroke and PDAC prognostic model pipeline to other chronic diseases

ObjectivesTo validate and test the generalisability of the SASKit-ML pipeline, a prepublished feature selection and machine learning pipeline for the prediction of health deterioration after a stroke or pancreatic adenocarcinoma...

Metagenomic functional profiling: to sketch or not to sketch?

Functional profiling of metagenomic samples is essential to decipher the functional capabilities of microbial communities. Traditional and more widely used functional profilers in the context of metagenomics rely on aligning reads against a known reference database. However, aligning sequencing reads against a large and fast-growing database is computationally expensive. In general, k-mer-based sketching techniques have been successfully used in metagenomics to address this bottleneck, notably in taxonomic profiling. In this work, we describe leveraging FracMinHash (implemented in sourmash, a publicly available software), a k-mer-sketching algorithm, to obtain functional profiles of metagenome samples. We show how pieces of the sourmash software (and the resulting FracMinHash sketches) can be put together in a pipeline to functionally profile a metagenomic sample. We named our pipeline fmh-funprofiler. We report that the functional profiles obtained using this pipeline demonstrate comparable completeness and better purity compared to the profiles obtained using other alignment-based methods when applied to simulated metagenomic data. We also report that fmh-funprofiler is 39-99× faster in wall-clock time, and consumes up to 40-55× less memory. Coupled with the KEGG database, this method not only replicates fundamental biological insights but also highlights novel signals from the Human Microbiome Project datasets. This fast and lightweight metagenomic functional profiler is freely available and can be accessed here: https://github.com/KoslickiLab/fmh-funprofiler. All scripts of the analyses we present in this manuscript can be found on GitHub.

The evolving facets of vaginal microbiota transplantation: reinvigorating the unexplored frontier amid complex challenges.

In an age of cutting-edge sequencing methods and worldwide endeavors such as The Human Microbiome Project and MetaHIT, the human microbiome stands as a complex and diverse community of microorganisms. A central theme in current scientific inquiry revolves around reinstating a balanced microbial composition, referred to as "eubiosis," as a targeted approach for treating vast array of diseases. Vaginal Microbiota Transplantation (VMT), inspired by the success of fecal microbiota transplantation, emerges as an innovative therapy addressing vaginal dysbacteriosis by transferring the complete microbiota from a healthy donor. Antibiotics, while effective, pose challenges with adverse effects, high recurrence rates, and potential harm to beneficial Lactobacillus strains. Continued antibiotic usage also sparks worries regarding the development of resistant strains. Probiotics, though showing promise, exhibit inconsistency in treating multifactorial diseases, and concerns linger about their suitability for diverse genetic backgrounds. Given the recurrent challenges associated with antibiotic and probiotic treatments, VMT emerges as an imperative alternative, offering a unique and promising avenue for efficiently and reliably managing vaginal dysbiosis among a majority of women. This review critically evaluates findings from both animal and human studies, offering nuanced insights into the efficacy and challenges of VMT. An extensive analysis of clinical trials, provides a current overview of ongoing and completed trials, shedding light on the evolving clinical landscape and therapeutic potential of VMT. Delving into the origins, mechanisms, and optimized protocols of VMT, the review underscores the imperative for sustained research efforts to advance this groundbreaking gynecological therapy.

Sequence similarity network analysis of drug- and dye-modifying azoreductase enzymes found in the human gut microbiome

Drug metabolism by human gut microbes is often exemplified by azo bond reduction in the anticolitic prodrug sulfasalazine. Azoreductase activity is often found in incubations with cell cultures or ex vivo gut microbiome samples and contributes to the xenobiotic metabolism of drugs and food additives. Applying metagenomic studies to personalized medicine requires knowledge of the genes responsible for sulfasalazine and other drug metabolism, and candidate genes and proteins for drug modifications are understudied. A representative gut-abundant azoreductase from Anaerotignum lactatifermentan DSM 14214 efficiently reduces sulfasalazine and another drug, phenazopyridine, but could not reduce all azo-bonded drugs in this class. We used enzyme kinetics to characterize this enzyme for its NADH-dependent reduction of these drugs and food additives and performed computational docking to provide the groundwork for understanding substrate specificity in this family. We performed an analysis of the Flavodoxin-like fold InterPro family (IPR003680) by computing a sequence similarity network to classify distinct subgroups of the family and then performed chemically-guided functional profiling to identify proteins that are abundant in the NIH Human Microbiome Project dataset. This strategy aims to reduce the number of unique azoreductases needed to characterize one protein family in the diverse set of potential drug- and dye-modifying activities found in the human gut microbiome.

Microbiome Knowledge in the General Community [ID 2683664

INTRODUCTION: The Human Microbiome Project (2007–2016) has contributed to the characterization of the microbiome and its relationship to health and disease processes. This information is only beginning to be fully understood. The aim of this study was to assess the general knowledge about the microbiome in the lay population. METHODS: A five-question survey was administered to the general population who attended Rutgers Day 2023, an informational open house showcasing the activities of Rutgers University. The survey was distributed by our study personnel. RESULTS: A total of 148 useable surveys were collected. We found that 73% of respondents had heard of the microbiome. Respondents were most aware of the gut microbiome (95.2%), followed by skin (51.4%), mouth (48.6%), and nose (45.9%). Respondents recognized microbiome altering factors as follows: dietary effects (89.2%), antibiotics (79.7%), exercise (60.8%), soap (42.6%), and pets (39.2%). Relationship to disease states was recognized for weight (83.1%), diabetes (72.0%), and heart disease (62.8%). Almost 20% of respondents took a probiotic, most often for gut health. However, 8.8% felt the microbiome influenced hair and eye color, and 13.5% thought it was influenced by music. CONCLUSION: This brief survey confirmed that there is a general awareness of the microbiome in the lay population, but a deficit of knowledge in many areas. Despite this, almost one in five respondents used a probiotic. As further microbiome research and its ability to affect disease becomes available, accurate and evidence-based information needs to be distributed to the community.

Bacteroidales-Specific Antimicrobial Genes Can Influence the Selection of the Dominant Fecal Strain of Bacteroides vulgatus and Bacteroides uniformis from the Gastrointestinal Tract Microbial Community.

Bacteroides vulgatus and Bacteroides uniformis are known to be abundant in the human fecal microbial community. Although these strains typically remain stable over time in humans, disruption of this microbial community following antibiotics resulted in the transient change to new strains suggesting that a complex, dynamic strain community exists in humans. To further study the selection of dominant fecal microbial strains from the gastrointestinal tract (GIT) community, we analyzed three longitudinal metagenomic sequencing data sets using BLAST+ to identify genes encoding Bacteroidales-specific antimicrobial proteins (BSAP) that have known functions to restrict species-specific replication of B. uniformis (BSAP-2) or B. vulgatus (BSAP-3) and have been postulated to provide a competitive advantage in microbial communities. In the HMP (Human Microbiome Project) data set, we found fecal samples from individuals had B. vulgatus or B. uniformis with either complete or deleted BSAP genes that did not change over time. We also examined fecal samples from two separate longitudinal data sets of individuals who had been given either single or multiple antibiotics. The BSAP gene pattern from most individuals given either single or multiple antibiotics recovered to be the same as the pre-antibiotic strain. However, in a few individuals, we found incomplete BSAP-3 genes at early times during the recovery that were replaced by B. vulgatus with the complete BSAP-3 gene, consistent with the function of the BSAP to specifically restrict Bacteroides spp. The results of these studies provide insights into the fluxes that occur in the Bacteroides spp. GIT community following perturbation and the dynamics of the selection of a dominant fecal strain of Bacteroides spp.

Integrative analysis with microbial modelling and machine learning uncovers potential alleviators for ulcerative colitis

ABSTRACT Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.

Towards a unified medical microbiome ecology of the OMU for metagenomes and the OTU for microbes

BackgroundMetagenomic sequencing technologies offered unprecedented opportunities and also challenges to microbiology and microbial ecology particularly. The technology has revolutionized the studies of microbes and enabled the high-profile human microbiome and earth microbiome projects. The terminology-change from microbes to microbiomes signals that our capability to count and classify microbes (microbiomes) has achieved the same or similar level as we can for the biomes (macrobiomes) of plants and animals (macrobes). While the traditional investigations of macrobiomes have usually been conducted through naturalists’ (Linnaeus & Darwin) naked eyes, and aerial and satellite images (remote-sensing), the large-scale investigations of microbiomes have been made possible by DNA-sequencing-based metagenomic technologies. Two major types of metagenomic sequencing technologies—amplicon sequencing and whole-genome (shotgun sequencing)—respectively generate two contrastingly different categories of metagenomic reads (data)—OTU (operational taxonomic unit) tables representing microorganisms and OMU (operational metagenomic unit), a new term coined in this article to represent various cluster units of metagenomic genes.ResultsThe ecological science of microbiomes based on the OTU representing microbes has been unified with the classic ecology of macrobes (macrobiomes), but the unification based on OMU representing metagenomes has been rather limited. In a previous series of studies, we have demonstrated the applications of several classic ecological theories (diversity, composition, heterogeneity, and biogeography) to the studies of metagenomes. Here I push the envelope for the unification of OTU and OMU again by demonstrating the applications of metacommunity assembly and ecological networks to the metagenomes of human gut microbiomes. Specifically, the neutral theory of biodiversity (Sloan’s near neutral model), Ning et al.stochasticity framework, core-periphery network, high-salience skeleton network, special trio-motif, and positive-to-negative ratio are applied to analyze the OMU tables from whole-genome sequencing technologies, and demonstrated with seven human gut metagenome datasets from the human microbiome project.ConclusionsAll of the ecological theories demonstrated previously and in this article, including diversity, composition, heterogeneity, stochasticity, and complex network analyses, are equally applicable to OMU metagenomic analyses, just as to OTU analyses. Consequently, I strongly advocate the unification of OTU/OMU (microbiomes) with classic ecology of plants and animals (macrobiomes) in the context of medical ecology.

An in-depth evaluation of metagenomic classifiers for soil microbiomes

BackgroundRecent endeavours in metagenomics, exemplified by projects such as the human microbiome project and TARA Oceans, have illuminated the complexities of microbial biomes. A robust bioinformatic pipeline and meticulous evaluation of their methodology have contributed to the success of these projects. The soil environment, however, with its unique challenges, requires a specialized methodological exploration to maximize microbial insights. A notable limitation in soil microbiome studies is the dearth of soil-specific reference databases available to classifiers that emulate the complexity of soil communities. There is also a lack of in-vitro mock communities derived from soil strains that can be assessed for taxonomic classification accuracy.ResultsIn this study, we generated a custom in-silico mock community containing microbial genomes commonly observed in the soil microbiome. Using this mock community, we simulated shotgun sequencing data to evaluate the performance of three leading metagenomic classifiers: Kraken2 (supplemented with Bracken, using a custom database derived from GTDB-TK genomes along with its own default database), Kaiju, and MetaPhlAn, utilizing their respective default databases for a robust analysis. Our results highlight the importance of optimizing taxonomic classification parameters, database selection, as well as analysing trimmed reads and contigs. Our study showed that classifiers tailored to the specific taxa present in our samples led to fewer errors compared to broader databases including microbial eukaryotes, protozoa, or human genomes, highlighting the effectiveness of targeted taxonomic classification. Notably, an optimal classifier performance was achieved when applying a relative abundance threshold of 0.001% or 0.005%. The Kraken2 supplemented with bracken, with a custom database demonstrated superior precision, sensitivity, F1 score, and overall sequence classification. Using a custom database, this classifier classified 99% of in-silico reads and 58% of real-world soil shotgun reads, with the latter identifying previously overlooked phyla using a custom database.ConclusionThis study underscores the potential advantages of in-silico methodological optimization in metagenomic analyses, especially when deciphering the complexities of soil microbiomes. We demonstrate that the choice of classifier and database significantly impacts microbial taxonomic profiling. Our findings suggest that employing Kraken2 with Bracken, coupled with a custom database of GTDB-TK genomes and fungal genomes at a relative abundance threshold of 0.001% provides optimal accuracy in soil shotgun metagenome analysis.

Update on the gut microbiome in health and diseases.

The Human Microbiome Project, Earth Microbiome Project, and next-generation sequencing have advanced novel genome association, host genetic linkages, and pathogen identification. The microbiome is the sum of the microbes, their genetic information, and their ecological niche. This study will describe how millions of bacteria in the gut affect the human body in health and disease. The gut microbiome changes in relation with age, with an increase in Bacteroidetes and Firmicutes. Host and environmental factors affecting the gut microbiome are diet, drugs, age, smoking, exercise, and host genetics. In addition, changes in the gut microbiome may affect the local gut immune system and systemic immune system. In this study, we discuss how the microbiome may affect the metabolism of healthy subjects or may affect the pathogenesis of metabolism-generating metabolic diseases. Due to the high number of publications on the argument, from a methodologically point of view, we decided to select the best papers published in referred journals in the last 3 years. Then we selected the previously published papers. The major goals of our study were to elucidate which microbiome and by which pathways are related to healthy and disease conditions.

Understanding the factors regulating host-microbiome interactions using Caenorhabditis elegans.

The Human Microbiome Project was a research programme that successfully identified associations between microbial species and healthy or diseased individuals. However, a major challenge identified was the absence of model systems for studying host-microbiome interactions, which would increase our capacity to uncover molecular interactions, understand organ-specificity and discover new microbiome-altering health interventions. Caenorhabditis elegans has been a pioneering model organism for over 70 years but was largely studied in the absence of a microbiome. Recently, ecological sampling of wild nematodes has uncovered a large amount of natural genetic diversity as well as a slew of associated microbiota. The field has now explored the interactions of C. elegans with its associated gut microbiome, a defined and non-random microbial community, highlighting its suitability for dissecting host-microbiome interactions. This core microbiome is being used to study the impact of host genetics, age and stressors on microbiome composition. Furthermore, single microbiome species are being used to dissect molecular interactions between microbes and the animal gut. Being amenable to health altering genetic and non-genetic interventions, C. elegans has emerged as a promising system to generate and test new hypotheses regarding host-microbiome interactions, with the potential to uncover novel paradigms relevant to other systems. This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.

Integrated annotation prioritizes metabolites with bioactivity in inflammatory bowel disease.

Microbial biochemistry is central to the pathophysiology of inflammatory bowel diseases (IBD). Improved knowledge of microbial metabolites and their immunomodulatory roles is thus necessary for diagnosis and management. Here, we systematically analyzed the chemical, ecological, and epidemiological properties of ~82k metabolic features in 546 Integrative Human Microbiome Project (iHMP/HMP2) metabolomes, using a newly developed methodology for bioactive compound prioritization from microbial communities. This suggested >1000 metabolic features as potentially bioactive in IBD and associated ~43% of prevalent, unannotated features with at least one well-characterized metabolite, thereby providing initial information for further characterization of a significant portion of the fecal metabolome. Prioritized features included known IBD-linked chemical families such as bile acids and short-chain fatty acids, and less-explored bilirubin, polyamine, and vitamin derivatives, and other microbial products. One of these, nicotinamide riboside, reduced colitis scores in DSS-treated mice. The method, MACARRoN, is generalizable with the potential to improve microbial community characterization and provide therapeutic candidates.

Improved DNA Extraction and Amplification Strategy for 16S rRNA Gene Amplicon-Based Microbiome Studies.

Next-generation sequencing technology has driven the rapid advancement of human microbiome studies by enabling community-level sequence profiling of microbiomes. Although all microbiome sequencing methods depend on recovering the DNA from a sample as a first critical step, lysis methods can be a major determinant of microbiome profile bias. Gentle enzyme-based DNA preparation methods preserve DNA quality but can bias the results by failing to open difficult-to-lyse bacteria. Mechanical methods like bead beating can also bias DNA recovery because the mechanical energy required to break tougher cell walls may shear the DNA of the more easily lysed microbes, and shearing can vary depending on the time and intensity of beating, influencing reproducibility. We introduce a non-mechanical, non-enzymatic, novel rapid microbial DNA extraction procedure suitable for 16S rRNA gene-based microbiome profiling applications that eliminates bead beating. The simultaneous application of alkaline, heat, and detergent ('Rapid' protocol) to milligram quantity samples provided consistent representation across the population of difficult and easily lysed bacteria equal to or better than existing protocols, producing sufficient high-quality DNA for full-length 16S rRNA gene PCR. The novel 'Rapid' method was evaluated using mock bacterial communities containing both difficult and easily lysed bacteria. Human fecal sample testing compared the novel Rapid method with a standard Human Microbiome Project (HMP) protocol for samples from lung cancer patients and controls. DNA recovered from both methods was analyzed using 16S rRNA gene sequencing of the V1V3 and V4 regions on the Illumina platform and the V1V9 region on the PacBio platform. Our findings indicate that the 'Rapid' protocol consistently yielded higher levels of Firmicutes species, which reflected the profile of the bacterial community structure more accurately, which was confirmed by mock community evaluation. The novel 'Rapid' DNA lysis protocol reduces population bias common to bead beating and enzymatic lysis methods, presenting opportunities for improved microbial community profiling, combined with the reduction in sample input to 10 milligrams or less, and it enables rapid transfer and simultaneous lysis of 96 samples in a standard plate format. This results in a 20-fold reduction in sample handling time and an overall 2-fold time advantage when compared to widely used commercial methods. We conclude that the novel 'Rapid' DNA extraction protocol offers a reliable alternative for preparing fecal specimens for 16S rRNA gene amplicon sequencing.

P429 Explainable AI identified metabolome and diet interactions in the blood and faceal samples

Abstract Background Background: Inflammatory Bowel Disease (IBD) represents a multifaceted condition that can be characterised by the interplay of four primary elements: immunity, genetics, microbiome and environmental factors. Among these, the relationship between nutrition and the metabolome is pivotal and a number of machine learning based approaches have been developed in an effort to gain a better understanding of this relationship. Nevertheless, to date, these approaches suffer from interpretability, thereby restricting their practical applicability within clinical settings.Hence, in this study, we leverage two publicly available datasets, the UK Biobank (UKB) and the Human Microbiome Project Phase II IBD Multi’Omics Database (HMP2), to perform metabolomics-based IBD disease predictions. We apply explainable an AI (XAI) based framework to understand these predictions and explore the diet-metabolite relationships in IBD and non-IBD individuals. Methods 4,137 plasma samples (2,099 IBD, 2,038 non-IBD) from UKB and 546 fecal samples (411 IBD, 135 non-IBD) from HMP2 with complete dietary data were used in the analysis. Random forest classification, optimized by grid search and estimator retraining was then trained to predict disease with Shapley Additive exPlanations (SHAP) included downstream to generate further insight on the model’s predictions. Finally, diet-metabolite associations were examined using Spearman-based correlations. Results From the plasma dataset, inflammatory markers, HDL and LDL cholesterols and omega 3 fatty acids were identified in relationship with IBD diagnosis. Within the feces samples, the discriminatory metabolites identified included B vitamins, phospholipids, urobilin, and taurine, and these latter provided a better predictive power than the plasma derived biomarkers (AUC: fecal 0.90.(95% CI: 0.84-0.96), plasma 0.66 (95% CI: 0.61-0.71). Regarding diet contribution, fish, fruit, vegetables, and processed foods exhibited a strong correlation with metabolites in both datasets, with correlations in fecal samples exhibiting a stong difference between IBD and non-IBD patients. Conclusion While this study has certain limitations, these results suggest that, compared to healthy individuals, IBD patients are characterised by a distinct gut profile primarily influenced by diet. It also suggests that disease, or health, influences how the gut responds to food intake. Future studies should confirm these findings using XAI and analysing both gut and blood matrices in same individuals along with their dietary intake for personalized IBD therapy development.

Exploring the presence of oral bacteria in non-oral sites of patients with cardiovascular diseases using whole metagenomic data

Cardiovascular diseases (CVDs) encompass various conditions affecting the heart and its blood vessels and are often linked with oral microbes. Our data analysis aimed to identify oral bacteria from other non-oral sites (i.e., gut, arterial plaque and cultured blood) that could be linked with CVDs. Taxonomic profiling identified bacteria to the species level and compared with the Human Oral Microbiome Database (HOMD). The oral bacteria in the gut, cultured blood and arterial plaque samples were catalogued, with their average frequency calculated for each sample. Additionally, data were filtered by comparison with the Human Microbiome Project (HMP) database. We identified 17,243 microbial species, of which 410 were present in the HOMD database and further denominated as “oral”, and were found in at least one gut sample, but only 221 and 169 species were identified in the cultured blood and plaque samples, respectively. Of the 410 species, 153 were present solely in oral-associated environments after comparison with the HMP database, irrespective of their presence in other body sites. Our results suggest a potential connection between the presence of specific species of oral bacterial and occurrence of CVDs. Detecting these oral bacterial species in non-oral sites of patients with CVDs could help uncover the link between oral health and general health, including cardiovascular conditions via bacterial translocation.

The impact of antibiotic exposure on antibiotic resistance gene dynamics in the gut microbiota of inflammatory bowel disease patients.

While antibiotics are commonly used to treat inflammatory bowel disease (IBD), their widespread application can disturb the gut microbiota and foster the emergence and spread of antibiotic resistance. However, the dynamic changes to the human gut microbiota and direction of resistance gene transmission under antibiotic effects have not been clearly elucidated. Based on the Human Microbiome Project, a total of 90 fecal samples were collected from 30 IBD patients before, during and after antibiotic treatment. Through the analysis workflow of metagenomics, we described the dynamic process of changes in bacterial communities and resistance genes pre-treatment, during and post-treatment. We explored potential consistent relationships between gut microbiota and resistance genes, and established gene transmission networks among species before and after antibiotic use. Exposure to antibiotics can induce alterations in the composition of the gut microbiota in IBD patients, particularly a reduction in probiotics, which gradually recovers to a new steady state after cessation of antibiotics. Network analyses revealed intra-phylum transfers of resistance genes, predominantly between taxonomically close organisms. Specific resistance genes showed increased prevalence and inter-species mobility after antibiotic cessation. This study demonstrates that antibiotics shape the gut resistome through selective enrichment and promotion of horizontal gene transfer. The findings provide insights into ecological processes governing resistance gene dynamics and dissemination upon antibiotic perturbation of the microbiota. Optimizing antibiotic usage may help limit unintended consequences like increased resistance in gut bacteria during IBD management.

Localisation of nitrate-reducing and highly abundant microbial communities in the oral cavity.

The nitrate (NO3-) reducing bacteria resident in the oral cavity have been implicated as key mediators of nitric oxide (NO) homeostasis and human health. NO3--reducing oral bacteria reduce inorganic dietary NO3- to nitrite (NO2-) via the NO3--NO2--NO pathway. Studies of oral NO3--reducing bacteria have typically sampled from either the tongue surface or saliva. The aim of this study was to assess whether other areas in the mouth could contain a physiologically relevant abundance of NO3- reducing bacteria, which may be important for sampling in clinical studies. The bacterial composition of seven oral sample types from 300 individuals were compared using a meta-analysis of the Human Microbiome Project data. This analysis revealed significant differences in the proportions of 20 well-established oral bacteria and highly abundant NO3--reducing bacteria across each oral site. The genera included Actinomyces, Brevibacillus, Campylobacter, Capnocytophaga, Corynebacterium, Eikenella, Fusobacterium, Granulicatella, Haemophilus, Leptotrichia, Microbacterium, Neisseria, Porphyromonas, Prevotella, Propionibacterium, Rothia, Selenomonas, Staphylococcus, Streptococcus and Veillonella. The highest proportion of NO3--reducing bacteria was observed in saliva, where eight of the bacterial genera were found in higher proportion than on the tongue dorsum, whilst the lowest proportions were found in the hard oral surfaces. Saliva also demonstrated higher intra-individual variability and bacterial diversity. This study provides new information on where samples should be taken in the oral cavity to assess the abundance of NO3--reducing bacteria. Taking saliva samples may benefit physiological studies, as saliva contained the highest abundance of NO3- reducing bacteria and is less invasive than other sampling methods. These results inform future studies coupling oral NO3--reducing bacteria research with physiological outcomes affecting human health.