The oral-gut microbiome axis in health and disease.

The human body hosts trillions of microorganisms throughout many diverse habitats with different physico-chemical characteristics. Among them, the oral cavity and the gut harbour some of the most dense and diverse microbial communities. Although these two sites are physiologically distinct, they are directly connected and can influence each other in several ways. For example, oral microorganisms can reach and colonize the gastrointestinal tract, particularly in the context of gut dysbiosis. However, the mechanisms of colonization and the role that the oral microbiome plays in causing or exacerbating diseases in other organs have not yet been fully elucidated. Here, we describe recent advances in our understanding of how the oral and intestinal microbiota interplay in relation to their impact on human health and disease.

Protocol for a multicentre cross-sectional, longitudinal ambulatory clinical trial in rheumatoid arthritis and Parkinson's disease patients analysing the relation between the gut microbiome, fasting and immune status in Germany (ExpoBiome).

INTRODUCTION: Chronic inflammatory diseases like rheumatoid arthritis (RA) and neurodegenerative disorders like Parkinson's disease (PD) have recently been associated with a decreased diversity in the gut microbiome, emerging as key driver of various diseases. The specific interactions between gut-borne microorganisms and host pathophysiology remain largely unclear. The microbiome can be modulated by interventions comprising nutrition.The aim of our clinical study is to (1) examine effects of prolonged fasting (PF) and time-restricted eating (TRE) on the outcome parameters and the immunophenotypes of RA and PD with (2) special consideration of microbial taxa and molecules associated with changes expected in (1), and (3) identify factors impacting the disease course and treatment by in-depth screening of microorganisms and molecules in personalised HuMiX gut-on-chip models, to identify novel targets for anti-inflammatory therapy. METHODS AND ANALYSIS: This trial is an open-label, multicentre, controlled clinical trial consisting of a cross-sectional and a longitudinal study. A total of 180 patients is recruited. For the cross-sectional study, 60 patients with PD, 60 patients with RA and 60 healthy controls are recruited at two different, specialised clinical sites. For the longitudinal part, 30 patients with PD and 30 patients with RA undergo 5-7 days of PF followed by TRE (16:8) for a period of 12 months. One baseline visit takes place before the PF intervention and 10 follow-up visits will follow over a period of 12 months (April 2021 to November 2023). ETHICS AND DISSEMINATION: Ethical approval was obtained to plan and conduct the trial from the institutional review board of the Charité-Universitätsmedizin Berlin (EA1/204/19), the ethics committee of the state medical association (Landesärztekammer) of Hessen (2021-2230-zvBO) and the Ethics Review Panel (ERP) of the University of Luxembourg (ERP 21-001 A ExpoBiome). The results of this study will be disseminated through peer-reviewed publications, scientific presentations and social media. TRIAL REGISTRATION NUMBER: NCT04847011.

The gut microbiome molecular complex in human health and disease.

The human gut microbiome produces a functional complex of biomolecules, including nucleic acids, (poly)peptides, structural molecules, and metabolites. This impacts human physiology in multiple ways, especially by triggering inflammatory pathways in disease. At present, much remains to be learned about the identity of key effectors and their causal roles.

SnapShot: The Expobiome Map.

The human gut microbiome is intricately connected to health and disease. Microbiome-derived molecules are implicated in many chronic diseases involving inflammation. Herein, we summarize the diverse complex of such immunogenic molecules, including nucleic acids, (poly)peptides, structural molecules, and metabolites. The interactions between this "expobiome" and human immune pathways are specifically illustrated in the context of chronic diseases. To view this SnapShot, open or download the PDF.

Roles of bacteriophages, plasmids and CRISPR immunity in microbial community dynamics revealed using time-series integrated meta-omics.

Viruses and plasmids (invasive mobile genetic elements (iMGEs)) have important roles in shaping microbial communities, but their dynamic interactions with CRISPR-based immunity remain unresolved. We analysed generation-resolved iMGE-host dynamics spanning one and a half years in a microbial consortium from a biological wastewater treatment plant using integrated meta-omics. We identified 31 bacterial metagenome-assembled genomes encoding complete CRISPR-Cas systems and their corresponding iMGEs. CRISPR-targeted plasmids outnumbered their bacteriophage counterparts by at least fivefold, highlighting the importance of CRISPR-mediated defence against plasmids. Linear modelling of our time-series data revealed that the variation in plasmid abundance over time explained more of the observed community dynamics than phages. Community-scale CRISPR-based plasmid-host and phage-host interaction networks revealed an increase in CRISPR-mediated interactions coinciding with a decrease in the dominant 'Candidatus Microthrix parvicella' population. Protospacers were enriched in sequences targeting genes involved in the transmission of iMGEs. Understanding the factors shaping the fitness of specific populations is necessary to devise control strategies for undesirable species and to predict or explain community-wide phenotypes.

Systematic characterization of human gut microbiome-secreted molecules by integrated multi-omics.

The human gut microbiome produces a complex mixture of biomolecules that interact with human physiology and play essential roles in health and disease. Crosstalk between micro-organisms and host cells is enabled by different direct contacts, but also by the export of molecules through secretion systems and extracellular vesicles. The resulting molecular network, comprised of various biomolecular moieties, has so far eluded systematic study. Here we present a methodological framework, optimized for the extraction of the microbiome-derived, extracellular biomolecular complement, including nucleic acids, (poly)peptides, and metabolites, from flash-frozen stool samples of healthy human individuals. Our method allows simultaneous isolation of individual biomolecular fractions from the same original stool sample, followed by specialized omic analyses. The resulting multi-omics data enable coherent data integration for the systematic characterization of this molecular complex. Our results demonstrate the distinctiveness of the different extracellular biomolecular fractions, both in terms of their taxonomic and functional composition. This highlights the challenge of inferring the extracellular biomolecular complement of the gut microbiome based on single-omic data. The developed methodological framework provides the foundation for systematically investigating mechanistic links between microbiome-secreted molecules, including those that are typically vesicle-associated, and their impact on host physiology in health and disease.

Dichloromethane Degradation Pathway from Unsequenced Hyphomicrobium sp. MC8b Rapidly Explored by Pan-Proteomics.

Several bacteria are able to degrade the major industrial solvent dichloromethane (DCM) by using the conserved dehalogenase DcmA, the only system for DCM degradation characterised at the sequence level so far. Using differential proteomics, we rapidly identified key determinants of DCM degradation for Hyphomicrobium sp. MC8b, an unsequenced facultative methylotrophic DCM-degrading strain. For this, we designed a pan-proteomics database comprising the annotated genome sequences of 13 distinct Hyphomicrobium strains. Compared to growth with methanol, growth with DCM induces drastic changes in the proteome of strain MC8b. Dichloromethane dehalogenase DcmA was detected by differential pan-proteomics, but only with poor sequence coverage, suggesting atypical characteristics of the DCM dehalogenation system in this strain. More peptides were assigned to DcmA by error-tolerant search, warranting subsequent sequencing of the genome of strain MC8b, which revealed a highly divergent set of dcm genes in this strain. This suggests that the dcm enzymatic system is less strongly conserved than previously believed, and that substantial molecular evolution of dcm genes has occurred beyond their horizontal transfer in the bacterial domain. Our study showed the power of pan-proteomics for quick characterization of new strains belonging to branches of the Tree of Life that are densely genome-sequenced.

Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance.

The development of reliable, mixed-culture biotechnological processes hinges on understanding how microbial ecosystems respond to disturbances. Here we reveal extensive phenotypic plasticity and niche complementarity in oleaginous microbial populations from a biological wastewater treatment plant. We perform meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics) on in situ samples over 14 months at weekly intervals. Based on 1,364 de novo metagenome-assembled genomes, we uncover four distinct fundamental niche types. Throughout the time-series, we observe a major, transient shift in community structure, coinciding with substrate availability changes. Functional omics data reveals extensive variation in gene expression and substrate usage amongst community members. Ex situ bioreactor experiments confirm that responses occur within five hours of a pulse disturbance, demonstrating rapid adaptation by specific populations. Our results show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity, and set the foundation for future ecological engineering efforts.

Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates.

Emerging antibiotic resistance is a major global health threat. The analysis of nucleic acid sequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistance determinants to inform molecular diagnostics and drug development. We collected genetic data (11,087 newly-sequenced whole genomes) and culture-based resistance profiles (10,991 out of the 11,087 isolates comprehensively tested against 22 antibiotics in total) of clinical isolates including 18 main species spanning a time period of 30 years. Species and drug specific resistance patterns were observed including increased resistance rates for Acinetobacter baumannii to carbapenems and for Escherichia coli to fluoroquinolones. Species-level pan-genomes were constructed to reflect the genetic repertoire of the respective species, including conserved essential genes and known resistance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed to infer gene-drug resistance associations using statistical testing. The isolate collection and the analysis results have been integrated into GEAR-base, a resource available for academic research use free of charge at https://gear-base.com.

Assessing the heterogeneity of in silico plasmid predictions based on whole-genome-sequenced clinical isolates.

High-throughput next-generation shotgun sequencing of pathogenic bacteria is growing in clinical relevance, especially for chromosomal DNA-based taxonomic identification and for antibiotic resistance prediction. Genetic exchange is facilitated for extrachromosomal DNA, e.g. plasmid-borne antibiotic resistance genes. Consequently, accurate identification of plasmids from whole-genome sequencing (WGS) data remains one of the major challenges for sequencing-based precision medicine in infectious diseases. Here, we assess the heterogeneity of four state-of-the-art tools (cBar, PlasmidFinder, plasmidSPAdes and Recycler) for the in silico prediction of plasmid-derived sequences from WGS data. Heterogeneity, sensitivity and precision were evaluated by reference-independent and reference-dependent benchmarking using 846 Gram-negative clinical isolates. Interestingly, the majority of predicted sequences were tool-specific, resulting in a pronounced heterogeneity across tools for the reference-independent assessment. In the reference-dependent assessment, sensitivity and precision values were found to substantially vary between tools and across taxa, with cBar exhibiting the highest median sensitivity (87.45%) but a low median precision (27.05%). Furthermore, integrating the individual tools into an ensemble approach showed increased sensitivity (95.55%) while reducing the precision (25.62%). CBar and plasmidSPAdes exhibited the strongest concordance with respect to identified antibiotic resistance factors. Moreover, false-positive plasmid predictions typically contained only few antibiotic resistance factors. In conclusion, while high degrees of heterogeneity and variation in sensitivity and precision were observed across the different tools and taxa, existing tools are valuable for investigating the plasmid-borne resistome. Nevertheless, additional studies on representative clinical data sets will be necessary to translate in silico plasmid prediction approaches from research to clinical application.

Comparison of initial oral microbiomes of young adults with and without cavitated dentin caries lesions using an in situ biofilm model.

Dental caries is caused by acids released from bacterial biofilms. However, the in vivo formation of initial biofilms in relation to caries remains largely unexplored. The aim of this study was to compare the oral microbiome during the initial phase of bacterial colonization for individuals with (CC) and without (NC) cavitated dentin caries lesions. Bovine enamel slabs on acrylic splints were worn by the volunteers (CC: 14, NC: 13) for in situ biofilm formation (2 h, 4 h, 8 h, 1 ml saliva as reference). Sequencing of the V1/V2 regions of the 16S rRNA gene was performed (MiSeq). The relative abundances of individual operational taxonomic units (OTUs) were compared between samples from the CC group and the NC group. Random forests models were furthermore trained to separate the groups. While the overall heterogeneity did not differ substantially between CC and NC individuals, several individual OTUs were found to have significantly different relative abundances. For the 8 h samples, most of the significant OTUs showed higher relative abundances in the CC group, while the majority of significant OTUs in the saliva samples were more abundant in the NC group. Furthermore, using OTU signatures enabled a separation between both groups, with area-under-the-curve (AUC) values of ~0.8. In summary, the results suggest that initial oral biofilms provide the potential to differentiate between CC and NC individuals.

First draft genome sequence of a strain belonging to the Zoogloea genus and its gene expression in situ.

The Gram-negative beta-proteobacterium Zoogloea sp. LCSB751 (LMG 29444) was newly isolated from foaming activated sludge of a municipal wastewater treatment plant. Here, we describe its draft genome sequence and annotation together with a general physiological and genomic analysis, as the first sequenced representative of the Zoogloea genus. Moreover, Zoogloea sp. gene expression in its environment is described using metatranscriptomic data obtained from the same treatment plant. The presented genomic and transcriptomic information demonstrate a pronounced capacity of this genus to synthesize poly-ß-hydroxyalkanoate within wastewater.

Integrated meta-omic analyses of the gastrointestinal tract microbiome in patients undergoing allogeneic hematopoietic stem cell transplantation.

In patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT), treatment-induced changes to the gastrointestinal tract (GIT) microbiome have been linked to adverse outcomes, most notably graft-versus-host disease (GvHD). However, it is presently unknown whether this relationship is causal or consequential. Here, we performed an integrated meta-omic analysis to probe deeper into the GIT microbiome changes during allo-HSCT and its accompanying treatments. We used 16S and 18S rRNA gene amplicon sequencing to resolve archaea, bacteria, and eukaryotes within the GIT microbiomes of 16 patients undergoing allo-HSCT for the treatment of hematologic malignancies. These results revealed a major shift in the GIT microbiome after allo-HSCT including a marked reduction in bacterial diversity, accompanied by only limited changes in eukaryotes and archaea. An integrated analysis of metagenomic and metatranscriptomic data was performed on samples collected from a patient before and after allo-HSCT for acute myeloid leukemia. This patient developed severe GvHD, leading to death 9 months after allo-HSCT. In addition to drastically decreased bacterial diversity, the post-treatment microbiome showed a higher overall number and higher expression levels of antibiotic resistance genes (ARGs). One specific Escherichia coli strain causing a paravertebral abscess was linked to GIT dysbiosis, suggesting loss of intestinal barrier integrity. The apparent selection for bacteria expressing ARGs suggests that prophylactic antibiotic administration may adversely affect the overall treatment outcome. We therefore assert that such analyses including information about the selection of pathogenic bacteria expressing ARGs may assist clinicians in "personalizing" regimens for individual patients to improve overall outcomes.

BusyBee Web: metagenomic data analysis by bootstrapped supervised binning and annotation.

Metagenomics-based studies of mixed microbial communities are impacting biotechnology, life sciences and medicine. Computational binning of metagenomic data is a powerful approach for the culture-independent recovery of population-resolved genomic sequences, i.e. from individual or closely related, constituent microorganisms. Existing binning solutions often require a priori characterized reference genomes and/or dedicated compute resources. Extending currently available reference-independent binning tools, we developed the BusyBee Web server for the automated deconvolution of metagenomic data into population-level genomic bins using assembled contigs (Illumina) or long reads (Pacific Biosciences, Oxford Nanopore Technologies). A reversible compression step as well as bootstrapped supervised binning enable quick turnaround times. The binning results are represented in interactive 2D scatterplots. Moreover, bin quality estimates, taxonomic annotations and annotations of antibiotic resistance genes are computed and visualized. Ground truth-based benchmarks of BusyBee Web demonstrate comparably high performance to state-of-the-art binning solutions for assembled contigs and markedly improved performance for long reads (median F1 scores: 70.02-95.21%). Furthermore, the applicability to real-world metagenomic datasets is shown. In conclusion, our reference-independent approach automatically bins assembled contigs or long reads, exhibits high sensitivity and precision, enables intuitive inspection of the results, and only requires FASTA-formatted input. The web-based application is freely accessible at: https://ccb-microbe.cs.uni-saarland.de/busybee.

Colonization and Succession within the Human Gut Microbiome by Archaea, Bacteria, and Microeukaryotes during the First Year of Life.

Perturbations to the colonization process of the human gastrointestinal tract have been suggested to result in adverse health effects later in life. Although much research has been performed on bacterial colonization and succession, much less is known about the other two domains of life, archaea, and eukaryotes. Here we describe colonization and succession by bacteria, archaea and microeukaryotes during the first year of life (samples collected around days 1, 3, 5, 28, 150, and 365) within the gastrointestinal tract of infants delivered either vaginally or by cesarean section and using a combination of quantitative real-time PCR as well as 16S and 18S rRNA gene amplicon sequencing. Sequences from organisms belonging to all three domains of life were detectable in all of the collected meconium samples. The microeukaryotic community composition fluctuated strongly over time and early diversification was delayed in infants receiving formula milk. Cesarean section-delivered (CSD) infants experienced a delay in colonization and succession, which was observed for all three domains of life. Shifts in prokaryotic succession in CSD infants compared to vaginally delivered (VD) infants were apparent as early as days 3 and 5, which were characterized by increased relative abundances of the genera Streptococcus and Staphylococcus, and a decrease in relative abundance for the genera Bifidobacterium and Bacteroides. Generally, a depletion in Bacteroidetes was detected as early as day 5 postpartum in CSD infants, causing a significantly increased Firmicutes/Bacteroidetes ratio between days 5 and 150 when compared to VD infants. Although the delivery mode appeared to have the strongest influence on differences between the infants, other factors such as a younger gestational age or maternal antibiotics intake likely contributed to the observed patterns as well. Our findings complement previous observations of a delay in colonization and succession of CSD infants, which affects not only bacteria but also archaea and microeukaryotes. This further highlights the need for resolving bacterial, archaeal, and microeukaryotic dynamics in future longitudinal studies of microbial colonization and succession within the neonatal gastrointestinal tract.

IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses.

Existing workflows for the analysis of multi-omic microbiome datasets are lab-specific and often result in sub-optimal data usage. Here we present IMP, a reproducible and modular pipeline for the integrated and reference-independent analysis of coupled metagenomic and metatranscriptomic data. IMP incorporates robust read preprocessing, iterative co-assembly, analyses of microbial community structure and function, automated binning, as well as genomic signature-based visualizations. The IMP-based data integration strategy enhances data usage, output volume, and output quality as demonstrated using relevant use-cases. Finally, IMP is encapsulated within a user-friendly implementation using Python and Docker. IMP is available at http://r3lab.uni.lu/web/imp/ (MIT license).

Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes.

The gastrointestinal microbiome is a complex ecosystem with functions that shape human health. Studying the relationship between taxonomic alterations and functional repercussions linked to disease remains challenging. Here, we present an integrative approach to resolve the taxonomic and functional attributes of gastrointestinal microbiota at the metagenomic, metatranscriptomic and metaproteomic levels. We apply our methods to samples from four families with multiple cases of type 1 diabetes mellitus (T1DM). Analysis of intra- and inter-individual variation demonstrates that family membership has a pronounced effect on the structural and functional composition of the gastrointestinal microbiome. In the context of T1DM, consistent taxonomic differences were absent across families, but certain human exocrine pancreatic proteins were found at lower levels. The associated microbial functional signatures were linked to metabolic traits in distinct taxa. The methodologies and results provide a foundation for future large-scale integrated multi-omic analyses of the gastrointestinal microbiome in the context of host-microbe interactions in human health and disease.

Identification, Recovery, and Refinement of Hitherto Undescribed Population-Level Genomes from the Human Gastrointestinal Tract.

Linking taxonomic identity and functional potential at the population-level is important for the study of mixed microbial communities and is greatly facilitated by the availability of microbial reference genomes. While the culture-independent recovery of population-level genomes from environmental samples using the binning of metagenomic data has expanded available reference genome catalogs, several microbial lineages remain underrepresented. Here, we present two reference-independent approaches for the identification, recovery, and refinement of hitherto undescribed population-level genomes. The first approach is aimed at genome recovery of varied taxa and involves multi-sample automated binning using CANOPY CLUSTERING complemented by visualization and human-augmented binning using VIZBIN post hoc. The second approach is particularly well-suited for the study of specific taxa and employs VIZBIN de novo. Using these approaches, we reconstructed a total of six population-level genomes of distinct and divergent representatives of the Alphaproteobacteria class, the Mollicutes class, the Clostridiales order, and the Melainabacteria class from human gastrointestinal tract-derived metagenomic data. Our results demonstrate that, while automated binning approaches provide great potential for large-scale studies of mixed microbial communities, these approaches should be complemented with informative visualizations because expert-driven inspection and refinements are critical for the recovery of high-quality population-level genomes.

VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data.

BACKGROUND: Metagenomics is limited in its ability to link distinct microbial populations to genetic potential due to a current lack of representative isolate genome sequences. Reference-independent approaches, which exploit for example inherent genomic signatures for the clustering of metagenomic fragments (binning), offer the prospect to resolve and reconstruct population-level genomic complements without the need for prior knowledge. RESULTS: We present VizBin, a Java™-based application which offers efficient and intuitive reference-independent visualization of metagenomic datasets from single samples for subsequent human-in-the-loop inspection and binning. The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation. We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium. The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented. CONCLUSIONS: VizBin can be applied de novo for the visualization and subsequent binning of metagenomic datasets from single samples, and it can be used for the post hoc inspection and refinement of automatically generated bins. Due to its computational efficiency, it can be run on common desktop machines and enables the analysis of complex metagenomic datasets in a matter of minutes. The software implementation is available at https://claczny.github.io/VizBin under the BSD License (four-clause) and runs under Microsoft Windows™, Apple Mac OS X™ (10.7 to 10.10), and Linux.