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Background: Alterations to the gut microbiome have been linked to multiple chronic diseases. However, the drivers of such changes remain largely unknown. The oral cavity acts as a major route of exposure to exogenous factors including pathogens, and processes therein may affect the communities in the subsequent compartments of the gastrointestinal tract. Here, we perform strain‑resolved, integrated meta‑genomic, transcriptomic, and proteomic analyses of paired saliva and stool samples collected from 35 individuals from eight families with multiple cases of type 1 diabetes mellitus (T1DM). Results: We identified distinct oral microbiota mostly reflecting competition between streptococcal species. More specifically, we found a decreased abundance of the commensal Streptococcus salivarius in the oral cavity of T1DM individuals, which is linked to its apparent competition with the pathobiont Streptococcus mutans. The decrease in S. salivarius in the oral cavity was also associated with its decrease in the gut as well as higher abundances in facultative anaerobes including Enterobacteria. In addition, we found evidence of gut inflammation in T1DM as reflected in the expression profiles of the Enterobacteria as well as in the human gut proteome. Finally, we were able to follow transmitted strain‑variants from the oral cavity to the gut at the individual omic levels, highlighting not only the transfer, but also the activity of the transmitted taxa along the gastrointestinal tract. Conclusions: Alterations of the oral microbiome in the context of T1DM impact the microbial communities in the lower gut, in particular through the reduction of “mouth‑to‑gut” transfer of Streptococcus salivarius. Our results indicate that the observed oral‑cavity‑driven gut microbiome changes may contribute towards the inflammatory processes involved in T1DM. Through the integration of multi‑omic analyses, we resolve strain‑variant “mouth‑to‑gut” transfer in a disease context.

The human gut constitutes a complex, dynamic ecosystem of trillions of microbial cells whose impact on their host’s health has been recognised in recent years. Moreover, inconceivable changes in gut physiology and the subsequent adaptations of the microbiome may offer a route to early diagnosis of diseases that are increasingly more difficult to treat the later they are detected and to monitor health in general. However, the released microbial cells may not hold the only informative molecules in stool. In Gut , Tarallo et al 1 combine gut microbiome profiling with an analysis of the stool microRNA profiles of the same healthy individuals. They investigate relationships between the two and their combined potential as biomarkers. There is great potential in improving one’s health by taking good care of one’s microbes. Hence, the microbiome’s interactions with the immune system, diet and a multitude of other host factors are a field of lively study. It is becoming clear that the microbiome modulates the health effects of life styles, and especially of diet. Mechanisms of this modulation are being discovered. Moreover, the intimate relationship of the microbiome with its host …

Decomposition by microorganisms of plastics in soils is almost unexplored despite the fact that the majority of plastics released into the environment end up in soils. Here, we investigate the decomposition process and microbiome of one of the most promising biobased and biodegradable plastics, poly(butylene succinate-co-adipate) (PBSA), under field soil conditions under both ambient and future predicted climates (for the time between 2070 and 2100). We show that the gravimetric and molar mass of PBSA is already largely reduced (28-33%) after 328 days under both climates. We provide novel information on the PBSA microbiome encompassing the three domains of life: Archaea, Bacteria, and Eukarya (fungi). We show that PBSA begins to decompose after the increase in relative abundances of aquatic fungi (Tetracladium spp.) and nitrogen-fixing bacteria. The PBSA microbiome is distinct from that of surrounding soils, suggesting that PBSA serves as a new ecological habitat. We conclude that the microbial decomposition process of PBSA in soil is more complex than previously thought by involving interkingdom relationships, especially between bacteria and fungi.

Caesarean section delivery (CSD) disrupts mother-to-neonate transmission of specific microbial strains and functional repertoires as well as linked immune system priming. Here we investigate whether differences in microbiome composition and impacts on host physiology persist at 1 year of age. We perform high-resolution, quantitative metagenomic analyses of the gut microbiomes of infants born by vaginal delivery (VD) or by CSD, from immediately after birth through to 1 year of life. Several microbial populations show distinct enrichments in CSD-born infants at 1 year of age including strains of Bacteroides caccae, Bifidobacterium bifidum and Ruminococcus gnavus, whereas others are present at higher levels in the VD group including Faecalibacterium prausnitizii, Bifidobacterium breve and Bifidobacterium kashiwanohense. The stimulation of healthy donor-derived primary human immune cells with LPS isolated from neonatal stool samples results in higher levels of tumour necrosis factor alpha (TNF-α) in the case of CSD extracts over time, compared to extracts from VD infants for which no such changes were observed during the first year of life. Functional analyses of the VD metagenomes at 1 year of age demonstrate a significant increase in the biosynthesis of the natural antibiotics, carbapenem and phenazine. Concurrently, we find antimicrobial resistance (AMR) genes against several classes of antibiotics in both VD and CSD. The abundance of AMR genes against synthetic (including semi-synthetic) agents such as phenicol, pleuromutilin and diaminopyrimidine are increased in CSD children at day 5 after birth. In addition, we find that mobile genetic elements, including phages, encode AMR genes such as glycopeptide, diaminopyrimidine and multidrug resistance genes. Our results demonstrate persistent effects at 1 year of life resulting from birth mode-dependent differences in earliest gut microbiome colonisation.

BackgroundThe microbiome has emerged as an environmental factor contributing to obesity and type 2 diabetes (T2D). While the majority of studies have focused on associations between the gut microbiome and metabolic disease, increasing evidence suggests links between circulating bacterial components (i.e. bacterial DNA) and cardiometabolic disease as well as blunted response to metabolic interventions such as bariatric surgery. In this aspect, thorough next generation sequencing based and contaminant aware approaches are lacking. To address these points, we tested whether bacterial DNA could be amplified in the blood of subjects with obesity and high metabolic risk under strict experimental and analytical control to minimize bacterial contamination. Moreover we characterized a bacterial signature associated with the individual metabolic risk and explored its dynamics alongside metabolic improvement after bariatric surgery.MethodsSubjects undergoing elective bariatric surgery were recruited into sex and BMI matched subgroups with (n=24) or without T2D (n=24). Bacterial DNA in the blood was quantified and prokaryotic 16S rRNA gene amplicons were sequenced. A contaminant aware approach was applied to derive a compositional microbial signature from bacterial sequences in subjects with and without T2D and within subjects at baseline and at three and twelve months post bariatric surgery. We modelled associations between bacterial load and composition with host metabolic and anthropometric markers. We further tested whether compositional shifts were related to weight loss response and T2D remission after bariatric surgery. Lastly, Catalyzed Reporter Deposition (CARD) - Fluorescence In Situ Hybridization (FISH) was employed to visualize bacteria in blood samples.ResultsContaminant aware classification of bacterial 16S rRNA sequences allowed the derivation of a blood bacterial signature, which was associated with metabolic health. Based on bacterial phyla and genera detected in the blood samples, a metabolic syndrome classification index score was derived and shown to robustly classify subjects along their actual clinical group. T2D was characterized by decreased bacterial richness and a loss of genera associated with improved metabolic health. Moreover, circulating bacterial load was significantly associated with metabolic health and increased after bariatric surgery. Weight loss and metabolic improvement following bariatric surgery were associated with an early and stable increase of these genera in parallel with improvements in key cardiometabolic risk parameters. CARD-FISH allowed the detection of living Bacteria in blood samples in obesity.ConclusionsWe show that the circulating bacterial signature reflects metabolic disease and its improvement after bariatric surgery. Our work provides contaminant aware evidence for the presence of living bacteria in the blood and suggests a putative crosstalk between components of the blood and metabolism in metabolic health regulation.

Background Pathogenic microorganisms cause disease by invading, colonizing, and damaging their host. Virulence factors including bacterial toxins contribute to pathogenicity. Additionally, antimicrobial resistance genes allow pathogens to evade otherwise curative treatments. To understand causal relationships between microbiome compositions, functioning, and disease, it is essential to identify virulence factors and antimicrobial resistance genes in situ. At present, there is a clear lack of computational approaches to simultaneously identify these factors in metagenomic datasets. Results Here, we present PathoFact, a tool for the contextualized prediction of virulence factors, bacterial toxins, and antimicrobial resistance genes with high accuracy (0.921, 0.832 and 0.979, respectively) and specificity (0.957, 0.989 and 0.994). We evaluate the performance of PathoFact on simulated metagenomic datasets and perform a comparison to two other general workflows for the analysis of metagenomic data. PathoFact outperforms all existing workflows in predicting virulence factors and toxin genes. It performs comparably to one pipeline regarding the prediction of antimicrobial resistance while outperforming the others. We further demonstrate the performance of PathoFact on three publicly available case-control metagenomic datasets representing an actual infection as well as chronic diseases in which either pathogenic potential or bacterial toxins are hypothesized to play a role. In each case, we identify virulence factors and AMR genes which differentiated between the case and control groups, thereby revealing novel gene associations with the studied diseases. Conclusion PathoFact is an easy-to-use, modular, and reproducible pipeline for the identification of virulence factors, bacterial toxins, and antimicrobial resistance genes in metagenomic data. Additionally, our tool combines the prediction of these pathogenicity factors with the identification of mobile genetic elements. This provides further depth to the analysis by considering the genomic context of the pertinent genes. Furthermore, PathoFact’s modules for virulence factors, toxins, and antimicrobial resistance genes can be applied independently, thereby making it a flexible and versatile tool. PathoFact, its models, and databases are freely available at https://pathofact.lcsb.uni.lu.

The relationship between biodiversity and ecosystem functioning (BEF) is a central issue in soil and microbial ecology. To date, most belowground BEF studies focus on the diversity of microbes analyzed by barcoding on total DNA, which targets both active and inactive microbes. This approach creates a bias as it mixes the part of the microbiome currently steering processes that provide actual ecosystem functions with the part not directly involved. Using experimental extensive grasslands under current and future climate, we used the bromodeoxyuridine (BrdU) immunocapture technique combined with pair-end Illumina sequencing to characterize both total and active microbiomes (including both bacteria and fungi) in the rhizosphere of Trifolium pratense. Rhizosphere function was assessed by measuring the activity of three microbial extracellular enzymes (β-glucosidase, N-acetyl-glucosaminidase, and acid phosphatase), which play central roles in the C, N, and P acquisition. We showed that the richness of overall and specific functional groups of active microbes in rhizosphere soil significantly correlated with the measured enzyme activities, while total microbial richness did not. Active microbes of the rhizosphere represented 42.8 and 32.1% of the total bacterial and fungal taxa, respectively, and were taxonomically and functionally diverse. Nitrogen fixing bacteria were highly active in this system with 71% of the total operational taxonomic units (OTUs) assigned to this group detected as active. We found the total and active microbiomes to display different responses to variations in soil physicochemical factors in the grassland, but with some degree of resistance to a manipulation mimicking future climate. Our findings provide critical insights into the role of active microbes in defining soil ecosystem functions in a grassland ecosystem. We demonstrate that the relationship between biodiversity-ecosystem functioning in soil may be stronger than previously thought.

Background Amplicon sequencing of phylogenetic marker genes, e.g., 16S, 18S, or ITS ribosomal RNA sequences, is still the most commonly used method to determine the composition of microbial communities. Microbial ecologists often have expert knowledge on their biological question and data analysis in general, and most research institutes have computational infrastructures to use the bioinformatics command line tools and workflows for amplicon sequencing analysis, but requirements of bioinformatics skills often limit the efficient and up-to-date use of computational resources. Results We present dadasnake, a user-friendly, 1-command Snakemake pipeline that wraps the preprocessing of sequencing reads and the delineation of exact sequence variants by using the favorably benchmarked and widely used DADA2 algorithm with a taxonomic classification and the post-processing of the resultant tables, including hand-off in standard formats. The suitability of the provided default configurations is demonstrated using mock community data from bacteria and archaea, as well as fungi. Conclusions By use of Snakemake, dadasnake makes efficient use of high-performance computing infrastructures. Easy user configuration guarantees flexibility of all steps, including the processing of data from multiple sequencing platforms. It is easy to install dadasnake via conda environments. dadasnake is available at https://github.com/a-h-b/dadasnake.

The human gut microbiome represents a complex ecosystem contributing essential functions to its host. Recent large-scale metagenomic studies have provided insights into its structure and functional potential. However, the functional repertoire which is actually contributed to human physiology remains largely unexplored. Here, by leveraging recent omics datasets, we challenge current assumptions regarding key attributes of the functional gut microbiome, in particular with respect to its variability. We further argue that the closing of existing gaps in functional knowledge should be addressed by a most-wanted gene list, the development and application of molecular and cellular high-throughput measurements, the development and sensible use of experimental models, as well as the direct study of observable molecular effects in the human host.

Background Increasing evidence connects the gut microbiota and the onset and/or phenotype of Parkinson's disease (PD). Differences in the abundances of specific bacterial taxa have been reported in PD patients. It is, however, unknown whether these differences can be observed in individuals at high risk, for example, with idiopathic rapid eye movement sleep behavior disorder, a prodromal condition of α-synuclein aggregation disorders including PD. Objectives To compare microbiota in carefully preserved nasal wash and stool samples of subjects with idiopathic rapid eye movement sleep behavior disorder, manifest PD, and healthy individuals. Methods Microbiota of flash-frozen stool and nasal wash samples from 76 PD patients, 21 idiopathic rapid eye movement sleep behavior disorder patients, and 78 healthy controls were assessed by 16S and 18S ribosomal RNA amplicon sequencing. Seventy variables, related to demographics, clinical parameters including nonmotor symptoms, and sample processing, were analyzed in relation to microbiome variability and controlled differential analyses were performed. Results Differentially abundant gut microbes, such as Akkermansia, were observed in PD, but no strong differences in nasal microbiota. Eighty percent of the differential gut microbes in PD versus healthy controls showed similar trends in idiopathic rapid eye movement sleep behavior disorder, for example, Anaerotruncus and several Bacteroides spp., and correlated with nonmotor symptoms. Metagenomic sequencing of select samples enabled the reconstruction of genomes of so far uncharacterized differentially abundant organisms. Conclusion Our study reveals differential abundances of gut microbial taxa in PD and its prodrome idiopathic rapid eye movement sleep behavior disorder in comparison to the healthy controls, and highlights the potential of metagenomics to identify and characterize microbial taxa, which are enriched or depleted in PD and/or idiopathic rapid eye movement sleep behavior disorder. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

With the technological advances of the last decade, it is now feasible to analyze microbiome samples, such as human stool specimens, using multi-omic techniques. Given the inherent sample complexity, there exists a need for sample methods which preserve as much information as possible about the biological system at the time of sampling. Here, we analyzed human stool samples preserved and stored using different methods, applying metagenomics as well as metaproteomics. Our results demonstrate that sample preservation and storage have a significant effect on the taxonomic composition of identified proteins. The overall identification rates, as well as the proportion of proteins from Actinobacteria were much higher when samples were flash frozen. Preservation in RNAlater overall led to fewer protein identifications and a considerable increase in the share of Bacteroidetes, as well as Proteobacteria. Additionally, a decrease in the share of metabolism-related proteins and an increase of the relative amount of proteins involved in the processing of genetic information was observed for RNAlater-stored samples. This suggests that great care should be taken in choosing methods for the preservation and storage of microbiome samples, as well as in comparing the results of analyses using different sampling and storage methods. Flash freezing and subsequent storage at &minus, 80 &deg, C should be chosen wherever possible.

The rate of caesarean section delivery (CSD) is increasing worldwide. It remains unclear whether disruption of mother-to-neonate transmission of microbiota through CSD occurs and whether it affects human physiology. Here we perform metagenomic analysis of earliest gut microbial community structures and functions. We identify differences in encoded functions between microbiomes of vaginally delivered (VD) and CSD neonates. Several functional pathways are over-represented in VD neonates, including lipopolysaccharide (LPS) biosynthesis. We link these enriched functions to individual-specific strains, which are transmitted from mothers to neonates in case of VD. The stimulation of primary human immune cells with LPS isolated from early stool samples of VD neonates results in higher levels of tumour necrosis factor (TNF-α) and interleukin 18 (IL-18). Accordingly, the observed levels of TNF-α and IL-18 in neonatal blood plasma are higher after VD. Taken together, our results support that CSD disrupts mother-to-neonate transmission of specific microbial strains, linked functional repertoires and immune-stimulatory potential during a critical window for neonatal immune system priming., The effects of caesarean section delivery on mother-to-neonate transmission of microbiota are unclear. Here the authors show that caesarean section delivery can affect the transmission of specific microbial strains and the immunomodulatory potential of the microbiota.

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.

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). Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1116-8) contains supplementary material, which is available to authorized users.

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.