Combinatorial, additive and dose-dependent drug-microbiome associations.

During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery1-5. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease.

Human gut microbes impact host serum metabolome and insulin sensitivity.

Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.

Effects of Low FODMAP Diet on Symptoms, Fecal Microbiome, and Markers of Inflammation in Patients With Quiescent Inflammatory Bowel Disease in a Randomized Trial.

BACKGROUND & AIMS: There is limited evidence that a diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs) reduces gut symptoms in quiescent inflammatory bowel disease (IBD). We performed a randomized, controlled trial to investigate the effects of a low FODMAP diet on persistent gut symptoms, the intestinal microbiome, and circulating markers of inflammation in patients with quiescent IBD. METHODS: We performed a single-blind trial of 52 patients with quiescent Crohn's disease or ulcerative colitis and persistent gut symptoms at 2 large gastroenterology clinics in the United Kingdom. Patients were randomly assigned to groups that followed a diet low in FODMAPs (n = 27) or a control diet (n = 25), with dietary advice, for 4 weeks. Gut symptoms and health-related quality of life were measured using validated questionnaires. Stool and blood samples were collected at baseline and end of trial. We assessed fecal microbiome composition and function using shotgun metagenomic sequencing and phenotypes of T cells in blood using flow cytometry. RESULTS: A higher proportion of patients reported adequate relief of gut symptoms following the low FODMAP diet (14/27, 52%) than the control diet (4/25, 16%, P=.007). Patients had a greater reduction in irritable bowel syndrome severity scores following the low FODMAP diet (mean reduction of 67; standard error, 78) than the control diet (mean reduction of 34; standard error, 50), although this difference was not statistically significant (P = .075). Following the low FODMAP diet, patients had higher health-related quality of life scores (81.9 ± 1.2) than patients on the control diet (78.3 ± 1.2, P = .042). A targeted analysis revealed that in stool samples collected at the end of the study period, patients on the low FODMAP diet had significantly lower abundance of Bifidobacterium adolescentis, Bifidobacterium longum, and Faecalibacterium prausnitzii than patients on control diet. However, microbiome diversity and markers of inflammation did not differ significantly between groups. CONCLUSIONS: In a trial of the low FODMAP diet vs a control diet in patients with quiescent IBD, we found no significant difference after 4 weeks in change in irritable bowel syndrome severity scores, but significant improvements in specific symptom scores and numbers reporting adequate symptom relief. The low FODMAP diet reduced fecal abundance of microbes believed to regulate the immune response, compared with the control diet, but had no significant effect on markers of inflammation. We conclude that a 4-week diet low in FODMAPs is safe and effective for managing persistent gut symptoms in patients with quiescent IBD. www.isrctn.com no.: ISRCTN17061468.

Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.

In recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported. In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals. Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D). Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis. Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin. We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species. Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa. These in turn cause functional microbiome shifts, in part alleviated by metformin-induced changes. Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication.

Commensal Hafnia alvei strain reduces food intake and fat mass in obese mice-a new potential probiotic for appetite and body weight management.

BACKGROUND/OBJECTIVES: Based on the recent identification of E.coli heat shock protein ClpB as a mimetic of the anorexigenic α-melanocyte stimulating hormone (α-MSH), the objective of this study was to preclinically validate Hafnia alvei, a ClpB-producing commensal bacterium as a potential probiotic for appetite and body weight management in overweight and obesity. METHODS: The involvement of enterobacterial ClpB in the putative anti-obesity effects was studied using ClpB-deficient E.coli. A food-grade H. alvei HA4597 strain synthetizing the ClpB protein with an α-MSH-like motif was selected as a candidate probiotic to be tested in ob/ob and high-fat diet (HFD)-fed obese and overweight mice. The relevance of the enterobacterial ClpB gene to human obesity was studied by in silico analysis of fecal metagenomes of 569 healthy individuals from the "MetaHIT" database. RESULTS: Chronic per os administration of native but not ClpB-deficient E.coli strain reduced body weight gain (p < 0.05) and daily meal frequency (p < 0.001) in ob/ob mice. Oral gavage of H.alvei for 18 and 46 days in ob/ob and HFD-fed obese mice, respectively, was well tolerated, reduced body weight gain and fat mass in both obesity models (p < 0.05) and decreased food intake in hyperphagic ob/ob mice (p < 0.001). Elevated fat tissue levels of phosphorylated hormone-sensitive lipase were detected in H.alvei -treated ob/ob mice (p < 0.01). Enterobacterial ClpB gene richness was lower in obese vs. non-obese humans (p < 0.0001) and correlated negatively with BMI in genera of Enterobacter, Klebsiella and Hafnia. CONCLUSIONS: H.alvei HA4597 strain reduces food intake, body weight and fat mass gain in hyperphagic and obese mice. These data combined with low enterobacterial ClpB gene abundance in the microbiota of obese humans provide the rationale for using H.alvei as a probiotic for appetite and body weight management in overweight and obesity.

MSPminer: abundance-based reconstitution of microbial pan-genomes from shotgun metagenomic data.

MOTIVATION: Analysis toolkits for shotgun metagenomic data achieve strain-level characterization of complex microbial communities by capturing intra-species gene content variation. Yet, these tools are hampered by the extent of reference genomes that are far from covering all microbial variability, as many species are still not sequenced or have only few strains available. Binning co-abundant genes obtained from de novo assembly is a powerful reference-free technique to discover and reconstitute gene repertoire of microbial species. While current methods accurately identify species core parts, they miss many accessory genes or split them into small gene groups that remain unassociated to core clusters. RESULTS: We introduce MSPminer, a computationally efficient software tool that reconstitutes Metagenomic Species Pan-genomes (MSPs) by binning co-abundant genes across metagenomic samples. MSPminer relies on a new robust measure of proportionality coupled with an empirical classifier to group and distinguish not only species core genes but accessory genes also. Applied to a large scale metagenomic dataset, MSPminer successfully delineates in a few hours the gene repertoires of 1661 microbial species with similar specificity and higher sensitivity than existing tools. The taxonomic annotation of MSPs reveals microorganisms hitherto unknown and brings coherence in the nomenclature of the species of the human gut microbiota. The provided MSPs can be readily used for taxonomic profiling and biomarkers discovery in human gut metagenomic samples. In addition, MSPminer can be applied on gene count tables from other ecosystems to perform similar analyses. AVAILABILITY AND IMPLEMENTATION: The binary is freely available for non-commercial users at www.enterome.com/downloads. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Alterations of the human gut microbiome in liver cirrhosis.

Liver cirrhosis occurs as a consequence of many chronic liver diseases that are prevalent worldwide. Here we characterize the gut microbiome in liver cirrhosis by comparing 98 patients and 83 healthy control individuals. We build a reference gene set for the cohort containing 2.69 million genes, 36.1% of which are novel. Quantitative metagenomics reveals 75,245 genes that differ in abundance between the patients and healthy individuals (false discovery rate < 0.0001) and can be grouped into 66 clusters representing cognate bacterial species; 28 are enriched in patients and 38 in control individuals. Most (54%) of the patient-enriched, taxonomically assigned species are of buccal origin, suggesting an invasion of the gut from the mouth in liver cirrhosis. Biomarkers specific to liver cirrhosis at gene and function levels are revealed by a comparison with those for type 2 diabetes and inflammatory bowel disease. On the basis of only 15 biomarkers, a highly accurate patient discrimination index is created and validated on an independent cohort. Thus microbiota-targeted biomarkers may be a powerful tool for diagnosis of different diseases.

Richness of human gut microbiome correlates with metabolic markers.

We are facing a global metabolic health crisis provoked by an obesity epidemic. Here we report the human gut microbial composition in a population sample of 123 non-obese and 169 obese Danish individuals. We find two groups of individuals that differ by the number of gut microbial genes and thus gut bacterial richness. They contain known and previously unknown bacterial species at different proportions; individuals with a low bacterial richness (23% of the population) are characterized by more marked overall adiposity, insulin resistance and dyslipidaemia and a more pronounced inflammatory phenotype when compared with high bacterial richness individuals. The obese individuals among the lower bacterial richness group also gain more weight over time. Only a few bacterial species are sufficient to distinguish between individuals with high and low bacterial richness, and even between lean and obese participants. Our classifications based on variation in the gut microbiome identify subsets of individuals in the general white adult population who may be at increased risk of progressing to adiposity-associated co-morbidities.

Metagenomic species profiling using universal phylogenetic marker genes.

To quantify known and unknown microorganisms at species-level resolution using shotgun sequencing data, we developed a method that establishes metagenomic operational taxonomic units (mOTUs) based on single-copy phylogenetic marker genes. Applied to 252 human fecal samples, the method revealed that on average 43% of the species abundance and 58% of the richness cannot be captured by current reference genome-based methods. An implementation of the method is available at http://www.bork.embl.de/software/mOTU/.

Quality control of microbiota metagenomics by k-mer analysis.

BACKGROUND: The biological and clinical consequences of the tight interactions between host and microbiota are rapidly being unraveled by next generation sequencing technologies and sophisticated bioinformatics, also referred to as microbiota metagenomics. The recent success of metagenomics has created a demand to rapidly apply the technology to large case-control cohort studies and to studies of microbiota from various habitats, including habitats relatively poor in microbes. It is therefore of foremost importance to enable a robust and rapid quality assessment of metagenomic data from samples that challenge present technological limits (sample numbers and size). Here we demonstrate that the distribution of overlapping k-mers of metagenome sequence data predicts sequence quality as defined by gene distribution and efficiency of sequence mapping to a reference gene catalogue. RESULTS: We used serial dilutions of gut microbiota metagenomic datasets to generate well-defined high to low quality metagenomes. We also analyzed a collection of 52 microbiota-derived metagenomes. We demonstrate that k-mer distributions of metagenomic sequence data identify sequence contaminations, such as sequences derived from "empty" ligation products. Of note, k-mer distributions were also able to predict the frequency of sequences mapping to a reference gene catalogue not only for the well-defined serial dilution datasets, but also for 52 human gut microbiota derived metagenomic datasets. CONCLUSIONS: We propose that k-mer analysis of raw metagenome sequence reads should be implemented as a first quality assessment prior to more extensive bioinformatics analysis, such as sequence filtering and gene mapping. With the rising demand for metagenomic analysis of microbiota it is crucial to provide tools for rapid and efficient decision making. This will eventually lead to a faster turn-around time, improved analytical quality including sample quality metrics and a significant cost reduction. Finally, improved quality assessment will have a major impact on the robustness of biological and clinical conclusions drawn from metagenomic studies.

Dietary modulation of the gut microbiota--a randomised controlled trial in obese postmenopausal women.

The gut microbiota has been implicated in obesity and its progression towards metabolic disease. Dietary interventions that target the gut microbiota have been suggested to improve metabolic health. The aim of the present study was to investigate the effect of interventions with Lactobacillus paracasei F19 or flaxseed mucilage on the gut microbiota and metabolic risk markers in obesity. A total of fifty-eight obese postmenopausal women were randomised to a single-blinded, parallel-group intervention of 6-week duration, with a daily intake of either L. paracasei F19 (9.4 × 1010 colony-forming units), flaxseed mucilage (10 g) or placebo. Quantitative metagenomic analysis of faecal DNA was performed to identify the changes in the gut microbiota. Diet-induced changes in metabolic markers were explored using adjusted linear regression models. The intake of flaxseed mucilage over 6 weeks led to a reduction in serum C-peptide and insulin release during an oral glucose tolerance test (P< 0.05) and improved insulin sensitivity measured by Matsuda index (P< 0.05). Comparison of gut microbiota composition at baseline and after 6 weeks of intervention with flaxseed mucilage showed alterations in abundance of thirty-three metagenomic species (P< 0.01), including decreased relative abundance of eight Faecalibacterium species. These changes in the microbiota could not explain the effect of flaxseed mucilage on insulin sensitivity. The intake of L. paracasei F19 did not modulate metabolic markers compared with placebo. In conclusion, flaxseed mucilage improves insulin sensitivity and alters the gut microbiota; however, the improvement in insulin sensitivity was not mediated by the observed changes in relative abundance of bacterial species.

Prepublication data sharing.

Rapid release of prepublication data has served the field of genomics well. Attendees at a workshop in Toronto recommend extending the practice to other biological data sets.

Building the repertoire of dispensable chromosome regions in Bacillus subtilis entails major refinement of cognate large-scale metabolic model.

The nonessential regions in bacterial chromosomes are ill-defined due to incomplete functional information. Here, we establish a comprehensive repertoire of the genome regions that are dispensable for growth of Bacillus subtilis in a variety of media conditions. In complex medium, we attempted deletion of 157 individual regions ranging in size from 2 to 159 kb. A total of 146 deletions were successful in complex medium, whereas the remaining regions were subdivided to identify new essential genes (4) and coessential gene sets (7). Overall, our repertoire covers ~76% of the genome. We screened for viability of mutant strains in rich defined medium and glucose minimal media. Experimental observations were compared with predictions by the iBsu1103 model, revealing discrepancies that led to numerous model changes, including the large-scale application of model reconciliation techniques. We ultimately produced the iBsu1103V2 model and generated predictions of metabolites that could restore the growth of unviable strains. These predictions were experimentally tested and demonstrated to be correct for 27 strains, validating the refinements made to the model. The iBsu1103V2 model has improved considerably at predicting loss of viability, and many insights gained from the model revisions have been integrated into the Model SEED to improve reconstruction of other microbial models.

Bacterial protein signals are associated with Crohn's disease.

OBJECTIVE: No Crohn's disease (CD) molecular maker has advanced to clinical use, and independent lines of evidence support a central role of the gut microbial community in CD. Here we explore the feasibility of extracting bacterial protein signals relevant to CD, by interrogating myriads of intestinal bacterial proteomes from a small number of patients and healthy controls. DESIGN: We first developed and validated a workflow-including extraction of microbial communities, two-dimensional difference gel electrophoresis (2D-DIGE), and LC-MS/MS-to discover protein signals from CD-associated gut microbial communities. Then we used selected reaction monitoring (SRM) to confirm a set of candidates. In parallel, we used 16S rRNA gene sequencing for an integrated analysis of gut ecosystem structure and functions. RESULTS: Our 2D-DIGE-based discovery approach revealed an imbalance of intestinal bacterial functions in CD. Many proteins, largely derived from Bacteroides species, were over-represented, while under-represented proteins were mostly from Firmicutes and some Prevotella members. Most overabundant proteins could be confirmed using SRM. They correspond to functions allowing opportunistic pathogens to colonise the mucus layers, breach the host barriers and invade the mucosae, which could still be aggravated by decreased host-derived pancreatic zymogen granule membrane protein GP2 in CD patients. Moreover, although the abundance of most protein groups reflected that of related bacterial populations, we found a specific independent regulation of bacteria-derived cell envelope proteins. CONCLUSIONS: This study provides the first evidence that quantifiable bacterial protein signals are associated with CD, which can have a profound impact on future molecular diagnosis.

Anti-inflammatory properties of Streptococcus salivarius, a commensal bacterium of the oral cavity and digestive tract.

Streptococcus salivarius is one of the first colonizers of the human oral cavity and gut after birth and therefore may contribute to the establishment of immune homeostasis and regulation of host inflammatory responses. The anti-inflammatory potential of S. salivarius was first evaluated in vitro on human intestinal epithelial cells and human peripheral blood mononuclear cells. We show that live S. salivarius strains inhibited in vitro the activation of the NF-κB pathway on intestinal epithelial cells. We also demonstrate that the live S. salivarius JIM8772 strain significantly inhibited inflammation in severe and moderate colitis mouse models. These in vitro and in vivo anti-inflammatory properties were not found with heat-killed S. salivarius, suggesting a protective response exclusively with metabolically active bacteria.

Transient colonizing microbes promote gut dysbiosis and functional impairment.

Species composition of the healthy adult gut microbiota tends to be stable over time. Destabilization of the gut microbiome under the influence of different factors is the main driver of the microbial dysbiosis and subsequent impacts on host physiology. Here, we used metagenomics data from a Swedish longitudinal cohort, to determine the stability of the gut microbiome and uncovered two distinct microbial species groups; persistent colonizing species (PCS) and transient colonizing species (TCS). We validated the continuation of this grouping, generating gut metagenomics data for additional time points from the same Swedish cohort. We evaluated the existence of PCS/TCS across different geographical regions and observed they are globally conserved features. To characterize PCS/TCS phenotypes, we performed bioreactor fermentation with faecal samples and metabolic modeling. Finally, using chronic disease gut metagenome and other multi-omics data, we identified roles of TCS in microbial dysbiosis and link with abnormal changes to host physiology.

In Vitro Modelling of Oral Microbial Invasion in the Human Colon.

Recent advances in the human microbiome characterization have revealed significant oral microbial detection in stools of dysbiotic patients. However, little is known about the potential interactions of these invasive oral microorganisms with commensal intestinal microbiota and the host. In this proof-of-concept study, we proposed a new model of oral-to-gut invasion by the combined use of an in vitro model simulating both the physicochemical and microbial (lumen- and mucus-associated microbes) parameters of the human colon (M-ARCOL), a salivary enrichment protocol, and whole-metagenome shotgun sequencing. Oral invasion of the intestinal microbiota was simulated by injection of enriched saliva in the in vitro colon model inoculated with a fecal sample from the same healthy adult donor. The mucosal compartment of M-ARCOL was able to retain the highest species richness levels over time, while species richness levels decreased in the luminal compartment. This study also showed that oral microorganisms preferably colonized the mucosal microenvironment, suggesting potential oral-to-intestinal mucosal competitions. This new model of oral-to-gut invasion can provide useful mechanistic insights into the role of oral microbiome in various disease processes. IMPORTANCE Here, we propose a new model of oral-to-gut invasion by the combined use of an in vitro model simulating both the physicochemical and microbial (lumen- and mucus-associated microbes) parameters of the human colon (M-ARCOL), a salivary enrichment protocol, and whole-metagenome shotgun sequencing. Our study revealed the importance of integrating the mucus compartment, which retained higher microbial richness during fermentation, showed the preference of oral microbial invaders for the mucosal resources, and indicated potential oral-to-intestinal mucosal competitions. It also underlined promising opportunities to further understand mechanisms of oral invasion into the human gut microbiome, define microbe-microbe and mucus-microbe interactions in a compartmentalized fashion, and help to better characterize the potential of oral microbial invasion and their persistence in the gut.

Evaluation of an Adapted Semi-Automated DNA Extraction for Human Salivary Shotgun Metagenomics.

Recent attention has highlighted the importance of oral microbiota in human health and disease, e.g., in Parkinson's disease, notably using shotgun metagenomics. One key aspect for efficient shotgun metagenomic analysis relies on optimal microbial sampling and DNA extraction, generally implementing commercial solutions developed to improve sample collection and preservation, and provide high DNA quality and quantity for downstream analysis. As metagenomic studies are today performed on a large number of samples, the next evolution to increase study throughput is with DNA extraction automation. In this study, we proposed a semi-automated DNA extraction protocol for human salivary samples collected with a commercial kit, and compared the outcomes with the DNA extraction recommended by the manufacturer. While similar DNA yields were observed between the protocols, our semi-automated DNA protocol generated significantly higher DNA fragment sizes. Moreover, we showed that the oral microbiome composition was equivalent between DNA extraction methods, even at the species level. This study demonstrates that our semi-automated protocol is suitable for shotgun metagenomic analysis, while allowing for improved sample treatment logistics with reduced technical variability and without compromising the structure of the oral microbiome.