Host-Microbe-Drug-Nutrient Screen Identifies Bacterial Effectors of Metformin Therapy.

Metformin is the first-line therapy for treating type 2 diabetes and a promising anti-aging drug. We set out to address the fundamental question of how gut microbes and nutrition, key regulators of host physiology, affect the effects of metformin. Combining two tractable genetic models, the bacterium E. coli and the nematode C. elegans, we developed a high-throughput four-way screen to define the underlying host-microbe-drug-nutrient interactions. We show that microbes integrate cues from metformin and the diet through the phosphotransferase signaling pathway that converges on the transcriptional regulator Crp. A detailed experimental characterization of metformin effects downstream of Crp in combination with metabolic modeling of the microbiota in metformin-treated type 2 diabetic patients predicts the production of microbial agmatine, a regulator of metformin effects on host lipid metabolism and lifespan. Our high-throughput screening platform paves the way for identifying exploitable drug-nutrient-microbiome interactions to improve host health and longevity through targeted microbiome therapies. VIDEO ABSTRACT.

Selection of aquatic microbiota exposed to the herbicides flufenacet and metazachlor.

Herbicides are important, ubiquitous environmental contaminants, but little is known about their interaction with bacterial aquatic communities. Here, we sampled a protected natural freshwater habitat and characterised its microbiome in interaction with herbicides. We evolved the freshwater microbiomes in a microcosm assay of exposure (28 days) to flufenacet and metazachlor at environmental concentrations of 0.5, 5 and 50 µg L-1 . Inhibitory effects of herbicides were exemplarily assessed in cultured bacteria from the same pond (Pseudomonas alcaligenes, Paenibacillus amylolyticus and Microbacterium hominis). Findings were compared to long-term concentrations as provided by local authorities. Here, environmental concentrations reached up to 11 µg L-1 (flufenacet) and 76 µg L-1 (metazachlor). Bacteria were inhibited at minimum inhibitory concentrations far above these values; however, concentrations of 50 µg L-1 of flufenacet resulted in measurable growth impairment. While most herbicide-exposed microcosm assays did not differ from controls, Acidobacteria were selected at high environmental concentrations of herbicides. Alpha-diversity (e.g., taxonomic richness on phylum level) was reduced when aquatic microbiomes were exposed to 50 µg metazachlor or flufenacet. One environmental strain of P. alcaligenes showed resistance to high concentrations of flufenacet (50 g L-1 ). In total, this study reveals that ecologic imbalance due to herbicide use significantly impacts aquatic microbiomes.

A Mitochondrial Polymorphism Alters Immune Cell Metabolism and Protects Mice from Skin Inflammation.

Several genetic variants in the mitochondrial genome (mtDNA), including ancient polymorphisms, are associated with chronic inflammatory conditions, but investigating the functional consequences of such mtDNA polymorphisms in humans is challenging due to the influence of many other polymorphisms in both mtDNA and the nuclear genome (nDNA). Here, using the conplastic mouse strain B6-mtFVB, we show that in mice, a maternally inherited natural mutation (m.7778G > T) in the mitochondrially encoded gene ATP synthase 8 (mt-Atp8) of complex V impacts on the cellular metabolic profile and effector functions of CD4+ T cells and induces mild changes in oxidative phosphorylation (OXPHOS) complex activities. These changes culminated in significantly lower disease susceptibility in two models of inflammatory skin disease. Our findings provide experimental evidence that a natural variation in mtDNA influences chronic inflammatory conditions through alterations in cellular metabolism and the systemic metabolic profile without causing major dysfunction in the OXPHOS system.

Metabolic Functions of Gut Microbes Associate With Efficacy of Tumor Necrosis Factor Antagonists in Patients With Inflammatory Bowel Diseases.

BACKGROUND & AIMS: Altered interactions between the mucosal immune system and intestinal microbiota contribute to pathogenesis of inflammatory bowel diseases (IBD). It is not clear how inhibitors of cytokines, such as antagonists of tumor necrosis factor (anti-TNF), affect the intestinal microbiome. We investigated the effects of anti-TNF agents on gut microbe community structure and function in a longitudinal 2-step study of patients with IBD. We correlated our findings with outcomes of treatment and investigated patterns of metabolites in fecal samples before and after anti-TNF therapy. METHODS: We performed a prospective study of 2 cohorts of patients in Germany; the discovery cohort comprised 12 patients with IBD, 17 patients with rheumatic disease, and 19 healthy individuals (controls); fecal samples were collected at baseline and 2, 6, and 30 weeks after induction of anti-TNF therapy. The validation cohort comprised 23 patients with IBD treated with anti-TNF or vedolizumab (anti-α4ß7 integrin) and 99 healthy controls; fecal samples were collected at baseline and at weeks 2, 6, and 14. Fecal microbiota were analyzed by V3-V4 16S ribosomal RNA gene amplicon sequencing. Clinical response and remission were determined by clinical disease activity scores. Metabolic network reconstruction and associated fecal metabolite level inference was performed in silico using the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. Metabolomic analyses of fecal samples from a subset of patients were performed to validate metabolites associated with treatment outcomes. RESULTS: Anti-TNF therapy shifted the diversity of fecal microbiota in patients with IBD, but not with rheumatic disease, toward that of controls. Across timepoints, diversity indices did not vary significantly between patients with IBD who did or did not achieve clinical remission after therapy. In contrast, in silico modeling of metabolic interactions between gut microbes found metabolite exchange to be significantly reduced at baseline in fecal samples from patients with IBD and to be associated with later clinical remission. Predicted levels of butyrate and substrates involved in butyrate synthesis (ethanol or acetaldehyde) were significantly associated with clinical remission following anti-TNF therapy, verified by fecal metabolomic analyses. CONCLUSIONS: Metabolic network reconstruction and assessment of metabolic profiles of fecal samples might be used to identify patients with IBD likely to achieve clinical remission following anti-TNF therapy and increase our understanding of the heterogeneity of IBD.

Ecology and evolution of metabolic cross-feeding interactions in bacteria.

Literature covered: early 2000s to late 2017Bacteria frequently exchange metabolites with other micro- and macro-organisms. In these often obligate cross-feeding interactions, primary metabolites such as vitamins, amino acids, nucleotides, or growth factors are exchanged. The widespread distribution of this type of metabolic interactions, however, is at odds with evolutionary theory: why should an organism invest costly resources to benefit other individuals rather than using these metabolites to maximize its own fitness? Recent empirical work has shown that bacterial genotypes can significantly benefit from trading metabolites with other bacteria relative to cells not engaging in such interactions. Here, we will provide a comprehensive overview over the ecological factors and evolutionary mechanisms that have been identified to explain the evolution and maintenance of metabolic mutualisms among microorganisms. Furthermore, we will highlight general principles that underlie the adaptive evolution of interconnected microbial metabolic networks as well as the evolutionary consequences that result for cells living in such communities.

Computing autocatalytic sets to unravel inconsistencies in metabolic network reconstructions.

MOTIVATION: Genome-scale metabolic network reconstructions have been established as a powerful tool for the prediction of cellular phenotypes and metabolic capabilities of organisms. In recent years, the number of network reconstructions has been constantly increasing, mostly because of the availability of novel (semi-)automated procedures, which enabled the reconstruction of metabolic models based on individual genomes and their annotation. The resulting models are widely used in numerous applications. However, the accuracy and predictive power of network reconstructions are commonly limited by inherent inconsistencies and gaps. RESULTS: Here we present a novel method to validate metabolic network reconstructions based on the concept of autocatalytic sets. Autocatalytic sets correspond to collections of metabolites that, besides enzymes and a growth medium, are required to produce all biomass components in a metabolic model. These autocatalytic sets are well-conserved across all domains of life, and their identification in specific genome-scale reconstructions allows us to draw conclusions about potential inconsistencies in these models. The method is capable of detecting inconsistencies, which are neglected by other gap-finding methods. We tested our method on the Model SEED, which is the largest repository for automatically generated genome-scale network reconstructions. In this way, we were able to identify a significant number of missing pathways in several of these reconstructions. Hence, the method we report represents a powerful tool to identify inconsistencies in large-scale metabolic networks. AVAILABILITY AND IMPLEMENTATION: The method is available as source code on http://users.minet.uni-jena.de/∼m3kach/ASBIG/ASBIG.zip. CONTACT: christoph.kaleta@uni-jena.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics.

While numerous health-beneficial interactions between host and microbiota have been identified, there is still a lack of targeted approaches for modulating these interactions. Thus, we here identify precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In the first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we use metabolic modeling to identify precision prebiotics for a two-member Caenorhabditis elegans microbiome community comprising the immune-protective target species Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. We experimentally confirm four of the predicted precision prebiotics, L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid, to specifically increase the abundance of MYb11. L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.IMPORTANCEWhile various mechanisms through which the microbiome influences disease processes in the host have been identified, there are still only few approaches that allow for targeted manipulation of microbiome composition as a first step toward microbiome-based therapies. Here, we propose the concept of precision prebiotics that allow to boost the abundance of already resident health-beneficial microbial species in a microbiome. We present a constraint-based modeling pipeline to predict precision prebiotics for a minimal microbial community in the worm Caenorhabditis elegans comprising the host-beneficial Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71 with the aim to boost the growth of MYb11. Experimentally testing four of the predicted precision prebiotics, we confirm that they are specifically able to increase the abundance of MYb11 in vitro and in vivo. These results demonstrate that constraint-based modeling could be an important tool for the development of targeted microbiome-based therapies against human diseases.

Tryptophan degradation as a systems phenomenon in inflammation - an analysis across 13 chronic inflammatory diseases.

BACKGROUND: Chronic inflammatory diseases (CIDs) are systems disorders that affect diverse organs including the intestine, joints and skin. The essential amino acid tryptophan (Trp) can be broken down to various bioactive derivatives important for immune regulation. Increased Trp catabolism has been observed in some CIDs, so we aimed to characterise the specificity and extent of Trp degradation as a systems phenomenon across CIDs. METHODS: We used high performance liquid chromatography and targeted mass spectrometry to assess the serum and stool levels of Trp and Trp derivatives. Our retrospective study incorporates both cross-sectional and longitudinal components, as we have included a healthy population as a reference and there are also multiple observations per patient over time. FINDINGS: We found reduced serum Trp levels across the majority of CIDs, and a prevailing negative relationship between Trp and systemic inflammatory marker C-reactive protein (CRP). Notably, serum Trp was low in several CIDs even in the absence of measurable systemic inflammation. Increases in the kynurenine-to-Trp ratio (Kyn:Trp) suggest that these changes result from increased degradation along the kynurenine pathway. INTERPRETATION: Increases in Kyn:Trp indicate the kynurenine pathway as a major route for CID-related Trp metabolism disruption and the specificity of the network changes indicates excessive Trp degradation relative to other proteogenic amino acids. Our results suggest that increased Trp catabolism is a common metabolic occurrence in CIDs that may directly affect systemic immunity. FUNDING: This work was supported by the DFG Cluster of Excellence 2167 "Precision medicine in chronic inflammation" (KA, SSchr, PR, BH, SWa), the BMBF (e:Med Juniorverbund "Try-IBD" 01ZX1915A and 01ZX2215, the e:Med Network iTREAT 01ZX2202A, and GUIDE-IBD 031L0188A), EKFS (2020_EKCS.11, KA), DFG RU5042 (PR, KA), and Innovative Medicines Initiative 2 Joint Undertakings ("Taxonomy, Treatments, Targets and Remission", 831434, "ImmUniverse", 853995, "BIOMAP", 821511).

Prediction of Response to Systemic Corticosteroids in Active UC by Microbial Composition-A Prospective Multicenter Study.

BACKGROUND: Corticosteroids are used for induction of remission in patients with moderately to severely active ulcerative colitis. However, up to one-third of patients fail to this therapy. We investigated if fecal microbial composition or its metabolic capacity are associated with response to systemic corticosteroids. METHODS: In this prospective, multicenter study, patients with active ulcerative colitis (Lichtiger score ≥4) receiving systemic corticosteroids were eligible. Data were assessed and fecal samples collected before and after 4 weeks of treatment. Patients were divided into responders (decrease of Lichtiger Score ≥50%) and nonresponders. The fecal microbiome was assessed by the 16S rRNA gene marker and analyzed with QIIME 2. Microbial metabolic pathways were predicted using parsimonious flux balance analysis. RESULTS: Among 93 included patients, 69 (74%) patients responded to corticosteroids after 4 weeks. At baseline, responders could not be distinguished from nonresponders by microbial diversity and composition, except for a subgroup of biologic-naïve patients. Within 4 weeks of treatment, responders experienced changes in beta diversity with enrichment of ascribed beneficial taxa, including Blautia, Anaerostipes, and Bifidobacterium, as well as an increase in predicted butyrate synthesis. Nonresponders had only minor longitudinal taxonomic changes with a significant increase of Streptococcus salivarius and a microbial composition shifting away from responders. CONCLUSION: Baseline microbial diversity and composition seem to be of limited use to predict response to systemic corticosteroids in active ulcerative colitis. Response is longitudinally associated with restoration of microbial composition and its metabolic capacity.

Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics.

The microbiome is increasingly receiving attention as an important modulator of host health and disease. However, while numerous mechanisms through which the microbiome influences its host have been identified, there is still a lack of approaches that allow to specifically modulate the abundance of individual microbes or microbial functions of interest. Moreover, current approaches for microbiome manipulation such as fecal transfers often entail a non-specific transfer of entire microbial communities with potentially unwanted side effects. To overcome this limitation, we here propose the concept of precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In a first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we present a metabolic modeling network framework that allows us to define precision prebiotics for a two-member C. elegans microbiome model community comprising the immune-protective Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. Thus, we predicted compounds that specifically boost the abundance of the host-beneficial MYb11, four of which were experimentally validated in vitro (L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid). L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that constraint-based metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.

Amino acid auxotrophies in human gut bacteria are linked to higher microbiome diversity and long-term stability.

Amino acid auxotrophies are prevalent among bacteria. They can govern ecological dynamics in microbial communities and indicate metabolic cross-feeding interactions among coexisting genotypes. Despite the ecological importance of auxotrophies, their distribution and impact on the diversity and function of the human gut microbiome remain poorly understood. This study performed the first systematic analysis of the distribution of amino acid auxotrophies in the human gut microbiome using a combined metabolomic, metagenomic, and metabolic modeling approach. Results showed that amino acid auxotrophies are ubiquitous in the colon microbiome, with tryptophan auxotrophy being the most common. Auxotrophy frequencies were higher for those amino acids that are also essential to the human host. Moreover, a higher overall abundance of auxotrophies was associated with greater microbiome diversity and stability, and the distribution of auxotrophs was found to be related to the human host's metabolome, including trimethylamine oxide, small aromatic acids, and secondary bile acids. Thus, our results suggest that amino acid auxotrophies are important factors contributing to microbiome ecology and host-microbiome metabolic interactions.

A gut bacterial signature in blood and liver tissue characterizes cirrhosis and hepatocellular carcinoma.

BACKGROUND: HCC is the leading cause of cancer in chronic liver disease. A growing body of experimental mouse models supports the notion that gut-resident and liver-resident microbes control hepatic immune responses and, thereby, crucially contribute to liver tumorigenesis. However, a comprehensive characterization of the intestinal microbiome in fueling the transition from chronic liver disease to HCC in humans is currently missing. METHODS: Here, we profiled the fecal, blood, and liver tissue microbiome of patients with HCC by 16S rRNA sequencing and compared profiles to nonmalignant cirrhotic and noncirrhotic NAFLD patients. RESULTS: We report a distinct bacterial profile, defined from 16S rRNA gene sequences, with reduced α-and ß-diversity in the feces of patients with HCC and cirrhosis compared to NAFLD. Patients with HCC and cirrhosis exhibited an increased proportion of fecal bacterial gene signatures in the blood and liver compared to NAFLD. Differential analysis of the relative abundance of bacterial genera identified an increased abundance of Ruminococcaceae and Bacteroidaceae in blood and liver tissue from both HCC and cirrhosis patients compared to NAFLD. Fecal samples from cirrhosis and HCC patients both showed a reduced abundance for several taxa, including short-chain fatty acid-producing genera, such as Blautia and Agathobacter. Using paired 16S rRNA and transcriptome sequencing, we identified a direct association between gut bacterial genus abundance and host transcriptome response within the liver tissue. CONCLUSIONS: Our study indicates perturbations of the intestinal and liver-resident microbiome as a critical determinant of patients with cirrhosis and HCC.

Using in-tube extraction and slice selective NMR experiments allow imaging via statistical analysis of metabolic profiles.

Imaging the distribution of metabolites is very powerful in diagnostics but it is also employed in fundamental research. Although NMR spectroscopy is well established for determining metabolic profiles of biological samples, its application is limited to magnetic resonance imaging that can produce images of larger structures, but the number of detectable metabolites is very low. Mass spectrometry imaging on the other hand is well established with pixel sizes in the µm range. This limits the analysis of larger structures like tissue sections and detection of metabolites depends on their ionization properties. High resolution NMR metabolomics could complement these methods. However, this is prevented due to time consuming extraction procedures. To overcome these limitations, the following protocol was established and applied to two different ham slices: sampling is directly done into the NMR tube and after extraction of polar and non-polar metabolites in the NMR tube, slice selective NMR spectra are acquired. Multivariate analysis (PCA) of the NMR-spectra and subsequent visualization of the differences correlate well with structures visible in the ham slices. The proposed protocol can be used for metabolic imaging and could complement other imaging methods.

Bovine milk microbiota: Evaluation of different DNA extraction protocols for challenging samples.

The use of an adequate protocol that accurately extracts microbial DNA from bovine milk samples is of importance for downstream analysis such as 16S ribosomal RNA gene sequencing. Although sequencing platforms such as Illumina are very common, there are reservations concerning reproducibility in challenging samples that combine low bacterial loads with high amounts of host DNA. The objective of this study was to evaluate six different DNA extraction protocols applied to four different prototype milk samples (low/high level of colony-forming units [cfu] and somatic cells). DNA extracts were sequenced on Illumina MiSeq with primers for the hypervariable regions V1V2 and V3V4. Different protocols were evaluated by analyzing the yield and purity of DNA extracts and the number of clean reads after sequencing. Three protocols with the highest median number of clean reads were selected. To assess reproducibility, these extraction replicates were resequenced in triplicates (n = 120). The most reproducible results for α- and ß-diversity were obtained with the modified DNeasy Blood & Tissue kit after a chemical pretreatment plus resuspension of the cream fraction. The unmodified QIAamp DNA Mini kit performed particularly weak in the sample representing unspecific mastitis. These results suggest that pretreatment in combination with the modified DNeasy Blood & Tissue kit is useful in extracting microbial DNA from challenging milk samples. To increase reproducibility, we recommend that duplicates, if not triplicates, should be sequenced. We showed that high counts of somatic cells challenged DNA extraction, which shapes the need to apply modified extraction protocols.

Long-Term Dietary Effects on Human Gut Microbiota Composition Employing Shotgun Metagenomics Data Analysis.

SCOPE: The gut microbiome regulates various metabolic pathways in the host and its dysbiosis is involved in the pathogenesis of diverse diseases. One of the major factors triggering gut microbiome establishment is diet. This study aims to unravel interactions and changes between diet and gut microbiome over a period of 3 years. METHODS AND RESULTS: This study investigates the relation between diet and the microbiome of 75 individuals over a 3-year time period. Shotgun metagenomic sequencing is performed to profile gut microbial composition and function. This study shows that there are significant changes in gut microbiome taxonomy and functional composition between two time points. Whereas microbial taxonomy is found to be highly individualized, overall microbial functions stay relatively stable. Moreover, in silico metabolic modeling of microbial communities indicates that changes in dietary intake of medium-chain saturated fatty acids is accompanied by an altered utilization of amino acids by the gut microbiome. CONCLUSION: The study design allows us to validate functional stability within the gut microbiome of healthy subjects over a 3-year period. However, enduring changes in nutrition such as increased alcohol consumption or decreased intake of vegetables come along with enhanced microbial functions that are associated with disease etiology.

Microbiota independent effects of oligosaccharides on Caco-2 cells -A semi-targeted metabolomics approach using DI-FT-ICR-MS coupled with pathway enrichment analysis.

Milk oligosaccharides (MOS) and galactooligosaccharides (GOS) are associated with many benefits, including anti-microbial effects and immune-modulating properties. However, the cellular mechanisms of these are largely unknown. In this study, the effects of enriched GOS and MOS mixtures from caprine and bovine milk consisting mainly 6'-galactosyllactose, 3'-sialyllactose, and 6'-sialyllactose on Caco-2 cells were investigated, and the treatment-specific metabolomes were described. In the control, the cells were treated with a sugar mix consisting of one-third each of glucose, galactose and lactose. A local metabolomics workflow with pathway enrichment was established, which specifically addresses DI-FT-ICR-MS analyses and includes adaptations in terms of measurement technology and sample matrices. By including quality parameters, especially the isotope pattern, we increased the precision of annotation. The independence from online tools, the fast adaptability to changes in databases, and the specific adjustment to the measurement technology and biomaterial used, proved to be a great advantage. For the first time it was possible to find 71 active pathways in a Caco-2 cell experiment. These pathways were assigned to 12 main categories, with amino acid metabolism and carbohydrate metabolism being the most dominant categories in terms of the number of metabolites and metabolic pathways. Treatment of Caco-2 cells with high GOS and glucose contents resulted in significant effects on several metabolic pathways, whereas the MOS containing treatments resulted only for individual metabolites in significant changes. An effect based on bovine or caprine origin alone could not be observed. Thus, it was shown that MOS and GOS containing treatments can exert microbiome-independent effects on the metabolome of Caco-2 cells.

Timing of antimicrobial prophylaxis for cesarean section is critical for gut microbiome development in term born infants.

Animal models imply that the perinatal exposure to antibiotics has a substantial impact on microbiome establishment of the offspring. We aimed to evaluate the effect of timing of antimicrobial prophylaxis for cesarean section before versus after cord clamping on gut microbiome composition of term born infants. We performed an exploratory, single center randomized controlled clinical trial. We included forty pregnant women with elective cesarean section at term. The intervention group received single dose intravenous cefuroxime after cord clamping (n = 19), the control group single dose intravenous cefuroxime 30 minutes before skin incision (n = 21). The primary endpoint was microbiome signature of infants and metabolic prediction in the first days of life as determined in meconium samples by 16S rRNA gene sequencing. Secondary endpoints were microbiome composition at one month and 1 year of life. In meconium samples of the intervention group, the genus Staphylococcus pre-dominated. In the control group, the placental cross-over of cefuroxime was confirmed in cord blood. A higher amino acid and nitrogen metabolism as well as increased abundance of the genera Cutibacterium, Corynebacterium and Streptophyta were noted (indicator families: Cytophagaceae, Lactobacilaceae, Oxalobacteraceae). Predictive models of metabolic function revealed higher 2'fucosyllactose utilization in control group samples. In the follow-up visits, a higher abundance of the genus Clostridium was evident in the intervention group. Our exploratory randomized controlled trial suggests that timing of antimicrobial prophylaxis is critical for early microbiome engraftment but not antimicrobial resistance emergence in term born infants.

gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models.

Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism's genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

Metabolic dissimilarity determines the establishment of cross-feeding interactions in bacteria.

The exchange of metabolites among different bacterial genotypes profoundly impacts the structure and function of microbial communities. However, the factors governing the establishment of these cross-feeding interactions remain poorly understood. While shared physiological features may facilitate interactions among more closely related individuals, a lower relatedness should reduce competition and thus increase the potential for synergistic interactions. Here, we investigate how the relationship between a metabolite donor and recipient affects the propensity of strains to engage in unidirectional cross-feeding interactions. For this, we performed pairwise cocultivation experiments between four auxotrophic recipients and 25 species of potential amino acid donors. Auxotrophic recipients grew in the vast majority of pairs tested (63%), suggesting metabolic cross-feeding interactions are readily established. Strikingly, both the phylogenetic distance between donor and recipient and the dissimilarity of their metabolic networks were positively associated with the growth of auxotrophic recipients. Analyzing the co-growth of species from a gut microbial community in silico also revealed that recipient genotypes benefitted more from interacting with metabolically dissimilar partners, thus corroborating the empirical results. Together, our work identifies the metabolic dissimilarity between bacterial genotypes as a key factor determining the establishment of metabolic cross-feeding interactions in microbial communities.

Modeling Drosophila gut microbe interactions reveals metabolic interconnectivity.

We know a lot about varying gut microbiome compositions. Yet, how the bacteria affect each other remains elusive. In mammals, this is largely based on the sheer complexity of the microbiome with at least hundreds of different species. Thus, model organisms such as Drosophila melanogaster are commonly used to investigate mechanistic questions as the microbiome consists of only about 10 leading bacterial species. Here, we isolated gut bacteria from laboratory-reared Drosophila, sequenced their respective genomes, and used this information to reconstruct genome-scale metabolic models. With these, we simulated growth in mono- and co-culture conditions and different media including a synthetic diet designed to grow Drosophila melanogaster. Our simulations reveal a synergistic growth of some but not all gut microbiome members, which stems on the exchange of distinct metabolites including tricarboxylic acid cycle intermediates. Culturing experiments confirmed our predictions. Our study thus demonstrates the possibility to predict microbiome-derived growth-promoting cross-feeding.