Hypervirulent Klebsiella pneumoniae employs genomic island encoded toxins against bacterial competitors in the gut.

The hypervirulent lineages of Klebsiella pneumoniae (HvKp) cause invasive infections such as Klebsiella-liver abscess. Invasive infection often occurs after initial colonization of the host gastrointestinal tract by HvKp. Over 80% of HvKp isolates belong to the clonal group 23 sublineage I that has acquired genomic islands (GIs) GIE492 and ICEKp10. Our analysis of 12 361 K. pneumoniae genomes revealed that GIs GIE492 and ICEKp10 are co-associated with the CG23-I and CG10118 HvKp lineages. GIE492 and ICEKp10 enable HvKp to make a functional bacteriocin microcin E492 (mccE492) and the genotoxin colibactin, respectively. We discovered that GIE492 and ICEKp10 play cooperative roles and enhance gastrointestinal colonization by HvKp. Colibactin is the primary driver of this effect, modifying gut microbiome diversity. Our in vitro assays demonstrate that colibactin and mccE492 kill or inhibit a range of Gram-negative Klebsiella species and Escherichia coli strains, including Gram-positive bacteria, sometimes cooperatively. Moreover, mccE492 and colibactin kill human anaerobic gut commensals that are similar to the taxa found altered by colibactin in the mouse intestines. Our findings suggest that GIs GIE492 and ICEKp10 enable HvKp to kill several commensal bacterial taxa during interspecies interactions in the gut. Thus, acquisition of GIE492 and ICEKp10 could enable better carriage in host populations and explain the dominance of the CG23-I HvKp lineage.

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures.

The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.

Profiling Microbial Communities in Idiopathic Granulomatous Mastitis.

Idiopathic granulomatous mastitis (IGM) is a rare and benign inflammatory breast disease with ambiguous aetiology. Contrastingly, lactational mastitis (LM) is commonly diagnosed in breastfeeding women. To investigate IGM aetiology, we profiled the microbial flora of pus and skin in patients with IGM and LM. A total of 26 patients with IGM and 6 patients with LM were included in the study. The 16S rRNA sequencing libraries were constructed from 16S rRNA gene amplified from total DNA extracted from pus and skin swabs in patients with IGM and LM controls. Constructed libraries were multiplexed and paired-end sequenced on HiSeq4000. Metagenomic analysis was conducted using modified microbiome abundance analysis suite customised R-resource for paired pus and skin samples. Microbiome multivariable association analyses were performed using linear models. A total of 21 IGM and 3 LM paired pus and skin samples underwent metagenomic analysis. Bray−Curtis ecological dissimilarity distance showed dissimilarity across four sample types (IGM pus, IGM skin, LM pus, and LM skin; PERMANOVA, p < 0.001). No characteristic dominant genus was observed across the IGM samples. The IGM pus samples were more diverse than corresponding IGM skin samples (Shannon and Simpson index; Wilcoxon paired signed-rank tests, p = 0.022 and p = 0.07). Corynebacterium kroppenstedtii, reportedly associated with IGM in the literature, was higher in IGM pus samples than paired skin samples (Wilcoxon, p = 0.022). Three other species and nineteen genera were statistically significant in paired IGM pus−skin comparison after antibiotic treatment adjustment and multiple comparisons correction. Microbial profiles are unique between patients with IGM and LM. Inter-patient variability and polymicrobial IGM pus samples cannot implicate specific genus or species as an infectious cause for IGM.

Genome-centric analysis of short and long read metagenomes reveals uncharacterized microbiome diversity in Southeast Asians.

Despite extensive efforts to address it, the vastness of uncharacterized 'dark matter' microbial genetic diversity can impact short-read sequencing based metagenomic studies. Population-specific biases in genomic reference databases can further compound this problem. Leveraging advances in hybrid assembly (using short and long reads) and Hi-C technologies in a cross-sectional survey, we deeply characterized 109 gut microbiomes from three ethnicities in Singapore to comprehensively reconstruct 4497 medium and high-quality metagenome assembled genomes, 1708 of which were missing in short-read only analysis and with >28× N50 improvement. Species-level clustering identified 70 (>10% of total) novel gut species out of 685, improved reference genomes for 363 species (53% of total), and discovered 3413 strains unique to these populations. Among the top 10 most abundant gut bacteria in our study, one of the species and >80% of strains were unrepresented in existing databases. Annotation of biosynthetic gene clusters (BGCs) uncovered more than 27,000 BGCs with a large fraction (36-88%) unrepresented in current databases, and with several unique clusters predicted to produce bacteriocins that could significantly alter microbiome community structure. These results reveal significant uncharacterized gut microbial diversity in Southeast Asian populations and highlight the utility of hybrid metagenomic references for bioprospecting and disease-focused studies.

Long-term ecological and evolutionary dynamics in the gut microbiomes of carbapenemase-producing Enterobacteriaceae colonized subjects.

Long-term colonization of the gut microbiome by carbapenemase-producing Enterobacteriaceae (CPE) is a growing area of public health concern as it can lead to community transmission and rapid increase in cases of life-threatening CPE infections. Here, leveraging the observation that many subjects are decolonized without interventions within a year, we used longitudinal shotgun metagenomics (up to 12 timepoints) for detailed characterization of ecological and evolutionary dynamics in the gut microbiome of a cohort of CPE-colonized subjects and family members (n = 46; 361 samples). Subjects who underwent decolonization exhibited a distinct ecological shift marked by recovery of microbial diversity, key commensals and anti-inflammatory pathways. In addition, colonization was marked by elevated but unstable Enterobacteriaceae abundances, which exhibited distinct strain-level dynamics for different species (Escherichia coli and Klebsiella pneumoniae). Finally, comparative analysis with whole-genome sequencing data from CPE isolates (n = 159) helped identify substrain variation in key functional genes and the presence of highly similar E. coli and K. pneumoniae strains with variable resistance profiles and plasmid sharing. These results provide an enhanced view into how colonization by multi-drug-resistant bacteria associates with altered gut ecology and can enable transfer of resistance genes, even in the absence of overt infection and antibiotic usage.

Mutual Exclusion of Methanobrevibacter Species in the Human Gut Microbiota Facilitates Directed Cultivation of a Candidatus Methanobrevibacter Intestini Representative.

Methanogenic Archaea (methanogens) are a phylogenetically diverse group of microorganisms and are considered to be the most abundant archaeal representatives in the human gut. However, the gut methanogen diversity of human populations in many global regions remains poorly investigated. Here, we report the abundance and diversity of gut methanogenic Archaea in a multi-ethnic cohort of healthy Singaporeans by using a concerted approach of metagenomic sequencing, 16S rRNA gene amplicon sequencing, and quantitative PCR. Our results indicate a mutual exclusion of Methanobrevibacter species, i.e., the highly prevalent Methanobrevibacter smithii and the less prevalent Candidatus Methanobrevibacter intestini in more than 80% of the samples when using an amplicon sequencing-based approach. Leveraging on this finding, we were able to select a fecal sample to isolate a representative strain, TLL-48-HuF1, for Candidatus Methanobrevibacter intestini. The analyzed physiological parameters of M. smithii DSM 861T and strain TLL-48-HuF1 suggest high similarity of the two species. Comparative genome analysis and the mutual exclusion of the Methanobrevibacter species indicate potentially different niche adaptation strategies in the human host, which may support the designation of Candidatus M. intestini as a novel species. IMPORTANCE Methanogens are important hydrogen consumers in the gut and are associated with differing host health. Here, we determine the prevalence and abundance of archaeal species in the guts of a multi-ethnic cohort of healthy Singapore residents. While Methanobrevibacter smithii is the most prevalent and abundant methanogen in the human gut of local subjects, the recently proposed Candidatus Methanobrevibacter intestini is the abundant methanogen in a minority of individuals that harbor them. The observed potential mutual exclusion of M. smithii and Ca. M. intestini provides further support to the proposal that the two physiologically similar strains may belong to different Methanobrevibacter species.

Genome-scale model reconstruction of the methylotrophic yeast Ogataea polymorpha.

BACKGROUND: Ogataea polymorpha is a thermotolerant, methylotrophic yeast with significant industrial applications. While previously mainly used for protein synthesis, it also holds promise for producing platform chemicals. O. polymorpha has the distinct advantage of using methanol as a substrate, which could be potentially derived from carbon capture and utilization streams. Full development of the organism into a production strain and estimation of the metabolic capabilities require additional strain design, guided by metabolic modeling with a genome-scale metabolic model. However, to date, no genome-scale metabolic model is available for O. polymorpha. RESULTS: To overcome this limitation, we used a published reconstruction of the closely related yeast Komagataella phaffii as a reference and corrected reactions based on KEGG and MGOB annotation. Additionally, we conducted phenotype microarray experiments to test the suitability of 190 substrates as carbon sources. Over three-quarter of the substrate use was correctly reproduced by the model and 27 new substrates were added, that were not present in the K. phaffii reference model. CONCLUSION: The developed genome-scale metabolic model of O. polymorpha will support the engineering of synthetic metabolic capabilities and enable the optimization of production processes, thereby supporting a sustainable future methanol economy.

Atopic dermatitis microbiomes stratify into ecologic dermotypes enabling microbial virulence and disease severity.

BACKGROUND: Atopic dermatitis (AD) is a common skin disease affecting up to 20% of the global population, with significant clinical heterogeneity and limited information about molecular subtypes and actionable biomarkers. Although alterations in the skin microbiome have been described in subjects with AD during progression to flare state, the prognostic value of baseline microbiome configurations has not been explored. OBJECTIVE: Our aim was to identify microbial signatures on AD skin that are predictive of disease fate. METHODS: Nonlesional skin of patients with AD and healthy control subjects were sampled at 2 time points separated by at least 4 weeks. Using whole metagenome analysis of skin microbiomes of patients with AD and control subjects (n = 49 and 189 samples), we identified distinct microbiome configurations (dermotypes A and B). Blood was collected for immunophenotyping, and skin surface samples were analyzed for correlations with natural moisturizing factors and antimicrobial peptides. RESULTS: Dermotypes were robust and validated across 2 additional cohorts (63 individuals), with strong enrichment of subjects with AD in dermotype B. Dermotype B was characterized by reduced microbial richness, depletion of Cutibacterium acnes, Dermacoccus and Methylobacterium species, individual-specific outlier abundance of Staphylococcus species (eg, S epidermidis, S capitis, S aureus), and enrichment in metabolic pathways (eg, branched chain amino acids and arginine biosynthesis) and virulence genes (eg, ß-toxin, δ-toxin) that defined a pathogenic ecology. Skin surface and circulating host biomarkers exhibited a distinct microbial-associated signature that was further reflected in more severe itching, frequent flares, and increased disease severity in patients harboring the dermotype B microbiome. CONCLUSION: We report distinct clusters of microbial profiles that delineate the role of microbiome configurations in AD heterogeneity, highlight a mechanism for ongoing inflammation, and provide prognostic utility toward microbiome-based disease stratification.

Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut.

Loss of diversity in the gut microbiome can persist for extended periods after antibiotic treatment, impacting microbiome function, antimicrobial resistance and probably host health. Despite widespread antibiotic use, our understanding of the species and metabolic functions contributing to gut microbiome recovery is limited. Using data from 4 discovery cohorts in 3 continents comprising >500 microbiome profiles from 117 individuals, we identified 21 bacterial species exhibiting robust association with ecological recovery post antibiotic therapy. Functional and growth-rate analysis showed that recovery is supported by enrichment in specific carbohydrate-degradation and energy-production pathways. Association rule mining on 782 microbiome profiles from the MEDUSA database enabled reconstruction of the gut microbial 'food web', identifying many recovery-associated bacteria as keystone species, with the ability to use host- and diet-derived energy sources, and support repopulation of other gut species. Experiments in a mouse model recapitulated the ability of recovery-associated bacteria (Bacteroides thetaiotaomicron and Bifidobacterium adolescentis) to promote recovery with synergistic effects, providing a boost of two orders of magnitude to microbial abundance in early time points and faster maturation of microbial diversity. The identification of specific species and metabolic functions promoting recovery opens up opportunities for rationally determining pre- and probiotic formulations offering protection from long-term consequences of frequent antibiotic usage.

Investigating metabolic interactions in a microbial co-culture through integrated modelling and experiments.

Microbial co-cultures have been used in several biotechnological applications. Within these co-cultures, the microorganisms tend to interact with each other and perform complex actions. Investigating metabolic interactions in microbial co-cultures is crucial in designing microbial consortia. Here, we present a pipeline integrating modelling and experimental approaches to understand metabolic interactions between organisms in a community. We define a new index named "Metabolic Support Index (MSI)", which quantifies the benefits derived by each organism in the presence of the other when grown as a co-culture. We computed MSI for several experimentally demonstrated co-cultures and showed that MSI, as a metric, accurately identifies the organism that derives the maximum benefit. We also computed MSI for a commonly used yeast co-culture consisting of Saccharomyces cerevisiae and Pichia stipitis and observed that the latter derives higher benefit from the interaction. Further, we designed two-stage experiments to study mutual interactions and showed that P. stipitis indeed derives the maximum benefit from the interaction, as shown from our computational predictions. Also, using our previously developed computational tool MetQuest, we identified all the metabolic exchanges happening between these organisms by analysing the pathways spanning the two organisms. By analysing the HPLC profiles and studying the isotope labelling, we show that P. stipitis consumes the ethanol produced by S. cerevisiae when grown on glucose-rich medium under aerobic conditions, as also indicated by our in silico pathway analyses. Our approach represents an important step in understanding metabolic interactions in microbial communities through an integrated computational and experimental workflow.

Enumerating all possible biosynthetic pathways in metabolic networks.

Exhaustive identification of all possible alternate pathways that exist in metabolic networks can provide valuable insights into cellular metabolism. With the growing number of metabolic reconstructions, there is a need for an efficient method to enumerate pathways, which can also scale well to large metabolic networks, such as those corresponding to microbial communities. We developed MetQuest, an efficient graph-theoretic algorithm to enumerate all possible pathways of a particular size between a given set of source and target molecules. Our algorithm employs a guided breadth-first search to identify all feasible reactions based on the availability of the precursor molecules, followed by a novel dynamic-programming based enumeration, which assembles these reactions into pathways of a specified size producing the target from the source. We demonstrate several interesting applications of our algorithm, ranging from identifying amino acid biosynthesis pathways to identifying the most diverse pathways involved in degradation of complex molecules. We also illustrate the scalability of our algorithm, by studying large graphs such as those corresponding to microbial communities, and identify several metabolic interactions happening therein. MetQuest is available as a Python package, and the source codes can be found at https://github.com/RamanLab/metquest .

Critical assessment of genome-scale metabolic networks: the need for a unified standard.

Genome-scale metabolic networks have been reconstructed for several organisms. These metabolic networks provide detailed information about the metabolism inside the cells, coupled with the genomic, proteomic and thermodynamic information. These networks are widely simulated using 'constraint-based' modelling techniques and find applications ranging from strain improvement for metabolic engineering to prediction of drug targets in pathogenic organisms. Components of these metabolic networks are represented in multiple file formats and also using different markup languages, with varying levels of annotations; this leads to inconsistencies and increases the complexities in comparing and analysing reconstructions on multiple platforms. In this work, we critically examine nearly 100 published genome-scale metabolic networks and their corresponding constraint-based models and discuss various issues with respect to model quality. One of the major concerns is the lack of annotations using standard identifiers that can uniquely describe several components such as metabolites, genes, proteins and reactions. We also find that many models do not have complete information regarding constraints on reactions fluxes and objective functions for carrying out simulations. Overall, our analysis highlights the need for a widely acceptable standard for representing constraint-based models. A rigorous standard can help in streamlining the process of reconstruction and improve the quality of reconstructed metabolic models.