Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria.

Accumulating evidence suggests that cardiovascular disease (CVD) is associated with an altered gut microbiome. Our understanding of the underlying mechanisms has been hindered by lack of matched multi-omic data with diagnostic biomarkers. To comprehensively profile gut microbiome contributions to CVD, we generated stool metagenomics and metabolomics from 1,429 Framingham Heart Study participants. We identified blood lipids and cardiovascular health measurements associated with microbiome and metabolome composition. Integrated analysis revealed microbial pathways implicated in CVD, including flavonoid, γ-butyrobetaine, and cholesterol metabolism. Species from the Oscillibacter genus were associated with decreased fecal and plasma cholesterol levels. Using functional prediction and in vitro characterization of multiple representative human gut Oscillibacter isolates, we uncovered conserved cholesterol-metabolizing capabilities, including glycosylation and dehydrogenation. These findings suggest that cholesterol metabolism is a broad property of phylogenetically diverse Oscillibacter spp., with potential benefits for lipid homeostasis and cardiovascular health.

Linking microbial genes to plasma and stool metabolites uncovers host-microbial interactions underlying ulcerative colitis disease course.

Understanding the role of the microbiome in inflammatory diseases requires the identification of microbial effector molecules. We established an approach to link disease-associated microbes to microbial metabolites by integrating paired metagenomics, stool and plasma metabolomics, and culturomics. We identified host-microbial interactions correlated with disease activity, inflammation, and the clinical course of ulcerative colitis (UC) in the Predicting Response to Standardized Colitis Therapy (PROTECT) pediatric inception cohort. In severe disease, metabolite changes included increased dipeptides and tauro-conjugated bile acids (BAs) and decreased amino-acid-conjugated BAs in stool, whereas in plasma polyamines (N-acetylputrescine and N1-acetylspermidine) increased. Using patient samples and Veillonella parvula as a model, we uncovered nitrate- and lactate-dependent metabolic pathways, experimentally linking V. parvula expansion to immunomodulatory tryptophan metabolite production. Additionally, V. parvula metabolizes immunosuppressive thiopurine drugs through xdhA xanthine dehydrogenase, potentially impairing the therapeutic response. Our findings demonstrate that the microbiome contributes to disease-associated metabolite changes, underscoring the importance of these interactions in disease pathology and treatment.

Cytotoxic Th1 cells mushroom after fungal exposures in Crohn's disease.

Fungi are consistently enriched in inflamed intestines, with elusive effects on host immunity. In a recent issue of Nature Medicine, Martini et al. identify a subset of Th1 cells able to lyse the epithelium, enriched in Crohn's disease patient samples after fungal exposure.

Rationally-defined microbial consortia suppress multidrug-resistant proinflammatory Enterobacteriaceae via ecological control.

Persistent colonization and outgrowth of pathogenic organisms in the intestine may occur due to long-term antibiotic usage or inflammatory conditions, which perpetuate dysregulated immunity and tissue damage1,2. Gram-negative Enterobacteriaceae gut pathobionts are particularly recalcitrant to conventional antibiotic treatment3,4, though an emerging body of evidence suggests that manipulation of the commensal microbiota may be a practical alternative therapeutic strategy5-7. In this study, we rationally isolated and down-selected commensal bacterial consortia from healthy human stool samples capable of strongly and specifically suppressing intestinal Enterobacteriaceae. One of the elaborated consortia, consisting of 18 commensal strains, effectively controlled ecological niches by regulating gluconate availability, thereby reestablishing colonization resistance and alleviating antibiotic-resistant Klebsiella-driven intestinal inflammation in mice. Harnessing these microbial activities in the form of live bacterial therapeutics may represent a promising solution to combat the growing threat of proinflammatory, antimicrobial-resistant bacterial infection.

Spatial host-microbiome sequencing reveals niches in the mouse gut.

Mucosal and barrier tissues, such as the gut, lung or skin, are composed of a complex network of cells and microbes forming a tight niche that prevents pathogen colonization and supports host-microbiome symbiosis. Characterizing these networks at high molecular and cellular resolution is crucial for understanding homeostasis and disease. Here we present spatial host-microbiome sequencing (SHM-seq), an all-sequencing-based approach that captures tissue histology, polyadenylated RNAs and bacterial 16S sequences directly from a tissue by modifying spatially barcoded glass surfaces to enable simultaneous capture of host transcripts and hypervariable regions of the 16S bacterial ribosomal RNA. We applied our approach to the mouse gut as a model system, used a deep learning approach for data mapping and detected spatial niches defined by cellular composition and microbial geography. We show that subpopulations of gut cells express specific gene programs in different microenvironments characteristic of regional commensal bacteria and impact host-bacteria interactions. SHM-seq should enhance the study of native host-microbe interactions in health and disease.

HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery.

CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.

Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism.

The perinatal period represents a critical window for cognitive and immune system development, promoted by maternal and infant gut microbiomes and their metabolites. Here, we tracked the co-development of microbiomes and metabolomes from late pregnancy to 1 year of age using longitudinal multi-omics data from a cohort of 70 mother-infant dyads. We discovered large-scale mother-to-infant interspecies transfer of mobile genetic elements, frequently involving genes associated with diet-related adaptations. Infant gut metabolomes were less diverse than maternal but featured hundreds of unique metabolites and microbe-metabolite associations not detected in mothers. Metabolomes and serum cytokine signatures of infants who received regular-but not extensively hydrolyzed-formula were distinct from those of exclusively breastfed infants. Taken together, our integrative analysis expands the concept of vertical transmission of the gut microbiome and provides original insights into the development of maternal and infant microbiomes and metabolomes during late pregnancy and early life.

A distinct clade of Bifidobacterium longum in the gut of Bangladeshi children thrives during weaning.

The gut microbiome has an important role in infant health and development. We characterized the fecal microbiome and metabolome of 222 young children in Dhaka, Bangladesh during the first two years of life. A distinct Bifidobacterium longum clade expanded with introduction of solid foods and harbored enzymes for utilizing both breast milk and solid food substrates. The clade was highly prevalent in Bangladesh, present globally (at lower prevalence), and correlated with many other gut taxa and metabolites, indicating an important role in gut ecology. We also found that the B. longum clades and associated metabolites were implicated in childhood diarrhea and early growth, including positive associations between growth measures and B. longum subsp. infantis, indolelactate and N-acetylglutamate. Our data demonstrate geographic, cultural, seasonal, and ecological heterogeneity that should be accounted for when identifying microbiome factors implicated in and potentially benefiting infant development.

The influence of the gut microbiome on BCG-induced trained immunity.

BACKGROUND: The bacillus Calmette-Guérin (BCG) vaccine protects against tuberculosis and heterologous infections but elicits high inter-individual variation in specific and nonspecific, or trained, immune responses. While the gut microbiome is increasingly recognized as an important modulator of vaccine responses and immunity in general, its potential role in BCG-induced protection is largely unknown. RESULTS: Stool and blood were collected from 321 healthy adults before BCG vaccination, followed by blood sampling after 2 weeks and 3 months. Metagenomics based on de novo genome assembly reveals 43 immunomodulatory taxa. The nonspecific, trained immune response is detected by altered production of cytokines IL-6, IL-1ß, and TNF-α upon ex vivo blood restimulation with Staphylococcus aureus and negatively correlates with abundance of Roseburia. The specific response, measured by IFN-γ production upon Mycobacterium tuberculosis stimulation, is associated positively with Ruminococcus and Eggerthella lenta. The identified immunomodulatory taxa also have the strongest effects on circulating metabolites, with Roseburia affecting phenylalanine metabolism. This is corroborated by abundances of relevant enzymes, suggesting alternate phenylalanine metabolism modules are activated in a Roseburia species-dependent manner. CONCLUSIONS: Variability in cytokine production after BCG vaccination is associated with the abundance of microbial genomes, which in turn affect or produce metabolites in circulation. Roseburia is found to alter both trained immune responses and phenylalanine metabolism, revealing microbes and microbial products that may alter BCG-induced immunity. Together, our findings contribute to the understanding of specific and trained immune responses after BCG vaccination.

Gut microbiome-mediated metabolism effects on immunity in rural and urban African populations.

The human gut microbiota is increasingly recognized as an important factor in modulating innate and adaptive immunity through release of ligands and metabolites that translocate into circulation. Urbanizing African populations harbor large intestinal diversity due to a range of lifestyles, providing the necessary variation to gauge immunomodulatory factors. Here, we uncover a gradient of intestinal microbial compositions from rural through urban Tanzanian, towards European samples, manifested both in relative abundance and genomic variation observed in stool metagenomics. The rural population shows increased Bacteroidetes, led by Prevotella copri, but also presence of fungi. Measured ex vivo cytokine responses were significantly associated with 34 immunomodulatory microbes, which have a larger impact on circulating metabolites than non-significant microbes. Pathway effects on cytokines, notably TNF-α and IFN-γ, differential metabolome analysis and enzyme copy number enrichment converge on histidine and arginine metabolism as potential immunomodulatory pathways mediated by Bifidobacterium longum and Akkermansia muciniphila.

Democratized image analytics by visual programming through integration of deep models and small-scale machine learning.

Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange ( http://orange.biolab.si ) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.

scOrange-a tool for hands-on training of concepts from single-cell data analytics.

MOTIVATION: Single-cell RNA sequencing allows us to simultaneously profile the transcriptomes of thousands of cells and to indulge in exploring cell diversity, development and discovery of new molecular mechanisms. Analysis of scRNA data involves a combination of non-trivial steps from statistics, data visualization, bioinformatics and machine learning. Training molecular biologists in single-cell data analysis and empowering them to review and analyze their data can be challenging, both because of the complexity of the methods and the steep learning curve. RESULTS: We propose a workshop-style training in single-cell data analytics that relies on an explorative data analysis toolbox and a hands-on teaching style. The training relies on scOrange, a newly developed extension of a data mining framework that features workflow design through visual programming and interactive visualizations. Workshops with scOrange can proceed much faster than similar training methods that rely on computer programming and analysis through scripting in R or Python, allowing the trainer to cover more ground in the same time-frame. We here review the design principles of the scOrange toolbox that support such workshops and propose a syllabus for the course. We also provide examples of data analysis workflows that instructors can use during the training. AVAILABILITY AND IMPLEMENTATION: scOrange is an open-source software. The software, documentation and an emerging set of educational videos are available at http://singlecell.biolab.si.

Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins.

MOTIVATION: RNA binding proteins (RBPs) play important roles in post-transcriptional control of gene expression, including splicing, transport, polyadenylation and RNA stability. To model protein-RNA interactions by considering all available sources of information, it is necessary to integrate the rapidly growing RBP experimental data with the latest genome annotation, gene function, RNA sequence and structure. Such integration is possible by matrix factorization, where current approaches have an undesired tendency to identify only a small number of the strongest patterns with overlapping features. Because protein-RNA interactions are orchestrated by multiple factors, methods that identify discriminative patterns of varying strengths are needed. RESULTS: We have developed an integrative orthogonality-regularized nonnegative matrix factorization (iONMF) to integrate multiple data sources and discover non-overlapping, class-specific RNA binding patterns of varying strengths. The orthogonality constraint halves the effective size of the factor model and outperforms other NMF models in predicting RBP interaction sites on RNA. We have integrated the largest data compendium to date, which includes 31 CLIP experiments on 19 RBPs involved in splicing (such as hnRNPs, U2AF2, ELAVL1, TDP-43 and FUS) and processing of 3'UTR (Ago, IGF2BP). We show that the integration of multiple data sources improves the predictive accuracy of retrieval of RNA binding sites. In our study the key predictive factors of protein-RNA interactions were the position of RNA structure and sequence motifs, RBP co-binding and gene region type. We report on a number of protein-specific patterns, many of which are consistent with experimentally determined properties of RBPs. AVAILABILITY AND IMPLEMENTATION: The iONMF implementation and example datasets are available at https://github.com/mstrazar/ionmf CONTACT: : tomaz.curk@fri.uni-lj.si SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Yeast as a cell factory: current state and perspectives.

The yeast Saccharomyces cerevisiae is one of the oldest and most frequently used microorganisms in biotechnology with successful applications in the production of both bulk and fine chemicals. Yet, yeast researchers are faced with the challenge to further its transition from the old workhorse to a modern cell factory, fulfilling the requirements for next generation bioprocesses. Many of the principles and tools that are applied for this development originate from the field of synthetic biology and the engineered strains will indeed be synthetic organisms. We provide an overview of the most important aspects of this transition and highlight achievements in recent years as well as trends in which yeast currently lags behind. These aspects include: the enhancement of the substrate spectrum of yeast, with the focus on the efficient utilization of renewable feedstocks, the enhancement of the product spectrum through generation of independent circuits for the maintenance of redox balances and biosynthesis of common carbon building blocks, the requirement for accurate pathway control with improved genome editing and through orthogonal promoters, and improvement of the tolerance of yeast for specific stress conditions. The causative genetic elements for the required traits of the future yeast cell factories will be assembled into genetic modules for fast transfer between strains. These developments will benefit from progress in bio-computational methods, which allow for the integration of different kinds of data sets and algorithms, and from rapid advancement in genome editing, which will enable multiplexed targeted integration of whole heterologous pathways. The overall goal will be to provide a collection of modules and circuits that work independently and can be combined at will, depending on the individual conditions, and will result in an optimal synthetic host for a given production process.

A bistable genetic switch based on designable DNA-binding domains.

Bistable switches are fundamental regulatory elements of complex systems, ranging from electronics to living cells. Designed genetic toggle switches have been constructed from pairs of natural transcriptional repressors wired to inhibit one another. The complexity of the engineered regulatory circuits can be increased using orthogonal transcriptional regulators based on designed DNA-binding domains. However, a mutual repressor-based toggle switch comprising DNA-binding domains of transcription-activator-like effectors (TALEs) did not support bistability in mammalian cells. Here, the challenge of engineering a bistable switch based on monomeric DNA-binding domains is solved via the introduction of a positive feedback loop composed of activators based on the same TALE domains as their opposing repressors and competition for the same DNA operator site. This design introduces nonlinearity and results in epigenetic bistability. This principle could be used to employ other monomeric DNA-binding domains such as CRISPR for applications ranging from reprogramming cells to building digital biological memory.