Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth

Immediately after birth, newborn babies experience rapid colonization by microorganisms from their mothers and the surrounding environment1. Diseases in childhood and later in life are potentially mediated by the perturbation of the colonization of the infant gut microbiota2. However, the effects of delivery via caesarean section on the earliest stages of the acquisition and development of the gut microbiota, during the neonatal period (≤1 month), remain controversial3,4. Here we report the disrupted transmission of maternal Bacteroides strains, and high-level colonization by opportunistic pathogens associated with the hospital environment (including Enterococcus, Enterobacter and Klebsiella species), in babies delivered by caesarean section. These effects were also seen, to a lesser extent, in vaginally delivered babies whose mothers underwent antibiotic prophylaxis and in babies who were not breastfed during the neonatal period. We applied longitudinal sampling and whole-genome shotgun metagenomic analysis to 1,679 gut microbiota samples (taken at several time points during the neonatal period, and in infancy) from 596 full-term babies born in UK hospitals; for a subset of these babies, we collected additional matched samples from mothers (175 mothers paired with 178 babies). This analysis demonstrates that the mode of delivery is a significant factor that affects the composition of the gut microbiota throughout the neonatal period, and into infancy. Matched large-scale culturing and whole-genome sequencing of over 800 bacterial strains from these babies identified virulence factors and clinically relevant antimicrobial resistance in opportunistic pathogens that may predispose individuals to opportunistic infections. Our findings highlight the critical role of the local environment in establishing the gut microbiota in very early life, and identify colonization with antimicrobial-resistance-containing opportunistic pathogens as a previously underappreciated risk factor in hospital births

The MITRE trial protocol: a study to evaluate the microbiome as a biomarker of efficacy and toxicity in cancer patients receiving immune checkpoint inhibitor therapy.

BACKGROUND: The gut microbiome is implicated as a marker of response to  immune checkpoint inhibitors (ICI) based on preclinical mouse models and preliminary observations in limited patient series. Furthermore, early studies suggest faecal microbial transfer may have therapeutic potential, converting ICI non-responders into responders. So far, identification of specific responsible bacterial taxa has been inconsistent, which limits future application. The MITRE study will explore and validate a microbiome signature in a larger scale prospective study across several different cancer types. METHODS: Melanoma, renal cancer and non-small cell lung cancer patients who are planned to receive standard immune checkpoint inhibitors are being recruited to the MITRE study. Longitudinal stool samples are collected prior to treatment, then at 6 weeks, 3, 6 and 12 months during treatment, or at disease progression/recurrence (whichever is sooner), as well as after a severe (≥grade 3 CTCAE v5.0) immune-related adverse event. Additionally, whole blood, plasma, buffy coat, RNA and peripheral blood mononuclear cells (PBMCs) is collected at similar time points and will be used for exploratory analyses. Archival tumour tissue, tumour biopsies at progression/relapse, as well as any biopsies from body organs collected after a severe toxicity are collected. The primary outcome measure is the ability of the microbiome signature to predict 1 year progression-free survival (PFS) in patients with advanced disease. Secondary outcomes include microbiome correlations with toxicity and other efficacy end-points. Biosamples will be used to explore immunological and genomic correlates. A sub-study will evaluate both COVID-19 antigen and antibody associations with the microbiome. DISCUSSION: There is an urgent need to identify biomarkers that are predictive of treatment response, resistance and toxicity to immunotherapy. The data generated from this study will both help inform patient selection for these drugs and provide information that may allow therapeutic manipulation of the microbiome to improve future patient outcomes. TRIAL REGISTRATION: NCT04107168 , ClinicalTrials.gov, registered 09/27/2019. Protocol V3.2 (16/04/2021)

The Mouse Gastrointestinal Bacteria Catalogue enables translation between the mouse and human gut microbiotas via functional mapping.

Funder: Royal SocietyHuman health and disease have increasingly been shown to be impacted by the gut microbiota, and mouse models are essential for investigating these effects. However, the compositions of human and mouse gut microbiotas are distinct, limiting translation of microbiota research between these hosts. To address this, we constructed the Mouse Gastrointestinal Bacteria Catalogue (MGBC), a repository of 26,640 high-quality mouse microbiota-derived bacterial genomes. This catalog enables species-level analyses for mapping functions of interest and identifying functionally equivalent taxa between the microbiotas of humans and mice. We have complemented this with a publicly deposited collection of 223 bacterial isolates, including 62 previously uncultured species, to facilitate experimental investigation of individual commensal bacteria functions in vitro and in vivo. Together, these resources provide the ability to identify and test functionally equivalent members of the host-specific gut microbiotas of humans and mice and support the informed use of mouse models in human microbiota research.Sir Henry Dale Fellowship jointly funded by Wellcome Trust and Royal Society [206245/Z/17/Z]. Rosetrees Trust [A2194]. Wellcome Trust [098051]

Structured machine learning methods for microbiology : mass spectrometry and high-throughput sequencing

L'utilisation des technologies haut débit est en train de changer aussi bien les pratiques que le paysage scientifique en microbiologie. D'une part la spectrométrie de masse a d'ores et déjà fait son entrée avec succès dans les laboratoires de microbiologie clinique. D'autre part, l'avancée spectaculaire des technologies de séquençage au cours des dix dernières années permet désormais à moindre coût et dans un temps raisonnable de caractériser la diversité microbienne au sein d'échantillons cliniques complexes. Aussi ces deux technologies sont pressenties comme les piliers de futures solutions de diagnostic. L'objectif de cette thèse est de développer des méthodes d'apprentissage statistique innovantes et versatiles pour exploiter les données fournies par ces technologies haut-débit dans le domaine du diagnostic in vitro en microbiologie. Le domaine de l'apprentissage statistique fait partie intégrante des problématiques mentionnées ci-dessus, au travers notamment des questions de classification d'un spectre de masse ou d'un “read” de séquençage haut-débit dans une taxonomie bactérienne.Sur le plan méthodologique, ces données nécessitent des développements spécifiques afin de tirer au mieux avantage de leur structuration inhérente: une structuration en “entrée” lorsque l'on réalise une prédiction à partir d'un “read” de séquençage caractérisé par sa composition en nucléotides, et un structuration en “sortie” lorsque l'on veut associer un spectre de masse ou d'un “read” de séquençage à une structure hiérarchique de taxonomie bactérienne.Using high-throughput technologies is changing scientific practices and landscape in microbiology. On one hand, mass spectrometry is already used in clinical microbiology laboratories. On the other hand, the last ten years dramatic progress in sequencing technologies allows cheap and fast characterization of microbial diversity in complex clinical samples. Consequently, the two technologies are approached in future diagnostics solutions. This thesis aims to play a part in new in vitro diagnostics (IVD) systems based on high-throughput technologies, like mass spectrometry or next generation sequencing, and their applications in microbiology.Because of the volume of data generated by these new technologies and the complexity of measured parameters, we develop innovative and versatile statistical learning methods for applications in IVD and microbiology. Statistical learning field is well-suited for tasks relying on high-dimensional raw data that can hardly be used by medical experts, like mass-spectrum classification or affecting a sequencing read to the right organism. Here, we propose to use additional known structures in order to improve quality of the answer. For instance, we convert a sequencing read (raw data) into a vector in a nucleotide composition space and use it as a structuredinput for machine learning approaches. We also add prior information related to the hierarchical structure that organizes the reachable micro-organisms (structured output)

Méthodes d’apprentissage structuré pour la microbiologie : spectrométrie de masse et séquençage haut-débit.

Using high-throughput technologies is changing scientific practices and landscape in microbiology. On one hand, mass spectrometry is already used in clinical microbiology laboratories. On the other hand, the last ten years dramatic progress in sequencing technologies allows cheap and fast characterization of microbial diversity in complex clinical samples. Consequently, the two technologies are approached in future diagnostics solutions. This thesis aims to play a part in new in vitro diagnostics (IVD) systems based on high-throughput technologies, like mass spectrometry or next generation sequencing, and their applications in microbiology.Because of the volume of data generated by these new technologies and the complexity of measured parameters, we develop innovative and versatile statistical learning methods for applications in IVD and microbiology. Statistical learning field is well-suited for tasks relying on high-dimensional raw data that can hardly be used by medical experts, like mass-spectrum classification or affecting a sequencing read to the right organism. Here, we propose to use additional known structures in order to improve quality of the answer. For instance, we convert a sequencing read (raw data) into a vector in a nucleotide composition space and use it as a structuredinput for machine learning approaches. We also add prior information related to the hierarchical structure that organizes the reachable micro-organisms (structured output).L'utilisation des technologies haut débit est en train de changer aussi bien les pratiques que le paysage scientifique en microbiologie. D'une part la spectrométrie de masse a d'ores et déjà fait son entrée avec succès dans les laboratoires de microbiologie clinique. D'autre part, l'avancée spectaculaire des technologies de séquençage au cours des dix dernières années permet désormais à moindre coût et dans un temps raisonnable de caractériser la diversité microbienne au sein d'échantillons cliniques complexes. Aussi ces deux technologies sont pressenties comme les piliers de futures solutions de diagnostic. L'objectif de cette thèse est de développer des méthodes d'apprentissage statistique innovantes et versatiles pour exploiter les données fournies par ces technologies haut-débit dans le domaine du diagnostic in vitro en microbiologie. Le domaine de l'apprentissage statistique fait partie intégrante des problématiques mentionnées ci-dessus, au travers notamment des questions de classification d'un spectre de masse ou d'un “read” de séquençage haut-débit dans une taxonomie bactérienne.Sur le plan méthodologique, ces données nécessitent des développements spécifiques afin de tirer au mieux avantage de leur structuration inhérente: une structuration en “entrée” lorsque l'on réalise une prédiction à partir d'un “read” de séquençage caractérisé par sa composition en nucléotides, et un structuration en “sortie” lorsque l'on veut associer un spectre de masse ou d'un “read” de séquençage à une structure hiérarchique de taxonomie bactérienne

On learning matrices with orthogonal columns or disjoint supports

16 pagesWe investigate new matrix penalties to jointly learn linear models with orthogonality constraints, generalizing the work of Xiao et al. [24] who proposed a strictly convex matrix norm for orthogonal trans- fer. We show that this norm converges to a particular atomic norm when its convexity parameter decreases, leading to new algorithmic solutions to minimize it. We also investigate concave formulations of this norm, corresponding to more aggressive strategies to induce orthogonality, and show how these penalties can also be used to learn sparse models with disjoint supports

Two microbiota subtypes identified in irritable bowel syndrome with distinct responses to the low FODMAP diet.

OBJECTIVE: Reducing FODMAPs (fermentable oligosaccharides, disaccharides, monosaccharides and polyols) can be clinically beneficial in IBS but the mechanism is incompletely understood. We aimed to detect microbial signatures that might predict response to the low FODMAP diet and assess whether microbiota compositional and functional shifts could provide insights into its mode of action. DESIGN: We used metagenomics to determine high-resolution taxonomic and functional profiles of the stool microbiota from IBS cases and household controls (n=56 pairs) on their usual diet. Clinical response and microbiota changes were studied in 41 pairs after 4 weeks on a low FODMAP diet. RESULTS: Unsupervised analysis of baseline IBS cases pre-diet identified two distinct microbiota profiles, which we refer to as IBSP (pathogenic-like) and IBSH (health-like) subtypes. IBSP microbiomes were enriched in Firmicutes and genes for amino acid and carbohydrate metabolism, but depleted in Bacteroidetes species. IBSH microbiomes were similar to controls. On the low FODMAP diet, IBSH and control microbiota were unaffected, but the IBSP signature shifted towards a health-associated microbiome with an increase in Bacteroidetes (p=0.009), a decrease in Firmicutes species (p=0.004) and normalisation of primary metabolic genes. The clinical response to the low FODMAP diet was greater in IBSP subjects compared with IBSH (p=0.02). CONCLUSION: 50% of IBS cases manifested a 'pathogenic' gut microbial signature. This shifted towards the healthy profile on the low FODMAP diet; and IBSP cases showed an enhanced clinical responsiveness to the dietary therapy. The effectiveness of FODMAP reduction in IBSP may result from the alterations in gut microbiota and metabolites produced. Microbiota signatures could be useful as biomarkers to guide IBS treatment; and investigating IBSP species and metabolic pathways might yield insights regarding IBS pathogenic mechanisms

Functional Microbiomics Reveals Alterations of the Gut Microbiome and Host Co‐Metabolism in Patients With Alcoholic Hepatitis

Alcohol-related liver disease is a major public health burden, and the gut microbiota is an important contributor to disease pathogenesis. The aim of the present study is to characterize functional alterations of the gut microbiota and test their performance for short-term mortality prediction in patients with alcoholic hepatitis. We integrated shotgun metagenomics with untargeted metabolomics to investigate functional alterations of the gut microbiota and host co-metabolism in a multicenter cohort of patients with alcoholic hepatitis. Profound changes were found in the gut microbial composition, functional metagenome, serum, and fecal metabolomes in patients with alcoholic hepatitis compared with nonalcoholic controls. We demonstrate that in comparison with single omics alone, the performance to predict 30-day mortality was improved when combining microbial pathways with respective serum metabolites in patients with alcoholic hepatitis. The area under the receiver operating curve was higher than 0.85 for the tryptophan, isoleucine, and methionine pathways as predictors for 30-day mortality, but achieved 0.989 for using the urea cycle pathway in combination with serum urea, with a bias-corrected prediction error of 0.083 when using leave-one-out cross validation. Conclusion: Our study reveals changes in key microbial metabolic pathways associated with disease severity that predict short-term mortality in our cohort of patients with alcoholic hepatitis