The person-to-person transmission landscape of the gut and oral microbiomes.

The human microbiome is an integral component of the human body and a co-determinant of several health conditions1,2. However, the extent to which interpersonal relations shape the individual genetic makeup of the microbiome and its transmission within and across populations remains largely unknown3,4. Here, capitalizing on more than 9,700 human metagenomes and computational strain-level profiling, we detected extensive bacterial strain sharing across individuals (more than 10 million instances) with distinct mother-to-infant, intra-household and intra-population transmission patterns. Mother-to-infant gut microbiome transmission was considerable and stable during infancy (around 50% of the same strains among shared species (strain-sharing rate)) and remained detectable at older ages. By contrast, the transmission of the oral microbiome occurred largely horizontally and was enhanced by the duration of cohabitation. There was substantial strain sharing among cohabiting individuals, with 12% and 32% median strain-sharing rates for the gut and oral microbiomes, and time since cohabitation affected strain sharing more than age or genetics did. Bacterial strain sharing additionally recapitulated host population structures better than species-level profiles did. Finally, distinct taxa appeared as efficient spreaders across transmission modes and were associated with different predicted bacterial phenotypes linked with out-of-host survival capabilities. The extent of microorganism transmission that we describe underscores its relevance in human microbiome studies5, especially those on non-infectious, microbiome-associated diseases.

Evaluating homophily in networks via HONTO (HOmophily Network TOol): a case study of chromosomal interactions in human PPI networks.

SUMMARY: It has been observed in different kinds of networks, such as social or biological ones, a typical behavior inspired by the general principle 'similarity breeds connections'. These networks are defined as homophilic as nodes belonging to the same class preferentially interact with each other. In this work, we present HONTO (HOmophily Network TOol), a user-friendly open-source Python3 package designed to evaluate and analyze homophily in complex networks. The tool takes in input from the network along with a partition of its nodes into classes and yields a matrix whose entries are the homophily/heterophily z-score values. To complement the analysis, the tool also provides z-score values of nodes that do not interact with any other node of the same class. Homophily/heterophily z-scores values are presented as a heatmap allowing a visual at-a-glance interpretation of results. AVAILABILITY AND IMPLEMENTATION: Tool's source code is available at https://github.com/cumbof/honto under the MIT license, installable as a package from PyPI (pip install honto) and conda-forge (conda install -c conda-forge honto), and has a wrapper for the Galaxy platform available on the official Galaxy ToolShed (Blankenberg et al., 2014) at https://toolshed.g2.bx.psu.edu/view/fabio/honto.

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses.

Data are the most important elements of bioinformatics: Computational analysis of bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments and therapies for patients. Bioinformatics and high-throughput biological data coming from different sources can even be more helpful, because each of these different data chunks can provide alternative, complementary information about a specific biological phenomenon, similar to multiple photos of the same subject taken from different angles. In this context, the integration of bioinformatics and high-throughput biological data gets a pivotal role in running a successful bioinformatics study. In the last decades, data originating from proteomics, metabolomics, metagenomics, phenomics, transcriptomics, and epigenomics have been labelled -omics data, as a unique name to refer to them, and the integration of these omics data has gained importance in all biological areas. Even if this omics data integration is useful and relevant, due to its heterogeneity, it is not uncommon to make mistakes during the integration phases. We therefore decided to present these ten quick tips to perform an omics data integration correctly, avoiding common mistakes we experienced or noticed in published studies in the past. Even if we designed our ten guidelines for beginners, by using a simple language that (we hope) can be understood by anyone, we believe our ten recommendations should be taken into account by all the bioinformaticians performing omics data integration, including experts.

An ontology-based approach for modelling and querying Alzheimer's disease data.

BACKGROUND: The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being "standardized" so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer's Disease (AD) in whose context specialized data collections such as the one by the Alzheimer's Disease Neuroimaging Initiative (ADNI) are maintained. METHODS: Ontologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner. RESULTS: We developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer's disease. CONCLUSIONS: The proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources.

IWTomics: testing high-resolution sequence-based 'Omics' data at multiple locations and scales.

Summary: With increased generation of high-resolution sequence-based 'Omics' data, detecting statistically significant effects at different genomic locations and scales has become key to addressing several scientific questions. IWTomics is an R/Bioconductor package (integrated in Galaxy) that, exploiting sophisticated Functional Data Analysis techniques (i.e. statistical techniques that deal with the analysis of curves), allows users to pre-process, visualize and test these data at multiple locations and scales. The package provides a friendly, flexible and complete workflow that can be employed in many genomic and epigenomic applications. Availability and implementation: IWTomics is freely available at the Bioconductor website (http://bioconductor.org/packages/IWTomics) and on the main Galaxy instance (https://usegalaxy.org/). Supplementary information: Supplementary data are available at Bioinformatics online.

TCGA2BED: extracting, extending, integrating, and querying The Cancer Genome Atlas.

BACKGROUND: Data extraction and integration methods are becoming essential to effectively access and take advantage of the huge amounts of heterogeneous genomics and clinical data increasingly available. In this work, we focus on The Cancer Genome Atlas, a comprehensive archive of tumoral data containing the results of high-throughout experiments, mainly Next Generation Sequencing, for more than 30 cancer types. RESULTS: We propose TCGA2BED a software tool to search and retrieve TCGA data, and convert them in the structured BED format for their seamless use and integration. Additionally, it supports the conversion in CSV, GTF, JSON, and XML standard formats. Furthermore, TCGA2BED extends TCGA data with information extracted from other genomic databases (i.e., NCBI Entrez Gene, HGNC, UCSC, and miRBase). We also provide and maintain an automatically updated data repository with publicly available Copy Number Variation, DNA-methylation, DNA-seq, miRNA-seq, and RNA-seq (V1,V2) experimental data of TCGA converted into the BED format, and their associated clinical and biospecimen meta data in attribute-value text format. CONCLUSIONS: The availability of the valuable TCGA data in BED format reduces the time spent in taking advantage of them: it is possible to efficiently and effectively deal with huge amounts of cancer genomic data integratively, and to search, retrieve and extend them with additional information. The BED format facilitates the investigators allowing several knowledge discovery analyses on all tumor types in TCGA with the final aim of understanding pathological mechanisms and aiding cancer treatments.

Time dynamics of protein complexes in the AD11 transgenic mouse model for Alzheimer's disease like pathology.

BACKGROUND: Many approaches exist to integrate protein-protein interaction data with other sources of information, most notably with gene co-expression data, to obtain information on network dynamics. It is of interest to look at groups of interacting gene products that form a protein complex. We were interested in applying new tools to the characterization of pathogenesis and dynamic events of an Alzheimer's-like neurodegenerative model, the AD11 mice, expressing an anti-NGF monoclonal antibody. The goal was to quantify the impact of neurodegeneration on protein complexes, by measuring the correlation between gene expression data by different metrics. RESULTS: Data were extracted from the gene expression profile of AD11 brain, obtained by Agilent microarray, at 1, 3, 6, 15 months of age. For genes coding proteins in complexes, the correlation matrix of pairwise expression was computed. The dynamics between correlation matrices at different time points was evaluated: paired T-test between average correlation levels and a normalized Euclidean distance with z-score. We unveiled a differential wiring of interactions in a set of complexes, whose network structure discriminates between transgenic and control mice. Furthermore, we analyzed the dynamics of gene expression values, by looking at changes in gene-to-gene correlation over time and identified those complexes that exhibit a different timedependent behaviour between transgenic and controls. The most significant changes in correlation dynamics are concentrated in the early stage of disease, with higher correlation in AD11 mice compared to controls. Many complexes go through dynamic changes over time, showing the role of the dysfunctional immunoproteasome, as early neurodegenerative disease event. Furthermore, this analysis shows key events in the neurodegeneration process of the AD11 model, by identifying significant differences in co-expression values of other complexes, such as parvulin complex, with a role in protein misfolding and proteostasis, and of complexes involved in transcriptional mechanisms. CONCLUSIONS: We have proposed a novel approach to analyze the network structure of protein complexes, by two different measures to evaluate the dynamics of gene-gene correlation matrices from gene expression profiles. The methodology was able to investigate the re-organization of interactions within protein complexes in the AD11 model of neurodegeneration.

A Perspective on Protein Structure Prediction Using Quantum Computers.

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

Neuroblastoma is associated with alterations in gut microbiome composition subsequent to maternal microbial seeding.

BACKGROUND: Neuroblastoma is the most frequent extracranial solid tumour in children, accounting for ∼15% of deaths due to cancer in childhood. The most common clinical presentation are abdominal tumours. An altered gut microbiome composition has been linked to multiple cancer types, and reported in murine models of neuroblastoma. Whether children with neuroblastoma display alterations in gut microbiome composition remains unexplored. METHODS: We assessed gut microbiome composition by shotgun metagenomic profiling in an observational cross-sectional study on 288 individuals, consisting of patients with a diagnosis of neuroblastoma at disease onset (N = 63), healthy controls matching the patients on the main covariates of microbiome composition (N = 94), healthy siblings of the patients (N = 13), mothers of patients (N = 59), and mothers of the controls (N = 59). We examined taxonomic and functional microbiome composition and mother-infant strain transmission patterns. FINDINGS: Patients with neuroblastoma displayed alterations in gut microbiome composition characterised by reduced microbiome richness, decreased relative abundances of 18 species (including Phocaeicola dorei and Bifidobacterium bifidum), enriched protein fermentation and reduced carbohydrate fermentation potential. Using machine learning, we could successfully discriminate patients from controls (AUC = 82%). Healthy siblings did not display such alterations but resembled the healthy control group. No significant differences in maternal microbiome composition nor mother-to-offspring transmission were detected. INTERPRETATION: Patients with neuroblastoma display alterations in taxonomic and functional gut microbiome composition, which cannot be traced to differential maternal seeding. Follow-up research should include investigating potential causal links. FUNDING: Italian Ministry of Health Ricerca Corrente and Ricerca Finalizzata 5 per mille (to MPonzoni); Fondazione Italiana Neuroblastoma (to MPonzoni); European Research Council (ERC-StG project MetaPG-716575 and ERC-CoG microTOUCH-101045015 to NS); the European H2020 program ONCOBIOME-825410 project (to NS); the National Cancer Institute of the National Institutes of Health 1U01CA230551 (to NS); the Premio Internazionale Lombardia e Ricerca 2019 (to NS); the MIUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) Bando 2017 Grant 2017J3E2W2 (to NS); EMBO ALTF 593-2020 and Knowledge Generation Project from the Spanish Ministry of Science and Innovation (PID2022-139328OA-I00) (to MV-C).

Homozygous EPRS1 missense variant causing hypomyelinating leukodystrophy-15 alters variant-distal mRNA m6A site accessibility.

Hypomyelinating leukodystrophy (HLD) is an autosomal recessive disorder characterized by defective central nervous system myelination. Exome sequencing of two siblings with severe cognitive and motor impairment and progressive hypomyelination characteristic of HLD revealed homozygosity for a missense single-nucleotide variant (SNV) in EPRS1 (c.4444 C > A; p.Pro1482Thr), encoding glutamyl-prolyl-tRNA synthetase, consistent with HLD15. Patient lymphoblastoid cell lines express markedly reduced EPRS1 protein due to dual defects in nuclear export and cytoplasmic translation of variant EPRS1 mRNA. Variant mRNA exhibits reduced METTL3 methyltransferase-mediated writing of N6-methyladenosine (m6A) and reduced reading by YTHDC1 and YTHDF1/3 required for efficient mRNA nuclear export and translation, respectively. In contrast to current models, the variant does not alter the sequence of m6A target sites, but instead reduces their accessibility for modification. The defect was rescued by antisense morpholinos predicted to expose m6A sites on target EPRS1 mRNA, or by m6A modification of the mRNA by METTL3-dCas13b, a targeted RNA methylation editor. Our bioinformatic analysis predicts widespread occurrence of SNVs associated with human health and disease that similarly alter accessibility of distal mRNA m6A sites. These results reveal a new RNA-dependent etiologic mechanism by which SNVs can influence gene expression and disease, consequently generating opportunities for personalized, RNA-based therapeutics targeting these disorders.

Population-level impacts of antibiotic usage on the human gut microbiome.

The widespread usage of antimicrobials has driven the evolution of resistance in pathogenic microbes, both increased prevalence of antimicrobial resistance genes (ARGs) and their spread across species by horizontal gene transfer (HGT). However, the impact on the wider community of commensal microbes associated with the human body, the microbiome, is less well understood. Small-scale studies have determined the transient impacts of antibiotic consumption but we conduct an extensive survey of ARGs in 8972 metagenomes to determine the population-level impacts. Focusing on 3096 gut microbiomes from healthy individuals not taking antibiotics we demonstrate highly significant correlations between both the total ARG abundance and diversity and per capita antibiotic usage rates across ten countries spanning three continents. Samples from China were notable outliers. We use a collection of 154,723 human-associated metagenome assembled genomes (MAGs) to link these ARGs to taxa and detect HGT. This reveals that the correlations in ARG abundance are driven by multi-species mobile ARGs shared between pathogens and commensals, within a highly connected central component of the network of MAGs and ARGs. We also observe that individual human gut ARG profiles cluster into two types or resistotypes. The less frequent resistotype has higher overall ARG abundance, is associated with certain classes of resistance, and is linked to species-specific genes in the Proteobacteria on the periphery of the ARG network.

Maternal and food microbial sources shape the infant microbiome of a rural Ethiopian population.

The human microbiome seeding starts at birth, when pioneer microbes are acquired mainly from the mother. Mode of delivery, antibiotic prophylaxis, and feeding method have been studied as modulators of mother-to-infant microbiome transmission, but other key influencing factors like modern westernized lifestyles with high hygienization, high-calorie diets, and urban settings, compared with non-westernized lifestyles have not been investigated yet. In this study, we explored the mother-infant sharing of characterized and uncharacterized microbiome members via strain-resolved metagenomics in a cohort of Ethiopian mothers and infants, and we compared them with four other cohorts with different lifestyles. The westernized and non-westernized newborns' microbiomes composition overlapped during the first months of life more than later in life, likely reflecting similar initial breast-milk-based diets. Ethiopian and other non-westernized infants shared a smaller fraction of the microbiome with their mothers than did most westernized populations, despite showing a higher microbiome diversity, and uncharacterized species represented a substantial fraction of those shared in the Ethiopian cohort. Moreover, we identified uncharacterized species belonging to the Selenomonadaceae and Prevotellaceae families specifically present and shared only in the Ethiopian cohort, and we showed that a locally produced fermented food, injera, can contribute to the higher diversity observed in the Ethiopian infants' gut with bacteria that are not part of the human microbiome but are acquired through fermented food consumption. Taken together, these findings highlight the fact that lifestyle can impact the gut microbiome composition not only through differences in diet, drug consumption, and environmental factors but also through its effect on mother-infant strain-sharing patterns.

Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4.

Metagenomic assembly enables new organism discovery from microbial communities, but it can only capture few abundant organisms from most metagenomes. Here we present MetaPhlAn 4, which integrates information from metagenome assemblies and microbial isolate genomes for more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01 M prokaryotic reference and metagenome-assembled genomes, we define unique marker genes for 26,970 species-level genome bins, 4,992 of them taxonomically unidentified at the species level. MetaPhlAn 4 explains ~20% more reads in most international human gut microbiomes and >40% in less-characterized environments such as the rumen microbiome and proves more accurate than available alternatives on synthetic evaluations while also reliably quantifying organisms with no cultured isolates. Application of the method to >24,500 metagenomes highlights previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mouse microbiomes and shows that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains.

MetaPhlAn 4 profiling of unknown species-level genome bins improves the characterization of diet-associated microbiome changes in mice.

Mouse models are key tools for investigating host-microbiome interactions. However, shotgun metagenomics can only profile a limited fraction of the mouse gut microbiome. Here, we employ a metagenomic profiling method, MetaPhlAn 4, which exploits a large catalog of metagenome-assembled genomes (including 22,718 metagenome-assembled genomes from mice) to improve the profiling of the mouse gut microbiome. We combine 622 samples from eight public datasets and an additional cohort of 97 mouse microbiomes, and we assess the potential of MetaPhlAn 4 to better identify diet-related changes in the host microbiome using a meta-analysis approach. We find multiple, strong, and reproducible diet-related microbial biomarkers, largely increasing those identifiable by other available methods relying only on reference information. The strongest drivers of the diet-induced changes are uncharacterized and previously undetected taxa, confirming the importance of adopting metagenomic methods integrating metagenomic assemblies for comprehensive profiling.

Outer Membrane Vesicles From The Gut Microbiome Contribute to Tumor Immunity by Eliciting Cross-Reactive T Cells.

A growing body of evidence supports the notion that the gut microbiome plays an important role in cancer immunity. However, the underpinning mechanisms remain to be fully elucidated. One attractive hypothesis envisages that among the T cells elicited by the plethora of microbiome proteins a few exist that incidentally recognize neo-epitopes arising from cancer mutations ("molecular mimicry (MM)" hypothesis). To support MM, the human probiotic Escherichia coli Nissle was engineered with the SIINFEKL epitope (OVA-E.coli Nissle) and orally administered to C57BL/6 mice. The treatment with OVA-E.coli Nissle, but not with wild type E. coli Nissle, induced OVA-specific CD8+ T cells and inhibited the growth of tumors in mice challenged with B16F10 melanoma cells expressing OVA. The microbiome shotgun sequencing and the sequencing of TCRs from T cells recovered from both lamina propria and tumors provide evidence that the main mechanism of tumor inhibition is mediated by the elicitation at the intestinal site of cross-reacting T cells, which subsequently reach the tumor environment. Importantly, the administration of Outer Membrane Vesicles (OMVs) from engineered E. coli Nissle, as well as from E. coli BL21(DE3)ΔompA, carrying cancer-specific T cell epitopes also elicited epitope-specific T cells in the intestine and inhibited tumor growth. Overall, our data strengthen the important role of MM in tumor immunity and assign a novel function of OMVs in host-pathogen interaction. Moreover, our results pave the way to the exploitation of probiotics and OMVs engineered with tumor specific-antigens as personalized mucosal cancer vaccines.

Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases.

Fecal microbiota transplantation (FMT) is highly effective against recurrent Clostridioides difficile infection and is considered a promising treatment for other microbiome-related disorders, but a comprehensive understanding of microbial engraftment dynamics is lacking, which prevents informed applications of this therapeutic approach. Here, we performed an integrated shotgun metagenomic systematic meta-analysis of new and publicly available stool microbiomes collected from 226 triads of donors, pre-FMT recipients and post-FMT recipients across eight different disease types. By leveraging improved metagenomic strain-profiling to infer strain sharing, we found that recipients with higher donor strain engraftment were more likely to experience clinical success after FMT (P = 0.017) when evaluated across studies. Considering all cohorts, increased engraftment was noted in individuals receiving FMT from multiple routes (for example, both via capsules and colonoscopy during the same treatment) as well as in antibiotic-treated recipients with infectious diseases compared with antibiotic-naïve patients with noncommunicable diseases. Bacteroidetes and Actinobacteria species (including Bifidobacteria) displayed higher engraftment than Firmicutes except for six under-characterized Firmicutes species. Cross-dataset machine learning predicted the presence or absence of species in the post-FMT recipient at 0.77 average AUROC in leave-one-dataset-out evaluation, and highlighted the relevance of microbial abundance, prevalence and taxonomy to infer post-FMT species presence. By exploring the dynamics of microbiome engraftment after FMT and their association with clinical variables, our study uncovered species-specific engraftment patterns and presented machine learning models able to predict donors that might optimize post-FMT specific microbiome characteristics for disease-targeted FMT protocols.

Intestinal Akkermansia muciniphila predicts clinical response to PD-1 blockade in patients with advanced non-small-cell lung cancer.

Aside from PD-L1 expression, biomarkers of response to immune checkpoint inhibitors (ICIs) in non-small-cell lung cancer (NSCLC) are needed. In a previous retrospective analysis, we documented that fecal Akkermansia muciniphila (Akk) was associated with clinical benefit of ICI in patients with NSCLC or kidney cancer. In the current study, we performed shotgun-metagenomics-based microbiome profiling in a large cohort of patients with advanced NSCLC (n = 338) treated with first- or second-line ICIs to prospectively validate the predictive value of fecal Akk. Baseline stool Akk was associated with increased objective response rates and overall survival in multivariate analyses, independent of PD-L1 expression, antibiotics, and performance status. Intestinal Akk was accompanied by a richer commensalism, including Eubacterium hallii and Bifidobacterium adolescentis, and a more inflamed tumor microenvironment in a subset of patients. However, antibiotic use (20% of cases) coincided with a relative dominance of Akk above 4.8% accompanied with the genus Clostridium, both associated with resistance to ICI. Our study shows significant differences in relative abundance of Akk that may represent potential biomarkers to refine patient stratification in future studies.

Genomic diversity and ecology of human-associated Akkermansia species in the gut microbiome revealed by extensive metagenomic assembly.

BACKGROUND: Akkermansia muciniphila is a human gut microbe with a key role in the physiology of the intestinal mucus layer and reported associations with decreased body mass and increased gut barrier function and health. Despite its biomedical relevance, the genomic diversity of A. muciniphila remains understudied and that of closely related species, except for A. glycaniphila, unexplored. RESULTS: We present a large-scale population genomics analysis of the Akkermansia genus using 188 isolate genomes and 2226 genomes assembled from 18,600 metagenomes from humans and other animals. While we do not detect A. glycaniphila, the Akkermansia strains in the human gut can be grouped into five distinct candidate species, including A. muciniphila, that show remarkable whole-genome divergence despite surprisingly similar 16S rRNA gene sequences. These candidate species are likely human-specific, as they are detected in mice and non-human primates almost exclusively when kept in captivity. In humans, Akkermansia candidate species display ecological co-exclusion, diversified functional capabilities, and distinct patterns of associations with host body mass. Analysis of CRISPR-Cas loci reveals new variants and spacers targeting newly discovered putative bacteriophages. Remarkably, we observe an increased relative abundance of Akkermansia when cognate predicted bacteriophages are present, suggesting ecological interactions. A. muciniphila further exhibits subspecies-level genetic stratification with associated functional differences such as a putative exo/lipopolysaccharide operon. CONCLUSIONS: We uncover a large phylogenetic and functional diversity of the Akkermansia genus in humans. This variability should be considered in the ongoing experimental and metagenomic efforts to characterize the health-associated properties of A. muciniphila and related bacteria.

Commensal Bifidobacterium Strains Enhance the Efficacy of Neo-Epitope Based Cancer Vaccines.

A large body of data both in animals and humans demonstrates that the gut microbiome plays a fundamental role in cancer immunity and in determining the efficacy of cancer immunotherapy. In this work, we have investigated whether and to what extent the gut microbiome can influence the antitumor activity of neo-epitope-based cancer vaccines in a BALB/c-CT26 cancer mouse model. Similarly to that observed in the C57BL/6-B16 model, Bifidobacterium administration per se has a beneficial effect on CT26 tumor inhibition. Furthermore, the combination of Bifidobacterium administration and vaccination resulted in a protection which was superior to vaccination alone and to Bifidobacterium administration alone, and correlated with an increase in the frequency of vaccine-specific T cells. The gut microbiome analysis by 16S rRNA gene sequencing and shotgun metagenomics showed that tumor challenge rapidly altered the microbiome population, with Muribaculaceae being enriched and Lachnospiraceae being reduced. Over time, the population of Muribaculaceae progressively reduced while the Lachnospiraceae population increased-a trend that appeared to be retarded by the oral administration of Bifidobacterium. Interestingly, in some Bacteroidales, Prevotella and Muribaculacee species we identified sequences highly homologous to immunogenic neo-epitopes of CT26 cells, supporting the possible role of "molecular mimicry" in anticancer immunity. Our data strengthen the importance of the microbiome in cancer immunity and suggests a microbiome-based strategy to potentiate neo-epitope-based cancer vaccines.