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.

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.

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.

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.

Metagenomic assembly enables novel organism discovery from microbial communities, but from most metagenomes it can only capture few abundant organisms. Here, we present a method - MetaPhlAn 4 - to integrate information from both metagenome assemblies and microbial isolate genomes for improved and more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01M prokaryotic reference and metagenome-assembled genomes, we defined 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 proved 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 highlighted previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mice microbiomes, and showed that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains. MetaPhlAn 4 thus integrates the novelty of metagenomic assemblies with the sensitivity and fidelity of reference-based analyses, providing efficient metagenomic profiling of uncharacterized species and enabling deeper and more comprehensive microbiome biomarker detection.