The gut microbiota’s function in regulating health has seen it linked to disease progression in several cancers. However, there is limited research detailing its influence in breast cancer (BrCa). This study found that antibiotic-induced perturbation of the gut microbiota significantly increases tumor progression in multiple BrCa mouse models. Metagenomics highlights the common loss of several bacterial species following antibiotic administration. One such bacteria, Faecalibaculum rodentium, rescued this increased tumor growth. Single-cell transcriptomics identified an increased number of cells with a stromal signature in tumors, and subsequent histology revealed an increased abundance of mast cells in the tumor stromal regions. We show that administration of a mast cell stabilizer, cromolyn, rescues increased tumor growth in antibiotic treated animals but has no influence on tumors from control cohorts. These findings highlight that BrCa-microbiota interactions are different from other cancers studied to date and suggest new research avenues for therapy development.

There is strong interest in using the gut microbiome for Parkinson’s disease (PD) diagnosis and treatment. However, a consensus on PD-associated microbiome features and a multi-study assessment of their diagnostic value is lacking. Here, we present a machine learning meta-analysis of PD microbiome studies of unprecedented scale (4489 samples). Within most studies, microbiome-based machine learning models accurately classify PD patients (average AUC 71.9%). However, these models are study-specific and do not generalise well across other studies (average AUC 61%). Training models on multiple datasets improves their generalizability (average LOSO AUC 68%) and disease specificity as assessed against microbiomes from other neurodegenerative diseases. Moreover, meta-analysis of shotgun metagenomes delineates PD-associated microbial pathways potentially contributing to gut health deterioration and favouring the translocation of pathogenic molecules along the gut-brain axis. Strikingly, microbial pathways for solvent and pesticide biotransformation are enriched in PD. These results align with epidemiological evidence that exposure to these molecules increases PD risk and raise the question of whether gut microbes modulate their toxicity. Here, we offer the most comprehensive overview to date about the PD gut microbiome and provide future reference for its diagnostic and functional potential.

Eukaryotes are habitats for bacterial organisms where the host colonization and dispersal among individual hosts have consequences for the bacterial ecology and evolution. Vertical symbiont transmission leads to geographic isolation of the microbial population and consequently to genetic isolation of microbiotas from individual hosts. In contrast, the extent of geographic and genetic isolation of horizontally transmitted microbiota is poorly characterized. Here we show that chemosynthetic symbionts of individual Bathymodiolus brooksi mussels constitute genetically isolated subpopulations. The reconstruction of core genome-wide strains from high-resolution metagenomes revealed distinct phylogenetic clades. Nucleotide diversity and strain composition vary along the mussel life span and individual hosts show a high degree of genetic isolation. Our results suggest that the uptake of environmental bacteria is a restricted process in B. brooksi, where self-infection of the gill tissue results in serial founder effects during symbiont evolution. We conclude that bacterial colonization dynamics over the host life cycle is thus an important determinant of population structure and genome evolution of horizontally transmitted symbionts.

Fungi and the mycobiome are a fundamental part of the human microbiome that contributes to human health and development. Despite this, relatively little is known about the mycobiome of the preterm infant gut. Here, we have characterised faecal fungal communities present in 11 premature infants born with differing degrees of prematurity and mapped how the mycobiome develops during early infancy. Using an ITS1 sequencing-based approach, the preterm infant gut mycobiome was found to be often dominated by a single species, typically a yeast. Candida was the most abundant genus, with the pathobionts C.albicans and C.parapsilosis highly prevalent and persistent in these infants. Gestational maturity at birth affected the distribution and abundance of these Candida, with hospital-associated C.parapsilosis more prevalent and abundant in infants born at less than 31 weeks. Fungal diversity was lowest at 6 months, but increased with age and change of diet, with food-associated Saccharomycescerevisiae most abundant in infants post weaning. This study provides a first insight into the fungal communities present within the preterm infant gut, identifying distinctive features including the prominence of pathobiont species, and the influence age and environmental factors play in shaping the development of the mycobiome.

The taxonomic composition of microbial communities can be assessed using universal marker amplicon sequencing. The most common taxonomic markers are the 16S rDNA for bacterial communities and the internal transcribed spacer (ITS) region for fungal communities, but various other markers are used for barcoding eukaryotes. A crucial step in the bioinformatic analysis of amplicon sequences is the identification of representative sequences. This can be achieved using a clustering approach or by denoising raw sequencing reads. DADA2 is a widely adopted algorithm, released as an R library, that denoises marker-specific amplicons from next-generation sequencing and produces a set of representative sequences referred to as ‘Amplicon Sequence Variants’ (ASV). Here, we present Dadaist2, a modular pipeline, providing a complete suite for the analysis that ranges from raw sequencing reads to the statistics of numerical ecology. Dadaist2 implements a new approach that is specifically optimised for amplicons with variable lengths, such as the fungal ITS. The pipeline focuses on streamlining the data flow from the command line to R, with multiple options for statistical analysis and plotting, both interactive and automatic.