StrainFLAIR: strain-level profiling of metagenomic samples using variation graphs

MeStanG-Resource for High-Throughput Sequencing Standard Data Sets Generation for Bioinformatic Methods Evaluation and Validation

Metagenomics analysis has enabled the measurement of the microbiome diversity in environmental samples without prior targeted enrichment. Functional and phylogenetic studies based on microbial diversity retrieved using HTS platforms have advanced from detecting known organisms and discovering unknown species to applications in disease diagnostics. Robust validation processes are essential for test reliability, requiring standard samples and databases deriving from real samples and in silico generated artificial controls. We propose a MeStanG as a resource for generating HTS Nanopore data sets to evaluate present and emerging bioinformatics pipelines. MeStanG allows samples to be designed with user-defined organism abundances expressed as number of reads, reference sequences, and predetermined or custom errors by sequencing profiles. The simulator pipeline was evaluated by analyzing its output mock metagenomic samples containing known read abundances using read mapping, genome assembly, and taxonomic classification on three scenarios: a bacterial community composed of nine different organisms, samples resembling pathogen-infected wheat plants, and a viral pathogen serial dilution sampling. The evaluation was able to report consistently the same organisms, and their read abundances as provided in the mock metagenomic sample design. Based on this performance and its novel capacity of generating exact number of reads, MeStanG can be used by scientists to develop mock metagenomic samples (artificial HTS data sets) to assess the diagnostic performance metrics of bioinformatic pipelines, allowing the user to choose predetermined or customized models for research and training.

Birthmode and environment-dependent microbiota transmission dynamics are complemented by breastfeeding during the first year

The composition and maturation of the early-life microbiota are modulated by a number of perinatal factors, whose interplay in relation to microbial vertical transmission remains inadequately elucidated. Using recent strain-tracking methodologies, we analyzed mother-to-infant microbiota transmission in two different birth environments: hospital-born (vaginal/cesarean) and home-born (vaginal) infants and their mothers. While delivery mode primarily explains initial compositional differences, place of birth impacts transmission timing-being early in homebirths and delayed in cesarean deliveries. Transmission patterns vary greatly across species and birth groups, yet certain species, like Bifidobacterium longum, are consistently vertically transmitted regardless of delivery setting. Strain-level analysis of B. longum highlights relevant and consistent subspecies replacement patterns mainly explained by breastfeeding practices, which drive changes in human milk oligosaccharide (HMO) degrading capabilities. Our findings highlight how delivery setting, breastfeeding duration, and other lifestyle preferences collectively shape vertical transmission, impacting infant gut colonization during early life.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Metagenomic Strain-Typing Combined with Isolate Sequencing Provides Increased Resolution of the Genetic Diversity of Campylobacter jejuni Carriage in Wild Birds

As the world's leading cause of human gastro-enteritis, the food- and waterborne pathogen Campylobacter needs to be intensively monitored through a One Health approach. Particularly, wild birds have been hypothesized to contribute to the spread of human clinical recurring C. jejuni genotypes across several countries. A major concern in studying epidemiological dynamics is resolving the large genomic diversity of strains circulating in the environment and various reservoirs, challenging to achieve with isolation techniques. Here, we applied a passive-filtration method to obtain isolates and in parallel recovered genotypes from metagenomic sequencing data from associated filter sweeps. For genotyping mixed strains, a reference-based computational workflow to predict allelic profiles of nine extended-MLST loci was utilized. We validated the pipeline by sequencing artificial mixtures of C. jejuni strains and observed the highest prediction accuracy when including obtained isolates as references. By analyzing metagenomic samples, we were able to detect over 20% additional genetic diversity and observed an over 50% increase in the potential to connect genotypes across wild-bird samples. With an optimized filtration method and a computational approach for genotyping strain mixtures, we provide the foundation for future studies assessing C. jejuni diversity in environmental and clinical settings at improved throughput and resolution.