The initial colonization of the infant gut is a complex process that defines the foundation for a healthy microbiome development. Bifidobacterium longum is one of the first colonizers of newborns’ gut, playing a crucial role in the healthy development of both the host and its microbiome. However, B. longum exhibits significant genomic diversity, with subspecies (e.g., Bifidobacterium longum subsp. infantis and subsp. longum) displaying distinct ecological and metabolic strategies including differential capabilities to break down human milk glycans (HMGs). To promote healthy infant microbiome development, a good understanding of the factors governing infant microbiome dynamics is required.
We analyzed newly sequenced gut microbiome samples of mother-infant pairs from the Amsterdam Infant Microbiome Study (AIMS) and four publicly available datasets to identify important environmental and bifidobacterial features associated with the colonization success and succession outcomes of B. longum subspecies. Metagenome-assembled genomes (MAGs) were generated and assessed to identify characteristics of B. longum subspecies in relation to early-life gut colonization. We further implemented machine learning tools to identify significant features associated with B. longum subspecies abundance.
B. longum subsp. longum was the most abundant and prevalent gut Bifidobacterium at one month, being replaced by B. longum subsp. infantisat six months of age. By utilizing metagenome-assembled genomes (MAGs), we reveal significant differences between and within B. longumsubspecies in their potential to break down HMGs. We further combined strain-tracking, meta-pangenomics and machine learning to understand these abundance dynamics and found an interplay of priority effects, milk-feeding type and HMG-utilization potential to govern them across the first six months of life. We find higher abundances of B. longum subsp. longumin the maternal gut microbiome, vertical transmission, breast milk and a broader range of HMG-utilizing genes to promote its abundance at one month of age. Eventually, we find B. longum subsp. longum to be replaced by B. longum subsp. infantis at six months of age due to a combination of nutritional intake, HMG-utilization potential and a diminishment of priority effects.
Our results establish a strain-level ecological framework explaining early-life abundance dynamics of B. longum subspecies. We highlight the role of priority effects, nutrition and significant variability in HMG-utilization potential in determining the predictable colonization and succession trajectories of B. longum subspecies, with potential implications for promoting infant health and well-being.
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The human gut harbors native microbial communities, forming a highly complex ecosystem. Synthetic microbial communities (SynComs) of the human gut are an assembly of microorganisms isolated from human mucosa or fecal samples. In recent decades, the ever-expanding culturing capacity and affordable sequencing, together with advanced computational modeling, started a ‘‘golden age’’ for harnessing the beneficial potential of SynComs to fight gastrointestinal disorders, such as infections and chronic inflammatory bowel diseases.
As simplified and completely defined microbiota, SynComs offer a promising reductionist approach to understanding the multispecies and multikingdom interactions in the microbe–host-immune axis. However, there are still many challenges to overcome before we can precisely construct SynComs of designed function and efficacy that allow the translation of scientific findings to patients’ treatments. Here, we discussed the strategies used to design, assemble, and test a SynCom, and address the significant challenges, which are of microbiological, engineering, and translational nature, that stand in the way of using SynComs as live bacterial therapeutics.
The authors have developed tools for creating big, organized library (database) for microbiome data (germs that live in and on our bodies) that can be easily accessed and used by researchers. This will help scientists share information and come up with new ways to tackle health issues, while also following privacy laws and protecting people’s personal information. They use a special platform to build the database and create a helpful set of tools that even non-experts can use to understand and work with the data. Below is a technical summary of the work.
The article proposes the creation of a real-time FAIR (Findable, Accessible, Interoperable, Reusable) compliant database for the handling and storage of human microbiome and host-associated data. This database development pipeline aims to facilitate innovation and reduce costs in research by making standardized, transparent, and readily available (meta)data.
The authors discuss potential conflicts arising from privacy laws and possible human genome sequences in metagenome shotgun data and propose alternate pathways for achieving compliance in such cases. They identify sensitive microbiome data, such as DNA sequences or geolocalized metadata, and consider the role of GDPR data regulations. The database is implemented using an open-source development platform, Supabase, allowing researchers to access, upload, download, and interact with human microbiome data in a FAIR compliant manner. Additionally, a large language model (LLM) is deployed to enable knowledge dissemination and non-expert usage of the database.
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