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Biology subjects

Mommers, M.

Publications and source records attributed to Mommers, M..

2 recordsLinked to original sources

Mutualistic interactions between Escherichia coli and Bifidobacterium bifidum enable degradation of human milk oligosaccharides in healthy infants

The development of the human gut microbiota during infancy is marked by frequent colonization of Enterobacteriaceae, a bacterial family notoriously associated with various diseases. Yet, despite their prominence in the absence of illness, their exact ecological role during healthy maturation of the infant gut remains poorly explored. Here, we analyse longitudinal stool samples from healthy, term-born, breastfed neonates (n=41) at two, six, and eleven months post-delivery, as well as microbiota of related mothers (n=30) with shotgun metagenomic sequencing, complemented by novel computational approaches and experimentation. Strain-resolved profiling indicates that dominant Bifidobacterium are frequently shared between infants and parenting mothers, while Escherichia coli originate from other sources, yet often persist within individuals. Despite their ecological differences, these genera co-exist, and both display evolutionary adaptations related to the utilization of human milk oligosaccharide (HMO) degradation products. We demonstrate that interactions between E. coli and Bifidobacterium bifidum are mutualistic in co-culture, where E. coli supplies cysteine to its auxotrophic partner, facilitating the cooperative degradation of 2'-fucosyllactose (2'FL), the predominant HMO. In turn, the liberated monosaccharides support E. coli proliferation and niche occupation. These findings reveal a fundamental cross-feeding interaction during development of healthy infant gut microbiota.

microbiology↗

Capturing the dynamics of microbiomes using individual-specific networks

BackgroundLongitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains a daunting task. The vast majority of statistical tools and methods available to study the microbiota are based upon cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on how to incorporate individual-specific microbial associations in temporal analyses when the focus lies on microbial interactions. ResultsHere, we propose a novel data analysis framework, called MNDA, to uncover taxon neighbourhood dynamics that combines representation learning and individual-specific microbiome co-occurrence networks. We show that tracking local neighbourhood dynamics in microbiome interaction or co-occurrence networks can yield complementary information to standard approaches that only use microbial abundances or pairwise microbial interactions. We use cohort data on infants for whom microbiome data was available at 6 and 9 months after birth, as well as information on mode of delivery and diet changes over time. In particular, MNDA-based prediction models outperform traditional prediction models based on individual-specific abundances, and enable the detection of microbes whose neighbourhood dynamics are informative of clinical variables. We further show that similarity analyses of individuals based on microbial neighbourhood dynamics can be used to find subpopulations of individuals with potential relevance to clinical practice. The annotated source code for the MNDA framework can be downloaded from: https://github.com/H2020TranSYS/microbiome_dynamics ConclusionsMNDA extracts information from matched microbiome profiles and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.

microbiology↗