Biology subjects

von Mutius, E.

Publications and source records attributed to von Mutius, E..

2 recordsLinked to original sources

bioRxiv2026

Fragmented gut-airway microbial networks and airway Moraxella clusters in preschool wheeze

Early-life wheezing in children has been associated with microbial alterations along the gut-airway axis, yet studies simultaneously investigating bacterial communities in both compartments remain scarce. The aim of this cross-sectional exploratory pilot study (n=25) was to characterize and compare nasal and stool bacterial communities in preschool children aged 1-4 years with recurrent wheezing and healthy controls using 16S rRNA gene metabarcoding. Across participants, nasal and stool bacteriomes were highly individualized and taxonomically diverse. Overall richness, evenness, and community composition did not differ significantly between healthy children and wheezers in either compartment. However, wheezers displayed markedly higher within-group variability, particularly in nasal communities. Stratification based on microbiome similarity to healthy samples revealed increased Moraxella and reduced commensal genera including Prevotella spp. and Veillonella, along with lower richness and evenness (all p<0.001) in nasal samples with divergent bacterial communities. Stool alterations were more subtle but included trends toward reduced Bacteroides, Faecalibacterium, and Alistipes in wheezers more divergent from healthy controls. Community assembly in both compartments was largely governed by stochastic processes but accompanied by less complex and more fragmented bacterial interaction networks in wheezing children. Cross-compartment correlations were also altered, most prominently involving stool Lactococcus showing stronger and more numerous correlations with nasal taxa in wheezers than in healthy controls. Divergent wheezers exhibited distinct modular network structure and cross-compartment profiles, consistent with a differentiated microbial organization. Together, these findings suggest compartment-specific differences in microbial interaction patterns across the gut-airway axis in early-life wheezing, despite limited differences in overall community diversity. Take home messagePreschool wheezers showed fragmented gut-airway microbial networks and Moraxella-associated airway community stratification despite limited differences in overall diversity.

microbiology↗

bioRxiv2020

NetCoMi: Network Construction and Comparison for Microbiome Data in R

Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization, and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analyzing, and comparing microbial association networks from high-throughput sequencing data. Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analyzing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa, or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMis wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from childrens rooms between samples from two study centers (Ulm and Munich). AvailabilityA script with R code used for producing the examples shown in this manuscript are provided as Supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi.

bioinformatics↗