bioRxivMicrobiologyPreprint
Food-Allergic Children Harbour a Reduced Oral Bacterial Load, Giving Misleading 16S rRNA Amplicon Profiles
Simona PanelliiD, Lodovico SterziiD, Clara Bonaiti, Miriam Acunzo, Enza D’AuriaiD, Gian Vincenzo ZuccottiiD, Francesco ComandatoreiD
Abstract
BackgroundFood allergies have become a major public health concern in Western countries due to their rising prevalence. The microbiota is increasingly recognized as a key factor in their pathogenesis. Using V3-V4 16S rRNA amplicon sequencing, our group previously reported alterations in the oral microbiota of children with food allergies compared to controls. Given growing awareness of biases associated with relative-abundance analyses in 16S rRNA studies, we re-analyzed the salivary DNA samples from our earlier work. MethodsTotal bacterial loads were quantified by qPCR using universal 16S rRNA primers, enabling the estimation of absolute abundances for each taxon in each sample.These data were then used to reassess key microbial community features and to directly compare conclusions derived from absolute versus relative abundance approaches. ResultsAllergic children exhibited a marked reduction in salivary bacterial load, a feature not detected in the original relative-abundance analysis. Incorporating absolute abundances also partially reshaped the previously defined taxonomic pictures. Similar distortions affected functional predictions, such as Short Chain Fatty Acids (SCFA)-producing capacity. ConclusionsOur data confirm that variations of the bacterial load across samples hinder efforts to associate microbiota features and disease phenotypes. We identify reduced oral bacterial load as a previously unrecognized characteristic of children with food allergies. This observation aligns with reports in other immune-mediated disorders, suggesting that overall microbiota depletion may represent a defining and potentially diagnostically feature of disease-associated ecosystems.
- organism: human
- assay: metagenomics
- research focus: clustering
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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