Integrated salivary microbiome and metabolome profiling reveals ecological and functional alterations in severe early childhood cariesOriginal paper
Karen Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease, four years before the first published case study.
Read MoreWhat was studied?
Researchers compared the salivary microbiome and metabolome of 30 children with severe early childhood caries (S-ECC) against 30 caries-free children. The goal was to characterize ecological and metabolic shifts underlying S-ECC and identify candidate biomarkers.
How was it studied?
Saliva samples underwent high-throughput 16S rRNA gene sequencing paired with untargeted metabolomics. Investigators identified differential taxa and metabolites, ran KEGG pathway enrichment, and performed integrated correlation analysis linking key bacteria to specific metabolites.
What did they find?
S-ECC saliva was enriched in Rothia, Lautropia, Lactobacillus, Achromobacter, Streptococcus mutans, Prevotella histicola, and Lachnoanaerobaculum saburreum, while Bergeyella and Acinetobacter were more abundant in caries-free children. Of 4,325 detected metabolites, 1,226 differed significantly, with amino acid metabolism metabolites (phenylalanine, tyrosine, arginine, proline, D-amino acids, aminobenzoate) upregulated in S-ECC. S. mutans, P. histicola, and L. saburreum correlated positively with metabolites including succinic acid, 2-piperidone, D-3-phenyllactic acid, and L-valine.
Why it matters
The paired microbiome-metabolome signature, especially amino acid metabolism alterations, points to potential biomarkers for early detection and targeted intervention in S-ECC. The authors note the cross-sectional, single-time-point design limits conclusions about disease progression, calling for longitudinal follow-up.