Several studies have demonstrated that nuclear magnetic resonance (NMR) metabolic profiles can differentiate patients with caries from healthy individuals; however, these studies only identified individual metabolites. The present study aimed to identify a salivary metabolite biomarker panel for the diagnosis of early childhood caries (ECC). Saliva samples from children with and without caries were analyzed using NMR spectroscopy. Multivariate and univariate analyses were performed to identify the discriminating metabolites. Selected metabolites were further evaluated and used to detect ECC. The saliva samples of children with ECC were characterized based on the increased levels of formate, glycerophosphocholine, and lactate and reduced levels of alanine, glycine, isoleucine, lysine, proline, and tyrosine. The levels of these metabolites were significantly different from those in the control in the ECC subgroup according to caries severity and correlated with the number of decayed and filled teeth or surfaces. Subsequently, an optimal salivary metabolite biomarker panel comprising formate, lactate, proline, and glycine was developed. This panel exhibited a better diagnostic performance for ECC than a single metabolite. These results demonstrate that salivary metabolic signatures can reflect oral conditions associated with dental caries, thereby emphasizing the importance of distinct salivary metabolic profiles as potential biomarkers of ECC.

Vibrio harveyi is one of the pathogens that threaten the shrimp farming industry. However, metabolic changes induced by V. harveyi infection in shrimp remain unknown. In this study, we first conducted high resolution-magic angle spinning (HR-MAS) nuclear magnetic resonance (NMR)-based metabolomics studies on gill, hepatopancreas, and haemolymph of V. harveyi-infected white leg shrimp, Litopenaeus vannamei. Using multivariate statistical analysis, we observed a clear separation between the early (3 and 9 h post-injection (hpi)) and late phases (24, 72 and 144 hpi) of the infection in all tissues. Moreover, metabolic changes in response to V. harveyi infection were faster in the haemolymph in the early phase and significantly changed in the late phase of the infection in the gills. Extensive changes were observed in the hepatopancreas, with 24 hpi being the turning point of progression from early to late phase infection in the hepatopancreas. V. harveyi infection increased the energy demand in L. vannamei and the amino acid and carbohydrate metabolism pathways also exhibited significant changes depending on the tissue. Thus, each tissue displayed different metabolic changes, depending on the progress of the infection.

Background: Periodontitis (PT) is a multifactorial, chronic inflammatory disease that can have heterogeneous clinical presentations. The oral microbiome and its metabolites have been implicated as the causes and regulators of PT pathogenesis. In this study, we assessed the oral microbiome and its metabolome in PT patients to clarify the interactions between the microbiome and its metabolites.Methods: A total of 112 subjects were recruited. Buccal and supragingival samples were collected for microbiome analysis. Saliva samples were collected for metabolomic analyses. Microbiome and metabolome data were analyzed and further integrated for combined analysis using various bioinformatics approaches.Results: Oral metabolomic analysis identified 28 metabolites distinguishing the healthy (H) and PT groups. PT group were further clustered into two subgroups (PT_G1 and PT_G2) depending on metabolite profiles. Oral microbiome analysis revealed discriminatory bacterial species in the H, PT_G1, and PT_G2 microbiota. Interestingly, PT_G2 had significantly higher concentration of short chain fatty acids and higher abundance of pathogenic bacteria. Integrated analysis of the microbiome and metabolome showed close association.Conclusion: Our results provide evidence of a close interplay between the oral microbiome and metabolome. Multi-omics approach including microbiome and microbe-associated metabolites may serve as diagnostic biomarkers and enhance treatment prediction in periodontal disease.