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Xu, V.

Publications and source records attributed to Xu, V..

2 recordsLinked to original sources

Longitudinal multi-omics along gingivitis development reveal a suboptimal-health gum state with periodontitis-like microbiome

Most adults experience episodes of gingivitis, which can progress to the irreversible, chronic state of periodontitis. However the mechanistic roles of plaque in gingivitis onset and progression to periodontitis remain elusive. Here, we integrated the longitudinal multi-omics data from plaque metagenome, metabolome and salivary cytokines in 40 adults who transit from naturally-occurring gingivitis (NG), to healthy gingivae (baseline) and then to experimental gingivitis (EG). During EG, rapid and consistent alterations in plaque microbiota, metabolites and salivary cytokines emerged as early as 24-72 hours after pause of oral hygiene, defining an asymptomatic sub-optimal health (SoH) stage. SoH also features a steep and synergetic decrease of plaque-derived betaine and Rothia spp., suggesting an anti-gum-inflammation mechanism by health-promoting microbial residents. Global, cross-cohort meta-analysis revealed a high Microbiome-based Periodontitis Index at SoH state, due to its convergent taxonomical and functional profiles towards those of periodontitis. In contrast, caries SoH features a microbial signature very distinct from caries. Thus SoH is a universal state of polymicrobial inflammations with disease-specific features, which is key to maintaining a disease-preventive plaque.

microbiology

Integrative omics data analysis uncovers biomarker genes and potential candidate drugs for G3 medulloblastoma

Medulloblastoma (MB) is the most common malignant brain tumor in infants and children. Four molecular subtypes of MB are recognized: WNT, SHH, Group 3 (G3), and Group 4 (G4). Compared with WNT and SHH subtypes, G3 MBs exhibit significantly worse outcomes and higher metastatic rates, and there is no effective treatment yet. Moreover, G3 and G4 MBs are much more common in boys than girls, i.e., sex bias, which also plays important roles in cancer prognosis and drug response. However, the molecular mechanism of G3 remains unclear, and there are no well-identified biomarker genes associated with these phenotypes, i.e., worse survival rate, higher metastasis rate, and sex bias. In this exploratory study, we aim to identify potential biomarkers associated with the three phenotypes using integrative analysis of gene expression, methylation and copy number variation datasets. In the results, we identified a set of biomarker genes and linked them into a network signature. The network signature showed better performance in the separation of G3 MB patients into subtypes with a significant difference in terms of the three phenotypes. To identify potentially effective drugs for G3 MBs, a set of drugs with diverse targets were prioritized, which can potentially inhibit the network signature. These drugs or combinations thereof might be effective for G3 treatment.

bioinformatics