No abstract

The oral microbiota has been observed to be influenced by cigarette smoking and linked to several human diseases. However, research on the effect of cigarette smoking on the oral microbiota has not been systematically conducted in the Chinese population. We profiled the oral microbiota of 316 healthy subjects in the Chinese population by 16S rRNA gene sequencing. The alpha diversity of oral microbiota was different between never smokers and smokers (P = 0.002). Several bacterial taxa were first reported to be associated with cigarette smoking by LEfSe analysis, including Moryella (q = 1.56E-04), Bulleidia (q = 1.65E-06), and Moraxella (q = 3.52E-02) at the genus level and Rothia dentocariosa (q = 1.55E-02), Prevotella melaninogenica (q = 8.48E-08), Prevotella pallens (q = 4.13E-03), Bulleidia moorei (q = 1.79E-06), Rothia aeria (q = 3.83E-06), Actinobacillus parahaemolyticus (q = 2.28E-04), and Haemophilus parainfluenzae (q = 4.82E-02) at the species level. Two nitrite-producing bacteria that can increase the acidity of the oral cavity, Actinomyces and Veillonella, were also enriched in smokers with FDR-adjusted q-values of 3.62E-06 and 1.10E-06, respectively. Notably, we observed that two acid production-related pathways, amino acid-related enzymes (q = 6.19E-05) and amino sugar and nucleotide sugar metabolism (q = 2.63E-06), were increased in smokers by PICRUSt analysis. Finally, the co-occurrence analysis demonstrated that smoker-enriched bacteria were significantly positively associated with each other and were negatively correlated with the bacteria decreased in smokers. Our results suggested that cigarette smoking may affect oral health by creating a different environment by altering bacterial abundance, connections among oral microbiota, and the microbiota and their metabolic function.

Polygenic risk scores (PRS) have the potential to identify individuals at risk of diseases, optimizing treatment, and predicting survival outcomes. Here, we construct and validate a genome-wide association study (GWAS) derived PRS for nasopharyngeal carcinoma (NPC), using a multi-center study of six populations (6 059 NPC cases and 7 582 controls), and evaluate its utility in a nested case-control study. We show that the PRS enables effective identification of NPC high-risk individuals (AUC = 0.65) and improves the risk prediction with the PRS incremental deciles in each population (Ptrend ranging from 2.79 × 10−7 to 4.79 × 10−44). By incorporating the PRS into EBV-serology-based NPC screening, the test’s positive predictive value (PPV) is increased from an average of 4.84% to 8.38% and 11.91% in the top 10% and 5% PRS, respectively. In summary, the GWAS-derived PRS, together with the EBV test, significantly improves NPC risk stratification and informs personalized screening.

The alteration in protein conformation not only affects the performance of its biological functions, but also leads to a variety of protein‐mediated diseases. Developing a sensitive strategy for protein detection and monitoring its conformation changes is of great significance for the diagnosis and treatment of protein conformation diseases. Herein, a plasmon‐enhanced fluorescence (PEF) sensor is developed, based on an aggregation‐induced emission (AIE) molecule to monitor conformational changes in protein, using prion protein as a model. Three anthracene derivatives with AIE characteristics are synthesized and a water‐miscible sulfonate salt of 9,10‐bis(2‐(6‐sulfonaphthalen‐2‐yl)vinyl)anthracene (BSNVA) is selected to construct the PEF–AIE sensor. The sensor is nearly non‐emissive when it is mixed with cellular prion protein while emits fluorescence when mixed with disease‐associated prion protein (PrPSc). The kinetic process of conformational conversion can be monitored through the fluorescence changes of the PEF–AIE sensor. By right of the amplified fluorescence signal, this PEF–AIE sensor can achieve a detection limit 10 pM lower than the traditional AIE probe and exhibit a good performance in human serum sample. Furthermore, molecular docking simulations suggest that BSNVA tends to dock in the β‐sheet structure of PrP by hydrophobic interaction between BSNVA and the exposed hydrophobic residues.