Influence of Environmental Factors on Salivary Microbiota and Their Metabolic Pathway: Next-Generation Sequencing Approach.

The current study aimed to investigate the effect of periodontitis and long-term heavy metal (HM) exposure on the salivary microbiome. The patients were divided into four groups as Wu Wei control (WWC) group involved healthy individuals, Wu Wei periodontitis (WWP) patients having periodontitis, Jing Chang with metal pollution periodontally healthy individuals (JCP), and Kuang periodontitis (KP). The most abundant bacteria identified at the phylum level in the WWC group were Bacteroides, Firmicutes, and Fusobacteria. Firmicutes were observed in a significantly higher proportion in the KP group than in the WWC, WWP, and JCP. At the genus level, the WWC has major dominating bacterial genera (such as Leptotrichia, Neisseria, and Fusobacterium) which were similar to WWP and KP group. The significant difference (p < 0.05) was found in alpha diversity while in beta diversity, the significant (p = 0.005) results were found among the four groups. The correlation of oral microbiota revealed that HMs present in the soil (Cr, Ni, and Cu) are associated with the growth of Capnocytophaga, Selenomonas, Aggregatibacter, and Campylobacter. The bacterial functions in the KP group were higher in translation and nucleotide metabolism than in the WWP group. This demonstrated that long-term exposure to HMs can influence the salivary microbiota which can alter the functioning, and diversity of bacteria.

Role of oral and gut microbiota in childhood obesity.

Childhood obesity not only causes damage to children's respiratory, cardiovascular, endocrine, motor, and other systems but also is a significant risk factor for metabolic diseases such as obesity in adulthood, which has become one of the serious public health problems worldwide. The etiology and pathogenesis of obesity are complex. In addition to genetic and lifestyle factors, recent studies have found that the microbes in the digestive tract play a crucial role in the occurrence and development of obesity. Among them, the gut microbiota has been confirmed to be one of the important pathogenic factors of obesity, which can mediate the occurrence and development of obesity by interfering with the balance of host energy metabolism and inducing low-grade chronic inflammation throughout the host. Targeting the gut microbiota to treat obesity through various methods such as fecal microbiota transplantation, dietary intervention, and probiotic supplementation has become a research hotspot in obesity treatment. In addition, the oral microbiota is also considered closely related to the occurrence and development of obesity due to its regulatory effect on the balance of gut microbiota. Exploring the relationship between oral and gut microbiota and childhood obesity elucidates the pathogenesis and treatment concepts of childhood obesity from a new perspective. It may provide new methods for the prevention and treatment of childhood obesity in the future.

Protein disulfide isomerase A3 as novel biomarker for endometrial cancer.

Objective: This study aims to investigate the potential of PDIA3 as a novel prognostic biomarker and therapeutic target for Endometrial Cancer (EC) with the ultimate goal of improving survival rates in EC patients. Methods: This study employed a combination of public database analysis and clinical tissue sample assays. The analysis included comparing the gene expression of PDIA3 between EC and adjacent paracancerous tissues, investigating this expression status using qPCR and immunohistochemistry (IHC) assays, studying the correlation of expression with different parameters using Chi-square test, Cox Regression, and log-rank test, as well as exploring the PDIA3-related immune infiltration and metabolic pathway using TIMER and GSEA. Results: The analysis of public datasets revealed that PDIA3 mRNA and protein expression was significantly higher in EC tissues compared to adjacent tissues (P = 4.1e-03, P = 1.95e-14, and P = 1.6e-27, respectively). The qPCR analysis supported this finding (P = 0.029). IHC analysis revealed a significant increase in PDIA3 expression in endometrial cancer (EC) tissues compared to adjacent normal tissues (P = 0.01). Furthermore, PDIA3 expression showed significant correlations with cancer stage and tumor grade. Multivariate Cox regression analysis suggested that the PDIA3 gene holds promise as a prognostic factor for EC patients (HR = 0.47, 95% CI [0.27, 0.82], P = 0.008). The results from TIMER demonstrated a positive correlation between PDIA3 and tumor-infiltrating CD8 T cells and macrophages, and a negative correlation with tumor-infiltrating CD4 T cells. Additionally, the GSEA results indicated that PDIA3 overexpression was associated with various metabolic processes in EC patients. Conclusion: PDIA3 has been validated as a potential biomarker for EC, and its expression is further associated with pathological staging and prognosis.

PSRR: A Web Server for Predicting the Regulation of miRNAs Expression by Small Molecules.

Background: MicroRNAs (miRNAs) play key roles in a variety of pathological processes by interacting with their specific target mRNAs for translation repression and may function as oncogenes (oncomiRs) or tumor suppressors (TSmiRs). Therefore, a web server that could predict the regulation relations between miRNAs and small molecules is expected to achieve implications for identifying potential therapeutic targets for anti-tumor drug development. Methods: Upon obtaining positive/known small molecule-miRNA regulation pairs from SM2miR, we generated a multitude of high-quality negative/unknown pairs by leveraging similarities between the small molecule structures. Using the pool of the positive and negative pairs, we created the Dataset1 and Dataset2 datasets specific to up-regulation and down-regulation pairs, respectively. Manifold machine learning algorithms were then employed to construct models of predicting up-regulation and down-regulation pairs on the training portion of pairs in Dataset1 and Dataset2, respectively. Prediction abilities of the resulting models were further examined by discovering potential small molecules to regulate oncogenic miRNAs identified from miRNA sequencing data of endometrial carcinoma samples. Results: The random forest algorithm outperformed four machine-learning algorithms by achieving the highest AUC values of 0.911 for the up-regulation model and 0.896 for the down-regulation model on the testing datasets. Moreover, the down-regulation and up-regulation models yielded the accuracy values of 0.91 and 0.90 on independent validation pairs, respectively. In a case study, our model showed highly-reliable results by confirming all top 10 predicted regulation pairs as experimentally validated pairs. Finally, our predicted binding affinities of oncogenic miRNAs and small molecules bore a close resemblance to the lowest binding energy profiles using molecular docking. Predictions of the final model are freely accessible through the PSRR web server at https://rnadrug.shinyapps.io/PSRR/. Conclusion: Our study provides a novel web server that could effectively predict the regulation of miRNAs expression by small molecules.