Unique miRomics Expression Profiles in -Infected Mandibles during Periodontitis Using Machine Learning.

is a subgingival periodontal bacterium constituting the subgingival pathogenic polymicrobial milieu during periodontitis (PD). miRNAs play a pivotal role in maintaining periodontal tissue homeostasis at the transcriptional, post-transcriptional, and epigenetic levels. The aim of this study was to characterize the global microRNAs (miRNA, miR) expression kinetics in 8- and 16-week-old -infected C57BL/6J mouse mandibles and to identify the miRNA bacterial biomarkers of disease process at specific time points. We examined the differential expression (DE) of miRNAs in mouse mandibles ( = 10) using high-throughput NanoString nCounter miRNA expression panels, which provided significant advantages over specific candidate miRNA or pathway analyses. All the -infected mice at two specific time points showed bacterial colonization (100%) in the gingival surface, along with a significant increase in alveolar bone resorption (ABR) ( < 0.0001). We performed a NanoString analysis of specific miRNA signatures, miRNA target pathways, and gene network analysis. A total of 115 miRNAs were DE in the mandible tissue during 8 and 16 weeks The infection, compared with sham infection, and the majority (99) of DE miRNAs were downregulated. nCounter miRNA expression kinetics identified 67 downregulated miRNAs (e.g., miR-375, miR-200c, miR-200b, miR-34b-5p, miR-141) during an 8-week infection, whereas 16 upregulated miRNAs (e.g., miR-1902, miR-let-7c, miR-146a) and 32 downregulated miRNAs (e.g., miR-2135, miR-720, miR-376c) were identified during a 16-week infection. Two miRNAs, miR-375 and miR-200c, were highly downregulated with >twofold change during an 8-week infection. Six miRNAs in the 8-week infection (miR-200b, miR-141, miR-205, miR-423-3p, miR-141-3p, miR-34a-5p) and two miRNAs in the 16-week infection (miR-27a-3p, miR-15a-5p) that were downregulated have also been reported in the gingival tissue and saliva of periodontitis patients. This preclinical in vivo study identified -specific miRNAs (miR-let-7c, miR-210, miR-146a, miR-423-5p, miR-24, miR-218, miR-26b, miR-23a-3p) and these miRs have also been reported in the gingival tissues and saliva of periodontitis patients. Further, several DE miRNAs that are significantly upregulated (e.g., miR-101b, miR-218, miR-127, miR-24) are also associated with many systemic diseases such as atherosclerosis, Alzheimer's disease, rheumatoid arthritis, osteoarthritis, diabetes, obesity, and several cancers. In addition to DE analysis, we utilized the XGBoost (eXtreme Gradient boost) and Random Forest machine learning (ML) algorithms to assess the impact that the number of miRNA copies has on predicting whether a mouse is infected. XGBoost found that miR-339-5p was most predictive for mice infection at 16 weeks. miR-592-5p was most predictive for mice infection at 8 weeks and also when the 8-week and 16-week results were grouped together. Random Forest predicted miR-592 as most predictive at 8 weeks as well as the combined 8-week and 16-week results, but miR-423-5p was most predictive at 16 weeks. In conclusion, the expression levels of miR-375 and miR-200c family differed significantly during disease process, and these miRNAs establishes a link between and development of periodontitis genesis, offering new insights regarding the pathobiology of this bacterium.