Periodontitis is a non-communicable chronic inflammatory disease characterized by the progressive and irreversible breakdown of the soft periodontal tissues and resorption of teeth-supporting alveolar bone. The etiology of periodontitis involves dysbiotic shifts in the diversity of microbial communities inhabiting the subgingival crevice, which is dominated by anaerobic Gram-negative bacteria, including Porphyromonas gingivalis. Indeed, P. gingivalis is a keystone pathogen with a repertoire of attributes that allow it to colonize periodontal tissues and influence the metabolism, growth rate, and virulence of other periodontal bacteria. The pathogenic potential of P. gingivalis has been traditionally analyzed using classical biochemical and molecular approaches. However, the arrival of new techniques, such as whole-genome sequencing, metagenomics, metatranscriptomics, proteomics, and metabolomics, allowed the generation of high-throughput data, offering a suitable option for bacterial analysis, allowing a deeper understanding of the pathogenic properties of P. gingivalis and its interaction with the host. In the present review, we revise the use of the different −omics technologies and techniques used to analyze bacteria and discuss their potential in studying the pathogenic potential of P. gingivalis.

Periodontitis is a chronic inflammatory disease characterized by the progressive and irreversible destruction of the periodontium. Its aetiopathogenesis lies in the constant challenge of the dysbiotic biofilm, which triggers a deregulated immune response responsible for the disease phenotype. Although the molecular mechanisms underlying periodontitis have been extensively studied, the regulatory mechanisms at the transcriptional level remain unclear. To generate transcriptomic data, we performed RNA shotgun sequencing of the oral mucosa of periodontitis-affected mice. Since genes are not expressed in isolation during pathological processes, we disclose here the complete repertoire of differentially expressed genes (DEG) and co-expressed modules to build Gene Regulatory Networks (GRNs) and identify the Master Transcriptional Regulators of periodontitis. The transcriptional changes revealed 366 protein-coding genes and 42 non-coding genes differentially expressed and enriched in the immune response. Furthermore, we found 13 co-expression modules with different representation degrees and gene expression levels. Our GRN comprises genes from 12 gene clusters, 166 nodes, of which 33 encode Transcription Factors, and 201 connections. Finally, using these strategies, 26 master regulators of periodontitis were identified. In conclusion, combining the transcriptomic analyses with the regulatory network construction represents a powerful and efficient strategy for identifying potential periodontitis-therapeutic targets.