Identification of Master Regulator Genes in Human Periodontitis

Abstract

Analytic approaches confined to fold-change comparisons of gene expression patterns between states of health and disease are unable to distinguish between primary causal disease drivers and secondary noncausal events. Genome-wide reverse engineering approaches can facilitate the identification of candidate genes that may distinguish between causal and associative interactions and may account for the emergence or maintenance of pathologic phenotypes. In this work, we used the algorithm for the reconstruction of accurate cellular networks (ARACNE) to analyze a large gene expression profile data set (313 gingival tissue samples from a cross-sectional study of 120 periodontitis patients) obtained from clinically healthy (n = 70) or periodontitis-affected (n = 243) gingival sites. The generated transcriptional regulatory network of the gingival interactome was subsequently interrogated with the master regulator inference algorithm (MARINA) and gene expression signature data from healthy and periodontitis-affected gingiva. Our analyses identified 41 consensus master regulator genes (MRs), the regulons of which comprised between 25 and 833 genes. Regulons of 7 MRs (HCLS1, ZNF823, XBP1, ZNF750, RORA, TFAP2C, and ZNF57) included >500 genes each. Gene set enrichment analysis indicated differential expression of these regulons in gingival health versus disease with a type 1 error between 2% and 0.5% and with >80% of the regulon genes in the leading edge. Ingenuity pathway analysis showed significant enrichment of 36 regulons for several pathways, while 6 regulons (those of MRs HCLS1, IKZF3, ETS1, NHLH2, POU2F2, and VAV1) were enriched for >10 pathways. Pathways related to immune system signaling and development were the ones most frequently enriched across all regulons. The unbiased analysis of genome-wide regulatory networks can enhance our understanding of the pathobiology of human periodontitis and, after appropriate validation, ultimately identify target molecules of diagnostic, prognostic, or therapeutic value.

Periodontal inflammation: Integrating genes and dysbiosis

“…For example, B-cell development, leukocyte extravasation signaling, phosphoinositide 3-kinase signaling in B lymphocytes, B-cell receptor signaling, and granulocyte adhesion and diapedesis were all among the top 10 most commonly enriched master regulator-associated pathways. Importantly, some of the major differentially regulated pathways reported in those 2 studies 10,14 have also been identified in other independent studies 30 . For example, pathways involved in cytokine and chemokine activities, B-cell receptor signaling, and defense and immunity proteins in both innate and adaptive immune responses, were reported to be among those most upregulated in periodontitis gingival tissues when assessed using RNA sequencing.…”

“…Using a reverse engineering approach, the same group reconstructed the regulatory network based on the co-expression data in gingival tissues and subsequently interrogated the transcription factor or master regulator-target gene interactions to identify periodontitis-specific pathways. 10 Of all the differentially regulated pathways, those related to the immune response and immune system development were the most frequently enriched. For example, B-cell development, leukocyte extravasation signaling, phosphoinositide 3-kinase signaling in B lymphocytes, B-cell receptor signaling, and granulocyte adhesion and diapedesis were all among the top 10 most commonly enriched master regulator-associated pathways.…”

“…Several transcriptome studies have elucidated common pathways or molecular regulatory networks that are specific to different states of periodontal diseases. [10][11][12][13] Other than those signature molecular events, several novel pathways, such as those associated with neural regulation or epithelial barrier defense function, have emerged. 13,14 These altered biologic activities, which have not been previously characterized in periodontal disease, provide a fresh perspective on understanding periodontal inflammation as a response to plaque bacteria.…”

Analysis of metabolic profiles of generalized aggressive periodontitis

“…Nevertheless, the specific pathogenesis of GAgP has not yet been clarified. Recently, a consistent, strong effort has been devoted to investigating periodontal pathogenic biomarkers through omics technology . Metabolomics is a branch of omics science that focuses on metabolites and variations of metabolic pathways in biological systems.…”

Environmental lead effects on gene expression in oral epithelial cells

“…The ETS proto‐oncogene 1 transcription factor was upregulated following biofilm challenge and directly controls the expression of cytokine and chemokine genes in a wide variety of cells . It has been reported as 1 of a set of master regulatory genes in periodontitis, and its enhanced expression confirmed from in vivo tissue samples and in vitro studies following challenge with P. gingivalis . Interleukin enhancing binding factor 3 (IL‐F3) can regulate the transcription of IL‐2 and a single report identified upregulation of the IL‐F3 protein in monocytes challenged with P. gingivalis .…”