Identification of Master Regulator Genes in Human Periodontitis

Identification of Master Regulator Genes in Human Periodontitis

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 ca...

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Bibliographic Details
Published in:Journal of dental research Vol. 95; no. 9; pp. 1010 - 1017
Main Authors: Sawle, A.D., Kebschull, M., Demmer, R.T., Papapanou, P.N.
Format: Journal Article
Language:English
Published: Los Angeles, CA SAGE Publications 01-08-2016
SAGE PUBLICATIONS, INC
Subjects:
Adult
Algorithms
Binding sites
Case-Control Studies
Chronic Periodontitis - genetics
Cross-Sectional Studies
Data processing
Datasets
Dentistry
Disease
Ets-1 protein
Gene expression
Gene set enrichment analysis
Genes, Regulator - genetics
Genomes
Gingiva
Gingiva - metabolism
Gum disease
Humans
Identification
Immune system
Patients
Periodontitis
Periodontitis - genetics
Phenotypes
Research Reports
Transcription
Transcriptome
molecular genetics
genomics
gingiva
periodontal disease(s)
gene expression
bioinformatics
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Summary: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.
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ISSN:0022-0345
1544-0591
DOI:10.1177/0022034516653588