Identification of Master Regulator Genes in Human Periodontitis

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

External organisations

  • The Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA.
  • Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
  • Division of Periodontics, Section of Oral, Diagnostic and Rehabilitation Sciences, College of Dental Medicine, Columbia University, New York, NY, USA pp192@cumc.columbia.edu.

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.

Details

Original languageEnglish
Pages (from-to)1010-7
Number of pages8
JournalJournal of Dental Research
Volume95
Issue number9
Early online date14 Jun 2016
Publication statusPublished - Aug 2016

Keywords

  • Adult, Algorithms, Case-Control Studies, Chronic Periodontitis/genetics, Cross-Sectional Studies, Genes, Regulator/genetics, Gingiva/metabolism, Humans, Periodontitis/genetics, Transcriptome