2017 ODAC CONFERENCE ARTE POSTER WINNER: Bioinformatics Analysis of the Inflammatory Acne Transcriptome Supports Mechanisms of Action for Current Treatments and Predicts Novel Therapies In Vivo

November 2017 | Volume 16 | Issue 11 | Features | 1166 | Copyright © November 2017

Rivka C. Stone MD PhD and Marjana Tomic-Canic PhD

Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami FL

Acne vulgaris, a common disorder of pilosebaceous follicles, adversely affects quality of life in patients. Formation of papules and pustules in inflammatory acne is orchestrated by a sustained host immune response whose features are incompletely understood, impeding the development of novel targeted therapies. To this end, we utilized transcriptome data previously obtained from lesional and non-lesional skin of 18 patients with inflammatory acne. We identified an acne “signature” of 217 genes that were coordinately up-or down-regulated in lesional skin of patients from two cohorts. Pathway analysis was enriched for biological processes of innate and adaptive immunity. We employed bioinformatics techniques to identify drugs predicted to target acne-relevant immune processes through their known effects on the expression of subsets of inflammatory acne “signature” genes. Using this approach, we linked minocycline to modulation of neutrophil and lymphocyte migration via its downregulation of IL1B, IL6, CXCL8, and CCR2 expression that is increased in lesional acne skin in vivo. Similar networks were constructed for tretinoin and salicylic acid therapies. Finally, we predicted a novel role for small molecule inhibitors of MEK/ERK signaling in the treatment of inflammatory acne. Taken together, our findings suggest that bioinformatics approaches can be successfully employed in the discovery an development of acne therapeutics.

J Drugs Dermatol. 2017;16(11):1166-1169.


Microarray datasets from skin biopsies of patients with inflammatory acne were downloaded from NCBI’s Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo). Eighteen patients from two clinical cohorts were included in this study (Table 1). An inflammatory acne lesion and adjacent non-lesional skin from the back or chest of each patient was biopsied (Figure 1).RNA was extracted from biopsy specimens, amplified, and fragmented as described.1,2 Labeled products were hybridized to Affymetrix Human Genome U133 Plus 2.0 Arrays (Affymetrix, USA). Normalized dataset files were downloaded from the GEO database and imported into Genespring 13.0 (Agilent Technologies). The processed data were compared by two-sided paired t-test of lesional (acne) vs. non lesional skin. Significantly differentially expressed probes with fold change >2 were used for bioinformatics analysis. Pathway analysis and downstream target/functional predictions were performed using Ingenuity Pathway Analysis (IPA; Qiagen). Statistical tools within the IPA software package used Fisher’s exact test with Benjamini- Hochberg correction for multiple testing to detect the reported significantly enriched pathways, biologic processes, and upstream regulators.


Many genes were de-regulated in lesional acne skin from patients in both cohorts, as compared with non-lesional skin (Figure 2). Ingenuity pathway analysis performed on the differentially expressed acne genes in each patient cohort revealed coordinated enrichment of canonical pathways of