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Tissue and Circulating MicroRNA Co-expression Analysis Shows Potential Involvement of miRNAs in the Pathobiology of Frontal Fibrosing Alopecia

2017; Elsevier BV; Volume: 137; Issue: 11 Linguagem: Inglês

10.1016/j.jid.2017.06.030

1523-1747

Christos Tziotzios, Chrysanthi Ainali, Simon Holmes, Fiona Cunningham, Su M. Lwin, Ioulios Palamaras, Kapil Bhargava, Janice Rymer, Catherine M. Stefanato, Niall Kirkpatrick, Sergio Vañó‐Galván, Christos Petridis, David A. Fenton, Michael A. Simpson, Alexandros Onoufriadis, John A. McGrath,

melanin and skin pigmentation

Frontal fibrosing alopecia (FFA) is a predominantly postmenopausal, primary lymphocytic cicatricial alopecia (Kossard, 1994Kossard S. Postmenopausal frontal fibrosing alopecia. Scarring alopecia in a pattern distribution.Arch Dermatol. 1994; 130: 770-774Crossref PubMed Scopus (356) Google Scholar) thought to be a clinical subvariant of lichen planopilaris (Kossard et al., 1997Kossard S. Lee M.S. Wilkinson B. Postmenopausal frontal fibrosing alopecia: a frontal variant of lichen planopilaris.J Am Acad Dermatol. 1997; 36: 59-66Abstract Full Text Full Text PDF PubMed Scopus (291) Google Scholar). Something of a misnomer, the descriptor FFA remains widely used for what is a generalized cutaneous lichenoid and fibrosing disorder, invariably associated with widespread body hair loss (Chew et al., 2010Chew A.L. Bashir S.J. Wain E.M. Fenton D.A. Stefanato C.M. Expanding the spectrum of frontal fibrosing alopecia: a unifying concept.J Am Acad Dermatol. 2010; 63: 653-660Abstract Full Text Full Text PDF PubMed Scopus (124) Google Scholar) extending beyond the frontovertex, especially if untreated (Figure 1a). Histopathologically, FFA features peri-isthmic lymphocytes (Figure 1b), and immune privilege collapse at the bulge may underpin inflammation-driven epithelial hair follicle stem cell loss and scarring (Tziotzios et al., 2016Tziotzios C. Stefanato C.M. Fenton D.A. Simpson M.A. McGrath J.A. Frontal fibrosing alopecia: reflections and hypotheses on aetiology and pathogenesis.Exp Dermatol. 2016; 25: 847-852Crossref PubMed Scopus (61) Google Scholar). MicroRNAs (miRNAs) are small, noncoding (RNAs that may affect protean biological functions (Lau et al., 2001Lau N.C. Lim L.P. Weinstein E.G. Bartel D.P. 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We sought to characterize miRNAs in FFA and probe disease relevance by undertaking tissue and circulating miRNA co-expression analysis in FFA patients versus matched control subjects. Neither deep transcriptomic analysis nor miRNA signature exploration has previously been explored for their bearing on FFA, to our knowledge. Seven newly diagnosed, treatment-naïve FFA patients and seven matched control subjects were recruited for tissue miRNA analysis (see Supplementary Table S1 online). After written informed consent and institutional ethics approval, temporal scalp skin biopsy samples were obtained from patients and control subjects. Each case was evaluated clinically and histologically to confirm active disease. Part of each biopsy sample was immediately immersed in RNAlater (Thermo Fisher Scientific, Waltham, MA); total RNA was isolated using RNeasy Plus Universal kit (Qiagen, Valencia, CA), and miRNAs were retained according to the manufacturer's protocol (see Supplementary Materials online). All samples underwent microarray analysis on Affymetrix GeneChip miRNA 4.0 arrays (Thermo Fisher Scientific). Next, to explore whether circulating miRNAs pertinent to fibrosis are relevant to FFA pathogenesis, venous blood was obtained from a separate cohort of 10 treatment-naïve patients with biopsy-confirmed FFA and 10 matched control subjects (see Supplementary Table S2 online). Plasma was isolated by centrifuging blood at 2,000g for 20 minutes at 4 °C and stored at –80 °C until analysis. MicroRNAs were isolated using Qiagen miRNeasy Serum/Plasma Kit according to the manufacturer's protocol. Expression analysis was undertaken using the Human Fibrosis miRNA PCR Array (Qiagen) (see Supplementary Materials and Supplementary Table S3 online). We first constructed a co-expression network of tissue miRNAs to investigate their molecular importance in FFA. The network was generated by assessing pairwise similarity of miRNAs expressed in each group (control subjects and FFA patients), calculated by the Pearson correlation coefficient (PCC) (Figure 2a, and see Supplementary Materials). A co-expression network establishes an association among miRNAs that show a coordinated expression pattern across a group of samples. It has been postulated that conserved co-expression confers a selective advantage, and therefore such genes might be functionally related (Piro et al., 2011Piro R.M. Ala U. Molineris I. Grassi E. Bracco C. Perego G.P. et al.An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction.Eur J Hum Genet. 2011; 19: 1173-1180Crossref PubMed Scopus (41) Google Scholar, Stuart et al., 2003Stuart J.M. Segal E. Koller D. Kim S.K. A gene-coexpression network for global discovery of conserved genetic modules.Science. 2003; 302: 249-255Crossref PubMed Scopus (1632) Google Scholar); such networks can therefore provide insight into how cells accomplish their functions. Only miRNA pairs that correlated above the 0.95 PCC threshold value were represented for each tissue network to capture the strongest relationship for miRNA expression regulation. The resulting FFA network displayed 2,089 co-expressed miRNAs (nodes) with 3,009 nonduplicate interactions (edges), whereas for the control group there were 2,100 co-expressed miRNAs (nodes) with 2,934 nonduplicate interactions (edges). Both tissue networks were used as molecular reference datasets to identify communities of miRNAs sharing common functions. For this analysis, we used the affinity propagation clustering algorithm (see Supplementary Materials). We generated a list of communities for the two tissue networks and automatically identified cluster centers (exemplars) as representative miRNAs of each community (see Supplementary Table S4 online). Next, differential expression analysis was applied in the plasma dataset to calculate the changes in expression between control and FFA samples and to identify up- or down-regulated circulating miRNAs (Figure 2b, and see Supplementary Figure S1 online). Fifty-five miRNAs were found to be up-regulated in FFA, and 11 miRNAs were down-regulated. For those circulating miRNAs, we further tested (i) their predictive value and (ii) if any of them mapped within the communities of tissue networks. To evaluate their predictive value, we used Random Forest with CARET package (caret.r-forge.r-project.org/) in R (https://www.r-project.org/), which ranks all data elements representing the most influential predictors (see Supplementary Figure S2 and Supplementary Table S5 online). To further evaluate circulating miRNAs that were found to be differentially expressed between control subjects and patients, we mapped them within the tissue-specific co-expression networks. Twenty exemplars in the control tissue miRNA network were significantly enriched in the plasma dataset compared with 27 exemplars that were significantly enriched in the FFA tissue miRNA network. Among these exemplars, there were 17 miRNAs common in both networks (patients and control subjects): three of these were specific to control subjects, and nine were representative of FFA. Of those nine circulating miRNAs, four were found to be highly predictive of disease status. Those miRNAs were, in order of importance, hsa-let-7d-5p, hsa-miR-18a-5p, has-miR-20a-5p, and hsa-miR-19a-3p, which are also co-targeting similar sets of genes. Although the latter three are part of the same cluster, hsa-let-7d-5p is independent of the cluster (Figure 2c), and the commonality in co-targeting similar genes is of particular interest. As previously suggested, if different miRNAs co-target similar sets of genes, these might regulate the same functions (Su et al., 2011Su Z. Xia J. Zhao Z. Functional complementation between transcriptional methylation regulation and post-transcriptional microRNA regulation in the human genome.BMC Genomics. 2011; 12: S5-S15Crossref PubMed Scopus (45) Google Scholar, Tsang et al., 2010Tsang J.S. Ebert M.S. van Oudenaarden A. Genome-wide dissection of microRNA functions and co-targeting networks using gene set signatures.Mol Cell. 2010; 38: 140-153Abstract Full Text Full Text PDF PubMed Scopus (187) Google Scholar). We applied generally applicable gene-set enrichment for pathway analysis (GAGE) to infer pathways associated with FFA and found mitogen-activated protein kinase (MAPK) signaling, endocytosis, and focal adhesion pathways to be down-regulated and enriched in the networks of co-targeting genes across these four miRNAs (see Supplementary Table S6 online). Our study identified circulating miRNAs hsa-let-7d-5p, hsa-miR-18a-5p, has-miR-20a-5p, and hsa-miR-19a-3p as being highly predictive of disease status in FFA by using a skin-specific miRNA network as a validating molecular mapping dataset. Functional validation is needed in the future to determine mechanistic relevance, and any additional circulating miRNAs that did not map to the skin network, despite having been found to be predictive in our analysis of plasma, could be further explored in larger cohorts. The authors state no conflict of interest. The authors acknowledge financial support from the Department of Health via the National Institute for Health Research (NIHR) Rare Diseases Translational Research Collaboration (NIHR-RD TRC) and a Comprehensive Biomedical Research centre award to Guy's and St. Thomas' NHS Foundation Trust in partnership with King's College London and King's College Hospital NHS Foundation Trust. We are thankful to clinical nurse specialist Ellie Stewart for her contribution to sample collection. We thank our patients and volunteers, because this study would not have been possible without their generous contributions. Download .pdf (12.6 MB) Help with pdf files Supplementary Data