Hindawi Publishing Corporation BioMed Research International Volume 2016, Article ID 5469371, 9 pages http://dx.doi.org/10.1155/2016/5469371

Research Article Analyzing the miRNA- Networks to Mine the Important miRNAs under Skin of and Mouse

Jianghong Wu,1,2,3,4,5 Husile Gong,1,2 Yongsheng Bai,5,6 and Wenguang Zhang1

1 College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China 2Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China 3Inner Mongolia Prataculture Research Center, Chinese Academy of Science, Hohhot 010031, China 4State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China 5Department of Biology, Indiana State University, Terre Haute, IN 47809, USA 6The Center for Genomic Advocacy, Indiana State University, Terre Haute, IN 47809, USA

Correspondence should be addressed to Yongsheng Bai; [email protected] and Wenguang Zhang; [email protected]

Received 11 April 2016; Revised 15 July 2016; Accepted 27 July 2016

Academic Editor: Nicola Cirillo

Copyright © 2016 Jianghong Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Genetic networks provide new mechanistic insights into the diversity of species morphology. In this study, we have integrated the MGI, GEO, and miRNA database to analyze the genetic regulatory networks under morphology difference of integument of and mice. We found that the network in the skin is highly divergent between human and mouse. The GO term of secretion was highly enriched, and this category was specific in human compared to mouse. These secretion might be involved in eccrine system evolution in human. In addition, total 62,637 miRNA binding target sites were predicted in human integument genes (IGs), while 26,280 miRNA binding target sites were predicted in mouse IGs. The interactions between miRNAs and IGs in human are more complex than those in mouse. Furthermore, hsa-miR-548, mmu-miR-466,andmmu-miR-467 have an enormous number of targets on IGs, which both have the role of inhibition of host immunity response. The pattern of distribution on the of these three miRNAs families is very different. The interaction of miRNA/IGs has added the new dimension in traditional gene regulation networks of skin. Our results are generating new insights into the gene networks basis of skin difference between human and mouse.

1. Introduction species. Mammals have diverse coat morphology, due to changes in the gene regulatory pathway/network. Common The integument includes the skin, coat/hair, and nails [1]. The genetic network variants have been shown to affect many mammalian coat hair is one of the defining characteristics complex traits, including integument morphology. There- of mammals [2]. Enormous morphological variations are fore, to understand the differences of molecular mechanism found among mammalian integument, especially in coat hair. for integument phenotype between mouse and human, it Hair is present in differing degrees in all mammals [3]. In is necessary to employ the scope of system biology. The most mammals, the hair is abundant enough to cover the comparison of gene coexpression networks and regulation body and form a thick coat, while dolphins, naked mole-rats, networks between two species is often a useful method to and humans are among the most hairless of all mammals identify the differences of critical biologic process associated [4]. However, humans with the hypertrichosis syndrome with morphology diversity. have hair covering their faces, their eyelids, and their bodies Mammalian Phenotype (MP) ontology [7] (http://www [5, 6]. Therefore, we provide a hypothesis that key genes .informatics.jax.org/) is made up of 17,330 terms (as of related to hair coat formation exist in every mammalian 02/08/2016), and most of these terms were characterized 2 BioMed Research International from abnormal mouse phenotypes. The ontology database Weighted Gene Co-Expression Network Analysis (WGCNA) deposited 1627 mouse/human orthologous genes with phe- [16, 17] was used for these expression datasets to create notype annotation related with integument (MP:0010771). consensus networks between human and mouse, and the High-throughput experiments have produced many microar- networks were visualized in Cytoscape 3.3.0 [18]. rays and next generation sequencing data that are collected in Gene Expression Omnibus (GEO) database [8]. Genetic 2.3. Functional Enrichments for IGs Network. To classify the networks have been widely used in biological research, terms and group for IGs in (GO) and KEGG bridging the gap between single genes and biological systems pathway, we used the Cytoscape plug-in ClueGO to imple- by investigating the relationships between different genes. ment enrichment analysis. We set kappa score threshold to miRNAs are small noncoding RNA molecules which play 0.5 and the 𝑝 value to 0.05; we used two-sided test, Bonferroni an essential role during skin development [9]. Variations in step down and GO term fusion. The other parameters of the theinteractionbetweenmiRNAandtargetgenearelikelyto software were set to default values [19]. influence the phenotypic differences between species. Here, weuseWeightedGeneCo-ExpressionNetworkAnalysis 2.4. Prediction of miRNA Targets for Integument Genes. For pre- (WGCNA) to reveal shared and unique characters of the diction of miRNA targets for hub genes, miRanda [20] soft- gene networks in human and mouse skin. We identify ware version aug2010 available at http://cbio.mskcc.org/mic- networks of coexpressed genes, which might be associated rorna data/miRanda-aug2010.tar.gz was employed to predict with biological functionally relevant coat morphology, and miRNAs regulated IGs. miRanda was running as the follow- explore differences in these biological processes between two ing command options: sc ≥ 180, en = 1 (no energy filtering), species. We also found that candidate miRNA may play a go = −9.0, ge = −4.0, and scale = 4.0. We set the option role in the regulation of gene expression networks in skin. -strict to prevent gaps and noncanonical base pairing in These results provide a system-level insight into evolutionary target sites. Human/mouse miRNA sequences were used as changes of the integument. query sequences, and the IGs sequences were used as [20]. We also predicted the targets with two miRNA prediction 2. Materials and Methods databases, miRTarBase [21] and miRDB [22], and selected In the present study, we emphasize the comparison between the intersections targets to construct the miRNA-mRNA the gene network for integument of human and that of regulatory network. mouse and identification of candidate miRNAs relevant to the integument genetic pathway and constructed an interaction 3. Results network between miRNAs and targeted genes. Figure S1 in Supplementary Material available online at http://dx.doi.org/ 3.1. Chromosome Location for Integument Genes of Human 10.1155/2016/5469371 shows pipeline for the study. and Mouse. We observed a uniform distribution of the 1627 integument genes across all in human and 2.1. Selection of Genes and miRNAs for Human and Mouse. mouse (Supplementary Figure S2). As can be seen in Figure Mouse/Human Orthology with Phenotype Annotations were S1, in general, 1627 integument genes are dispersed along the downloaded from the MGI database [10]. 1627 of these genes chromosomes of human and mouse. These results provide are related to the integument phenotype (MP:0010771). To insights into the biological underpinnings of integument draw the chromosome location for integument genes (IGs), phenotype and the pleiotropic connections between traits. It the R package org.Mm.eg.db [11] and org.Hs.eg.db [12] were is very difficult to detect which chromosome is essential to used. We downloaded the gene expression data of skin for integument phenotype because these integument genes will mouse and human from the Gene Expression Omnibus be uniformly distributed on all chromosomes. (GEO) [13] and mouse and human miRNAs from miRBASE (http://www.mirbase.org/cgi-bin/browse.pl/) [14]. Mouse and 󸀠 3.2. Gene Expression Network. ByusingWGCNA,wegen- human 3 UTR sequences of 1627 integument genes were ob- erated two consensus networks of the human and mouse tained from Ensemble (http://www.ensembl.org/) by biomaRt (Figure1)whichshowanetworkheatmapplotofagenenet- in R language [15]. work together with the corresponding hierarchical clustering dendrograms and the resulting modules. Then, the gene 2.2. Construction of Gene Networks. To compare the gene expression networks were visualized in Cytoscape. There are networks of skin for human and mouse, we selected many two groups in human skin consensus networks constructed relevant data from the GEO database [8], and then we from 560 genes (nodes) and 18391 interactions (edges), while filtered out all but a core collection of datasets that were only one group in mouse skin gene networks was constructed similar enough for useful bioinformatic comparison. First, we from 368 genes and 1757 interactions (Figure 2). To eval- downloaded all datasets that were run on same Affymetrix uate the complexity of gene networks, we calculated the platform, one platform in human (GPL570) and one in mouse interaction degrees for gene expression network. The average (GPL1261) (Table S1). Second, we selected only relevant interaction degrees are 32.8 (18391/560) and 4.8 (1757/368) for experiments about skin samples on the platform. Third, we human and mouse, respectively. extracted IGs expression data from the chip matrix. Finally, We use the ClueGO to compare three clusters of genes a total of 1487 genes were analyzed in this study. Then, from Group 1 and Group 2 of human and the mouse BioMed Research International 3

(a) (b)

Figure 1: The TMO plot shows the modules of gene expression in human (a) and mouse (b) by WGCNA.

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Figure 2: The consensus networks for skin of human and mouse: (a) for human and (b) for mouse.

consensus networks (Figure 2). 44 significantly overrepre- genes and response to organic substance were common in sented categories were identified (Figure 3). Most of the three clusters. positive regulations of cellular process were clustered into Furthermore, we found that four GO terms were highly two independent modules. More than half of the common enriched, and these terms were specific in human compared genes among three clusters were involved in regulation of to mouse. These four GO terms could be divided into three cellular and metabolic processes, such as negative regulation categories (Figure 4). The regulation of secretion category of nucleobase-containing compound metabolic process and contains two GO terms: regulation of secretion and regula- regulation of macromolecule biosynthetic process. Some of tion of secretion by cell. the most significant categories were response to stimulus, regulation of localization, negative regulation of biological 3.3. miRNA Target Sites in IGs. miRNAs are implicated process, cell differentiation, single-multicellular organism in integument development. To examine how the miRNAs process, and response to organic substance. Analysis of the interacted with the gene networks under integument, we modules showed that the majority of the response to stimulus used miRanda to predict the miRNA targets for IGs. Total 4 BioMed Research International

Figure 3: Overrepresented GO categories in the integument gene list of human and mouse. The figure was drawn by Cytoscape 3.3.0 and shows enriched GO categories for common genes in three clusters. The pie chart shows the leading group term in each of the GO categories. ∗∗ ∗ The kappa score threshold was set to ≥0.5. If the term/group is oversignificant𝑝 ( value < 0.001). If the term/group is significant (0.001 <𝑝value < 0.05).

% genes/term 01 23456789101112 Regulation of secretion 89∗∗ Regulation of secretion by cell 80∗∗ Cell-cell signaling 143∗∗ G- coupled receptor 90∗∗ signaling pathway

∗∗ Figure 4: Specific GO term enrichment as estimated for the human versus mouse. If the term/group is oversignificant𝑝 ( value < 0.001). ∗ If the term/group is significant (0.001 <𝑝value < 0.05).

62,637 miRNA binding target sites were predicted for 1627 datasetof1915mousemiRNAsusedfortheanalysis.Asingle human IGs selected and the dataset of 2588 human miRNAs miRNA can regulate hundreds of transcripts and a single used for the analysis. Total 26,280 miRNA binding target transcript can have binding sites for multiple miRNAs of the sites were predicted for 1627 mouse IGs selected and the same or different sequence. To determine the portion of the BioMed Research International 5

Table 1: Top 10 miRNA families for IGs of human and mouse.

Human miRNA family Target number Mouse miRNA family Target number 1 hsa-miR-548 4643 mmu-miR-466 1704 2 hsa-miR-1273 547 mmu-miR-467(669) 956 3 hsa-miR-30 412 mmu-let-7 235 4 hsa-miR-520 332 mmu-miR-297 222 5 hsa-miR-302 281 mmu-miR-6951 176 6 hsa-miR-3689 277 mmu-miR-3473 173 7 hsa-miR-4763 273 mmu-miR-30 166 8 hsa-miR-619 266 mmu-miR-7116 154 9 hsa-miR-4775 262 mmu-miR-181 140 10 hsa-miR-513 241 mmu-miR-7661 133

Scale 100 kb mm10 chr2: 10,400,000 10,450,000 10,500,000 10,550,000 UCSC genes (RefSeq, GenBank, tRNAs, and comparative genomics) AK076687 Sfmbt2 Sfmbt2 mir-466m mir-467a-4 mir-467d Sfmbt2 mir-669f mir-467a-3 mir-466 Sfmbt2 Sfmbt2 mir-669e Gm25285 mir-669m-2 Sfmbt2 Sfmbt2 mir-669b mir-669a-1 mir-466n mir-669d mir-467a-4 mir-669o mir-669l mir-466b-4 mir-466g mir-669d-2 mir-669a-1 mir-466l mir-297a-3 mir-467c mir-467a-4 mir-669i mir-466b-4 mir-466b-4 mir-669h mir-669a-3 mir-467a-4 mir-669k mir-466b-2 mir-467a-9 mir-669a-1 mir-466b-8 mir-467a-4 mir-669a-1 mir-466b-4 mir-669g mir-669a-1 mir-669j mir-467a-3 mir-467a-2 mir-466b-3 mir-466b-8 mir-669a-1 mir-669a-1 mir-669a-2 mir-467b mir-669m-1 mir-466c-2 mir-669a-1 mir-467a-6 mir-466c-2 mir-669a-1 mir-467a-4 mir-466b-4 mir-669a-1 mir-466b-4 Mouse ESTs that have been spliced Spliced ESTs Simple nucleotide polymorphisms (dbSNP 142) found in ≥ 1% of samples Common SNPs(142) Detailed visualization of RepeatMasker annotations RepeatMasker Viz. Repeating elements by RepeatMasker RepeatMasker

Figure 5: The chromosome location of mmu-miR-466 and mmu-miR-467 family.

main miRNA in the total miRNA target file, we selected the 3.4. Construction of a miRNA-mRNA Regulatory Network. top 10 miRNA families based on the target number of IGs Two miRNA prediction databases miRTarBase and miRDB (Table 1). The hsa-miR-548 family has predicted 4643 target were used to verify the results of miRanda. The intersections sites distributed on 541 IGs of the human. The mmu-miR- targets of three algorithms were selected to construct the 466 family and mmu-miR-467 family have predicted 1,704 miRNA-mRNA regulatory network (Table S2). The resulting target sites and 956 target sites, respectively, distributed on miRNA/target mRNA pairs and gene networks for human 375 and 310 IGs of the mouse. The hsa-miR-548 family is and mouse were visualized in Cytoscape, where edges from widely distributed in the whole . However, miRNA to genes represent a potential regulatory relationship bothofthesetwomiRNAfamiliesarelocatedinanintron and edges from gene to gene represent an expression correla- of Sfmbt2 gene on of the mouse (Figure 5). tion. The network consisted of two almost separate groups, These three miRNA families have common target genes which could be connected when miRNAs is added to the (Figure 6) and might present some similarity function to skin expression gene network of human (Figure 7). These miRNA- morphogenesis. target interactions were added to the network of the mouse, 6 BioMed Research International

Hsa-miR-548 Mus-miR-466 Analysis of the GO terms category showed that the majority of the positive regulations of cellular process and response to stimulus genes were common in three clusters, 65 295 which may be due to the perception function of skin to in vivoandinvitroenvironments[30,31].Inotherwords,these 118 common categories in human and mouse skin were involved in protection [32, 33], sensation [34], temperature regulation 121 [34], immunity [35], exocrine, and endocrine. By comparing 61 the GO terms for those genes in expression network, the term 71 of regulation of secretion category was enriched in human skin. Human has eccrine glands and thermal apocrine glands. Apocrine glands are always associated with hair follicles, and eccrine sweat glands cover almost the entire body surface 57 ofhuman[36],whileeccrinesweatglandsinthemouseare found only on the footpads [37]. These results might provide an important insight into the evolution of thermal eccrine Mus-miR-467 system in human. The secretion category of the genes might Figure 6: The Venn diagram of the target gene list of three miRNAs. be involved in the eccrine system in human. miRNA plays an essential role in the regulation of skin development and morphogenesis [38]. miRNA family is a group of miRNAs that share common seed sequences, which which have not significantly affected the consensus networks has a similar regulation function [39]. We found that the in mouse skin (Figure 7). hsa-miR-548 family has the highest amount of target sites among the identified miRNAs in human and the mmu-miR- 4. Discussion 466 and mmu-miR-467 familiesaretoptwointhemiRNAs list predicted in the mouse. Generally, increasing the number Recently, a lot of biology databases have been published. of target sites within a gene improves the efficiency of miRNA How to integrate these resources in system biology is a regulation [40]. More target sites provide more opportunity bigchallengeforanalysisofcertainbiologicalproblems for recognition by miRNA and increase the kinetics and over- [23]. In this study, we have integrated the MGI, GEO, and all level of transcript regulation [41]. miR-548 is a primate- miRNA databases to analyze the genetic regulatory networks specific miRNA family, which has 69 members distributed under morphology difference of integument of human and in almost all human chromosomes [42]. The hsa-miR-548 mouse. 1627 mouse/human orthologous genes related with family is involved in multiple biological processes, such as sig- integument phenotype were deposited in MGI database. naling pathways, immunity, and osteogenic differentiation, These genes were widely scattered across the mouse genome and some [43–47]. hsa-miR-548 takes part in IFN and the human genome, which suggest that the integument signaling which responds to the virus and bacterial infections is a quantity phenotype with polygenic determinism. We also on the cell [46]. hsa-miR-548 also can turn down the host have constructed the expression correlation networks of IGs antiviral response by degradation of IFN-𝜆1 [43]. UVB irra- with the gene expression matrix from the GEO database. diations can downregulate hsa-miR-548 of human epidermal With the process of evolution, the organisms have increased melanocytes cell [48]. These results suggested that hsa-miR- in complexity. However, the organism’s complexity is not 548 might contribute to dynamic regulatory network of skin simply associated with the number of its genes [24]. Now, transcriptome of human. Compared to human, mmu-miR- the interaction degrees as a straightforward detector were 466 and 467 families only located in intron 10 of Sfmbt2 used to evaluate the complexity of gene networks [25]. Our genes. This area of intron 10 has a larger cluster of miRNAs, results revealed that the networks of human skin are much which is specifically present in mouse and rat [49]. Based morecomplexthanthoseofmousewhichmightbeexplained on miRBase database (http://www.mirbase.org/) definition, by the fact that the integument structure at the anatomical clustered miRNAs are a group of miRNAs located within levelismuchmorecomplexinhuman,comparedwithmouse 10 Kb of distance on the same chromosome. In this study, [26]. Many researches reported that one gene could evolve mmu-miR-466, mmu-miR-467,andmmu-miR-669 clusters new function to adapt to the change of environment during haveonecorepromoterregionandtranscriptionalstartsite species divergence, from lower to higher species [27, 28]. shared with the Sfmbt2 gene. The expressions of mmu-miR- Erwin and Davidson reported that the reorganizations of 466 and mmu-miR-467 markedly waved during the hair gene networks could change the animal morphology and follicle cycling in mouse [50] and were downregulated in the basic of the evolution is regulatory changes within a melanoma of mouse by curcumin diet [51]. And histone gene regulation network [29]. These results also confirm our deacetylation and metabolic oxidative stress can induce the hypothesis that those key genes related with the hair coat exist activity of mmu-miR-466 [52]. Skin serves as a barrier in every mammalian genome and the diverse morphology between body and pathogens (disease causing organisms), in mammal just because of the difference in gene regulation which is part of the innate immune system [53]. Both of networks. hsa-miRNA-548 and mmu-miR-466 and mmu-miR-467 can BioMed Research International 7

POLG

ARID3ACYSLTR2

BARX2 ADORA1 ATE1 SLC38A10CYP19A1 MIB1 IKBKB mmu-miR-466b-3pNRAS E2F5 FES mmu-miR-466p-3p STAT5B LATS1GNAO1 TRAPPC6A mmu-miR-466c-3p PRLR SLC6A4GABRA2TCF15 RARGSREBF2 AGPAT2 WDR37 hsa-let-7b-5p PRL GRIK1HTR3AMKL1FGF23KISS1RVGFARXSLC12A1INPP5D HLX LIPE mmu-miR-340-5p GPRC6ACLDN6FGF5SCN11AKRT76HTR1FPTGIRTLL1CSF2OXTRTOM1L2TYRO3ACADS mmu-miR-466m-3pmmu-miR-466o-3pTGFBR1 hsa-miR-18b-5p RGS4GRIA1GSCGALR2GPR3RSPO2TMC1LMX1BAVPR2NTRK1TCF21IL4RLN1SNAP25CITED1CHRNA4OPRM1NPBADCY1KCNIP3CHST8SEMA6CHOXD1LTC4SGLRA3VHLIL17RARXRBPORCNCDKN2C MAPK14 hsa-miR-6740-5phsa-miR-1294hsa-miR-98-5p SOX21NMUR2RLBP1CLRN1MTA2FGF20NMUR1ADAMTS20OPRK1PTCH2KCNJ11NAGSCLPSANK1EMX1CLCN1FOXP3CNR2GBX1NTSR1AICDACHRNB4GABRR1AGTR2PARK2HOXB9GRM7MUSKCHRNGGFRA2SLC4A1DUSP26ARHGAP1ADRBK1ERCC2FGF6 NR1H2P2RX7 CHEK1mmu-miR-7b-5p hsa-miR-18a-5pESR1 CCDC57TREML1RHBDL1HOXD13OPRD1SOX11TREX2P2RX6MYOGALX4GATA1CDH23AQP2TMPRSS6SLC17A6HFE2CCR10AMHR2RXFP1PDX1CTSESRCAGAP2FGATFF1EDARADDCD19PAHLIPICSN2IL13PRXSLC39A3POLD1TWF1CCM2TRPC4AP MAPK1 hsa-miR-6124hsa-miR-3919hsa-miR-3927-3p OPRL1NTSR2SLC5A7CACNA1SFOXB1SYT7PTGER1DRD2PROKR2CLEC1BLMX1ANOS2FSHRCDH8NKX2-5USH1CBRSK1CD40LGPOU3F2GHRHRCASRHTR2CCACNA1BSLC45A2IL10FOXA2CRHBRSK2DLGAP3CYP7A1DBHWNT10ACARD11SLC4A4GRM5BCL2HCN2ETV4TRPV4ZFPM1HOMER3CHRNB2ANO1EMILIN1 CNR1 MEGF8 hsa-let-7f-5p CLP1PDE6BPDYNPRDM8GFI1BCDK5R2ZAP70STOML3ASCL1POMCHAP1HOXB13GRM1APOA1ATOH1TRPM3RXFP2HCN1SLC12A5OXTSOX2CSN3SCN10AESR2ALAS2TRPA1HNF1ACSF3GRIK5TOP3BPLCB2HSF1ZBTB17NR4A1HOXC8 RAD18 hsa-miR-22-3p FOXG1VWA1PRKCAGATA4WNT7AHOXB1POU3F4TNFSF11CAMK4PITX3P2RX3PLD4CAMK2ALALBASSTR4KIF1AAGERRAC3GNRHRNRP2SHHMYO7APNOCCCKBRITGB3GPC5HTR7PDGFBECE1CDKN2ANFKB2 mmu-miR-1952 SLC6A3BTN1A1COL19A1KSR1MC5RTPH2CNTNAP2BCL2L1FCRLA mmu-miR-5101 IGF1R GATA5 SOX18PIK3CG JDP2 B3GNT3 MTHFRPRKAR1BBCL2L11hsa-miR-5589-5p ERN1 IDS FGF11 PTGS2 FARP2 SSTR4RXRA mmu-miR-342-3p hsa-miR-8485 hsa-miR-24-3p mmu-miR-466l-5pGATA4 OPRL1 WFS1mmu-miR-466i-3p mmu-miR-126a-5p hsa-miR-30e-5p hsa-miR-32-5p ALDH16A1PSEN1ZBTB17 TLR4XPC GABBR1mmu-miR-495-3pF2RL1 mmu-miR-136-5p hsa-miR-137 MYO5A USH1CTOM1L2TRPC4APFGF6STK11 ANK1 SFN PLEKHG5GRIA1 hsa-miR-548c-3p PLG ABCC6 hsa-miR-23b-3p MMP12mmu-miR-466f-3p FIGN SLC19A1 PTEN RAG1 EDN3 CNTNAP2CAMK2AGRIK5 LIPI TSC2 BDNFmmu-miR-3082-5pLEF1F2 PAX1 MAPT hsa-miR-21-5pNCOA3hsa-miR-20b-5phsa-miR-301b-3phsa-miR-19a-3p hsa-miR-23a-3p ATP8A2GBX1mmu-miR-377-3pAP3D1SNIP1SMOC1POFUT1VEGFA hsa-miR-17-5p PTGDS CASR SRSF4 SHH hsa-miR-20a-5phsa-miR-106b-5p hsa-miR-26a-5p GNRHR MCOLN3FGF9FGF10ADORA1KIF1A PNPLA6MPV17PPP2R5D GNAQ STAT3 hsa-miR-3666 DLG4hsa-miR-203a-3p OTC CACNA1B KCNA6BRSK2 GNAO1THRAPLCL1GFRA2 mmu-miR-297b-5p hsa-miR-106a-5pLDLR EGLN2 PDYN PI4KA OXTMORF4L1PPM1F ATRNCD28mmu-miR-297c-5p E2F1 PAX3 ETS2KCNIP3NAB2RUNX3 hsa-miR-451b IRF1 hsa-miR-19b-3p PRDM6ARX STOML3P2RX3 TRPM3 P2RX6CDKN2CPRLCLCN6 SOD2 KCNJ16 hsa-miR-497-5pKL hsa-miR-4668-5p SCN9A GABRA4 DLG4TNFRSF19CISD2PAQR3 SNAI2TBX15SNAP25 CASK GABRA2 mmu-miR-466l-3p CACNA1AARIH2 RPS6KA3 PRLR TULP3NGFRGRASPHOXC8 VEGFA hsa-miR-93-5p SLC12A5EGFR DDR1 WDR48CDK7 TCF7L2SLC4A4 PTPRBHTT mmu-miR-3062-5p CRKL PCSK2POMC TMC1GRIA2GATA5MAP2K7T NF1 PTCH1PTGDR NEO1PRDM8TACR1 DMTF1hsa-miR-199a-5p CSF1 HTR1F GRIA4 COQ9SLC17A7ADAMTS9RXRBCDC73 FECH mmu-miR-669n hsa-miR-199b-5p POU3F4RHOT2BECN1CHRNGUBE3B GIT2 mmu-miR-297a-5pTAB2CD274 GRIA3 hsa-miR-130b-3phsa-miR-301a-3p NCOA1MED1 MTDHPPARASLC12A6 KCNJ1OXTRPIRT CASKCD4TNFACPP mmu-miR-574-5p hsa-miR-449ahsa-miR-1277-5p CDH8 LMX1BUBE2BGNAS MBD5MIB1 MDM2ECE1 MDM4 hsa-miR-16-5p GJA1 HTR2C MAP3K1 CARD11PAX6 CBSNCOA2 BMPR1ABIRC6PPM1AAEBP2OPHN1RHBDF1TCEB3 OPRM1 NOTCH1 hsa-miR-15b-5p hsa-miR-130a-3p MTA1 HOXB1CLDN6 PLCG1MMP24 EGF IDUA SIK2CREB5CHRNB4NQO2 hsa-miR-4703-5pDDR1 hsa-miR-215-5p ZEB1 HOXB13MADD RCE1SCN10A KISS1R YES1 MAF NAB1 CASP8 hsa-miR-34a-5p hsa-miR-192-5p hsa-miR-200a-3p NRG3SSFA2 GBF1DOPEY2 KDM4CPARK2MSH3RB1ZMPSTE24TERF2IP KALRNWRN BCL2 TERF1 mmu-miR-466i-5p NTSR2EDARKIF1B SENP1PGGT1B AR HIRATSC1FREM2 CRIM1 RUNX1 MAP4K5YY1PPP1R15BALDH2DSC3SERTAD2hsa-miR-107 hsa-miR-141-3p GAB1 GAKMAPK7 CDKAL1 NCOR1SMCHD1GNA13SOCS5 mmu-miR-466p-5p TMEM79DICER1hsa-miR-103a-3p EDARADD CBX7 SMARCA2ATR POLBCDC73NBL1CTNND1AARS ZBTB20 hsa-miR-429 E2F3 MAP2K2ERCC2SLC39A3ITPR2 PTPN11SLC6A2 NEK1 FARP2mmu-miR-9-5p mmu-miR-466a-5p CHUK KIF1BGOLM1HIF1Ahsa-miR-15a-5pRINT1 MEOX2RPS20hsa-miR-200b-3pPTPRZ1 HOMER2 USP4 ARNT NAV2 MIA3ATM GSTP1 PERPNPY1RGNAQLAMP2 hsa-miR-200c-3p CDK5R1MAP3K11NIT1 TAP1 MNTPOU2F1RB1CC1 mmu-miR-1187 mmu-miR-466e-5p RFWD2KIFAP3KITCTSATK2EVPLRAC1PAK1IP1RPL27A KDRADAMTS5COL1A2 ZFPM2 GHRHR EZH1SIRT2 HERC3ERBB2 ARIH2 SMARCA2ASPABBS2SHOC2ARID4ACDK7SLC20A1 OPRD1 RINT1BRD1VPS33ALMF1MSH2SYNE2 CCDC57VDR ITPKBSIK3 ERBB2IP S1PR2 LBHSCARF1ERCC3 NOTCH2JAG1PTGES3FMR1DBIRPL38YAP1CHD2CD34RPL24THBS2A2MEBF1LMO2COL3A1 FAAHSLC11A2 MKLN1BRD7BBS4 MAVS DHCR24CISD2ZMPSTE24FBXW7NCOR1NDUFS4ATP2C1ROBO1PROS1SNX5BIRC6YES1ZDHHC13TNFRSF1AAPLP2HDAC1TNIP1NXF1ARCN1CRIM1 S100A10LUM CDK5 PLCB3 RAF1SOS1NFE2L1 NR2C2 SOD1 IFT27TGFBR1APRTMRPS35BUB3LRRK2TERF2IPTRPV1CDK4LBRPRMT5ERP44HMGN1ERCC5NFE2L1FBXO11NPM1SDC1SOD1RECK CDH5 FN1 TFPI BSCL2 ERP44HPS3XIAPXPA ROCK1 XPAMAPK8SP3POFUT1TBC1D20SMAD2OTCTSG101MAP2K1GABBR1TPH1BSCL2ARPC2RALBP1BMPR1ASMARCA5ERCC1PTPN6OPHN1NCOA1ILKUSP9XMAPK6ARL6IP5AEBP1PDGFRBIGFBP7 GJB3 CLPPKDM4BYY1 PHF2MCOLN1CREBBPTAX1BP1ALDH2 SMC6SIRT6 hsa-miR-223-3pASB1HDAC4MYBUBE3AUSF2PSTPIP2SMC6FOXO3NPEPPSDDB2SUPV3L1IRS2TSC1ATF4NDFIP1RAPH1RUNX1T1RELN COL1A1 APCCHD2 FOXO1 CBX6 CLCN6ITPAATRPHF2LIPANFE2SLC25A37NSUN2EZH2PPM1ATBK1WRNSTIM1ITGB5BRD1TAX1BP1CBLB COL5A1COL5A2SLIT2 TRAF3IP2RAE1YAP1FBXW7 MECP2 USP4ATOX1PLCB3MED31NR2C2DNM1LPFASFTOLRP4MGAT1PNNhsa-miR-6838-5p DCNDPT mmu-miR-466d-5pHMOX2 FOXO3PRDM16 mmu-miR-1192 POU2F1 PTPRSRELAICMTTIMM23PPP1R13BMYO6IRF2 JAG2MKLN1 FMOD FSTL1SCARA5 STIM2 MAF1 VAC14TFAM T SIRT6CREBBPF3NOTCH3BIN1TRIM21GUSB SGSHHPS4 ATP2C1 MAPKAPK5KRAS CBY1CBL TNRC6AAFAP1L2SPENRAF1RPS19GAK CDK5RAP2 RNF8 CLOCKAGAmmu-miR-298-5p TCF3 PYGO2HOXD9 PDGFASRA1 FBN1FBLN5 DICER1 CELSR1LIPH PKNOX1TRAF3IP2MPP5 PHB mmu-miR-466n-5p PYGO2HDAC4LRIG2 CENPJ hsa-miR-424-5p PRKDC NCOA3 BBS2 SLC7A7LMNB1 GBF1 ETS1 LSR MEFV UACAhsa-miR-320aCOL5A3 ICMT HSF1OSTM1mmu-miR-15a-5pmmu-miR-669f-3p hsa-let-7c-5pB4GALT1hsa-miR-101-3p ABCD4 mmu-miR-22-3p hsa-miR-150-5p mmu-miR-132-3pmmu-miR-182-5pmmu-miR-25-3p ARID4A MDM2MGRN1 hsa-miR-125b-5p mmu-miR-466k HNF1A FGFR1 NOTCH1MAEA IFT27 IL17REGNAZ hsa-miR-29b-3p hsa-miR-3929 mmu-let-7b-5p hsa-miR-149-3p NR2F6 AKT2

hsa-miR-29a-3p mmu-miR-34a-5p Human Mouse (a) (b)

Figure 7: The interaction networks between IGs and miRNA family. (a) For human and (b) for mouse. The purple nodes represent miRNAs and the blue nodes represent target gene.

inhibit the host immunity response [54]. It is necessary to further detect the role of these miRNAs, miRNA/IGs specific carryouttheresearchonhowthesemiRNAscontributeto regulatory networks were added in IGs expression correlated integument morphogenesis in the future. When we added networks, which will advance the dimension to skin regula- the relationship of miRNAs and their target genes by three tion networks. hsa-miR-548, mmu-miR-466,andmmu-miR- prediction algorithms to the expression correlation networks, 467 have an enormous number of targets on IGs, which those two clusters in gene regulatory networks of human both have the role of inhibition of host immunity response. have been integrated, which add a new dimension to genetic The regulations of factors to downstream genes networks under human integument. The changes of the hub and miRNA to transcription factors affect the spatial and gene in gene regulatory networks result in the evolutionary temporal expression of genes during skin morphogenesis. alternation and the morphological difference [29]. Our results provide a new avenue to understand the genetic networks basis of skin difference between human and mouse. 5. Conclusions Competing Interests Genetic networks variants have been shown to affect many complex traits, including integument morphology. In this The authors declare that there are no competing interests. study, we try to compare the regulatory networks of miRNAs andIGsintheskinofhumanandmouse.Wedownloaded mRNAexpressiondatainhumanandmouseskinfromthe Authors’ Contributions GEO database to create within-species consensus networks by WGCNA. There is a big difference in consensus networks Jianghong Wu and Husile Gong contributed equally to this between human and mouse; human consensus networks are work. more complex compared to mouse. Two principal regulatory networks were found in human: one module contains 286 IGs Acknowledgments specifically involved in secretion, whereas the other module contains 250 IGs which are enriched for cellular response to The authors thank Lizhong Ding, Department of Biology, stress and catabolic process. The secretion category, which is IndianaStateUniversity,USA,forRscriptsandthank specific category for human compared to mouse, of the genes Ethan Rath, Department of Biology, Indiana State University, mightbeinvolvedineccrinesysteminhuman.Then,total USA, for polishing manuscript and thank the Center for 62,637 miRNA binding target sites were predicted in human Genomic Advocacy at Indiana state University for provid- IGs, while 26,280 miRNA binding target sites were predicted ing the workspace and cutting-edge computational server. in mouse IGs. The interactions between miRNAs and IGs This work was supported by the National Natural Science of human are also more complex compared to mouse. To Foundation of China (31560623 to Dr. Jianghong Wu and 8 BioMed Research International

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