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Oncotarget logoLink to Oncotarget
. 2016 Nov 16;7(52):86266–86279. doi: 10.18632/oncotarget.13379

Assessment of differentially expressed plasma microRNAs in nonsyndromic cleft palate and nonsyndromic cleft lip with cleft palate

Jingyun Li 1,#, Jijun Zou 2,#, Qian Li 1, Ling Chen 1, Yanli Gao 1, Hui Yan 1, Bei Zhou 1, Jun Li 1
PMCID: PMC5349912  PMID: 27863433

Abstract

Plasma microRNAs (miRNAs) have recently emerged as a new class of regulatory molecules that influence many biological functions. However, the expression profile of plasma microRNAs in nonsyndromic cleft palate (NSCP) or nonsyndromic cleft lip with cleft palate (NSCLP) remains poorly investigated. In this study, we used Agilent human miRNA microarray chips to monitor miRNA levels in three NSCP plasma samples (mixed as the CP group), three NSCLP plasma samples (mixed as the CLP group) and three normal plasma samples (mixed as the Control group). Six selected plasma miRNAs were validated in samples from an additional 16 CP, 33 CLP and 8 healthy children using qRT-PCR. Using Venn diagrams, distinct and overlapping dysregulated miRNAs were identified. Their respective target genes were further assessed using gene ontology and pathway analysis. The results show that distinct or overlapping biological processes and signalling pathways were involved in CP and CLP. Our study showed that the common key gene targets reflected functional relationships to the Notch, Wnt, phosphatidylinositol and Hedgehog signalling pathways. Further studies should examine the mechanism of the potential target genes, which may provide new avenues for future clinical prevention and therapy.

Keywords: nonsyndromic cleft palate, nonsyndromic cleft lip with cleft palate, plasma microRNA, miRNA microarray

INTRODUCTION

Orofacial clefts include cleft palate only (CPO), cleft lip with cleft palate (CLP) and cleft lip only (CLO). Approximately 1/800 live births worldwide are affected by these diseases [1]. Nonsyndromic orofacial clefts occur as isolated entities with no other apparent structural and/or developmental abnormalities. The majority of CLP cases are nonsyndromic (NS) (∼70%) [2]. Cleft palate only (CPO) is the least common form of the orofacial clefts (approximately 33%) [3]. The aetiology is multifactorial and involves both genetic and environmental risk factors. Most studies suggest that distinct etiological mechanisms underlie CLP and CPO [4, 5]; however, some overlapping exists in their aetiologies [6, 7]. Thus, our knowledge about whether CPO does indeed differ from CLP remains incomplete.

Mounting evidence suggests that miRNAs could be functionally important for the regulation of vertebrate and mammalian orofacial clefting [810]. The identification of the miRNA-mRNA regulatory molecules is important to understand the regulatory mechanisms of CLP and CPO. The potential of plasma miRNAs to be potential non-invasive diagnostic biomarkers for nonsyndromic cleft lip in infants has been reported [11]. In this study, based on our Agilent human miRNA microarray chips, we identified differentially expressed plasma microRNAs in nonsyndromic cleft palate and nonsyndromic cleft lip with cleft palate. Using gene ontology and pathway analysis of the target genes of these miRNAs, we report that distinct biological processes interact and coordinate the physiology of nonsyndromic cleft palate and nonsyndromic cleft lip with cleft palate.

RESULTS

miRNA microarray analysis

To investigate whether circulating miRNAs are associated with the pathogenesis of cleft palate and cleft lip with cleft palate, plasma samples were collected from healthy children and children with nonsyndromic cleft palate (NSCLP) and cleft lip with cleft palate (NSCLP). A comprehensive miRNA microarray analysis was performed on nine plasma samples, including three NSCP plasma samples (mixed as the CP group), three NSCLP plasma samples (mixed as the CLP group) and three plasma samples from healthy children (mixed as the Control group). Hierarchical clustering was used to show the miRNAs expression variation and patterns among these three groups CP, CLP and Control (Figure 1A). We also screened for differentially expressed miRNAs that showed a two-fold or greater change (compared to the control group) in the CP and CLP plasma samples, and illustrated the overlapping between the two data sets using Venn diagrams (Figure 1B, 1C). A total of 63 miRNAs were upregulated in both the CP and CLP plasma samples (Figure 1B). The upregulated miRNAs are listed in Table 1 (fold change ≥ 2). Furthermore, 49 miRNAs were downregulated in both the CP and CLP plasma samples (Figure 1C). The downregulated miRNAs are listed in Table 2 (fold change ≥ 2).

Figure 1. plasma miRNA microarray expression data among the patients with nonsyndromic cleft palate, nonsyndromic cleft lip with cleft palate and healthy children.

Figure 1

(A) Hierarchical clustering reveals the miRNA expression profile. (B) Venn diagrams show the number of distinct and overlapping upregulated miRNAs in CP and CLP. (C) Venn diagrams show the number of distinct and overlapping downregulated miRNAs in CP and CLP.

Table 1. List of 63 miRNAs that were co-overexpressed in CP and CLP plasma samples.

miRNA Fold change in CP Fold change in CLP Mirbase_accession_No
hsa-let-7a-5p 2.593625 3.62266 MIMAT0000062
hsa-let-7f-5p 36.22597 94.70817 MIMAT0000067
hsa-miR-1182 21.12385 26.03614 MIMAT0005827
hsa-miR-1185-2-3p 35.17025 22.02562 MIMAT0022713
hsa-miR-1226-5p 33.54898 41.68948 MIMAT0005576
hsa-miR-1249-3p 40.65461 26.77842 MIMAT0005901
hsa-miR-126-5p 22.59582 11.5428 MIMAT0000444
hsa-miR-139-3p 9.764356 30.04384 MIMAT0004552
hsa-miR-144-5p 22.1287 34.8168 MIMAT0004600
hsa-miR-150-5p 2.004063 5.130724 MIMAT0000451
hsa-miR-17-3p 25.74652 10.66161 MIMAT0000071
hsa-miR-194-5p 2.004321 2.671813 MIMAT0000460
hsa-miR-195-5p 11.29176 10.05747 MIMAT0000461
hsa-miR-215-5p 10.32331 24.41001 MIMAT0000272
hsa-miR-27b-3p 10.79146 4.974262 MIMAT0000419
hsa-miR-301a-3p 2.216769 2.039103 MIMAT0000688
hsa-miR-30b-5p 43.5225 53.10868 MIMAT0000420
hsa-miR-30c-1-3p 9.047681 4.742653 MIMAT0004674
hsa-miR-30c-5p 10.41076 4.763296 MIMAT0000244
hsa-miR-3156-5p 39.43275 33.64425 MIMAT0015030
hsa-miR-3158-5p 36.59335 29.4613 MIMAT0019211
hsa-miR-340-5p 4.435901 10.44386 MIMAT0004692
hsa-miR-3648 2.844126 7.529149 MIMAT0018068
hsa-miR-374a-5p 41.436 53.56016 MIMAT0000727
hsa-miR-3945 54.65085 55.2666 MIMAT0018361
hsa-miR-424-5p 11.5428 23.91295 MIMAT0001341
hsa-miR-4314 48.6155 91.10153 MIMAT0016868
hsa-miR-4417 39.91649 27.81709 MIMAT0018929
hsa-miR-4496 21.48931 25.86879 MIMAT0019031
hsa-miR-4508 20.71299 22.6453 MIMAT0019045
hsa-miR-4673 23.30205 21.36562 MIMAT0019755
hsa-miR-4688 9.853577 37.87732 MIMAT0019777
hsa-miR-4698 2.111651 2.899994 MIMAT0019793
hsa-miR-4707-3p 53.56016 88.45938 MIMAT0019808
hsa-miR-4716-3p 25.26925 22.1287 MIMAT0019827
hsa-miR-4734 10.01005 24.63449 MIMAT0019859
hsa-miR-4769-5p 10.66161 4.959765 MIMAT0019922
hsa-miR-532-5p 10.62257 4.541014 MIMAT0002888
hsa-miR-550a-3-5p 22.36834 10.8752 MIMAT0020925
hsa-miR-564 9.835221 47.83535 MIMAT0003228
hsa-miR-601 35.01135 36.03937 MIMAT0003269
hsa-miR-6071 21.2377 52.38286 MIMAT0023696
hsa-miR-6133 10.44386 27.50138 MIMAT0024617
hsa-miR-652-3p 22.6453 4.860917 MIMAT0003322
hsa-miR-660-5p 24.63449 28.21952 MIMAT0003338
hsa-miR-663a 28.76448 47.35553 MIMAT0003326
hsa-miR-6738-5p 9.491392 40.3395 MIMAT0027377
hsa-miR-6748-5p 10.37443 20.60795 MIMAT0027396
hsa-miR-6758-5p 23.39996 9.81465 MIMAT0027416
hsa-miR-6774-5p 20.60795 4.699301 MIMAT0027448
hsa-miR-6777-3p 35.46738 53.8186 MIMAT0027455
hsa-miR-6784-5p 33.23554 28.4822 MIMAT0027468
hsa-miR-6793-5p 23.91295 3.695676 MIMAT0027486
hsa-miR-6807-5p 27.50138 34.4576 MIMAT0027514
hsa-miR-6820-5p 22.91788 39.11779 MIMAT0027540
hsa-miR-6887-5p 28.4822 22.30914 MIMAT0027674
hsa-miR-6889-5p 9.57147 39.91649 MIMAT0027678
hsa-miR-7109-5p 22.30914 20.89674 MIMAT0028115
hsa-miR-711 21.61314 28.39723 MIMAT0012734
hsa-miR-7114-5p 9.41187 23.30205 MIMAT0028125
hsa-miR-8060 29.71088 24.85582 MIMAT0030987
hsa-miR-8089 9.716781 22.36834 MIMAT0031016
hsa-miR-877-5p 36.42551 38.19915 MIMAT0004949

Table 2. List of 49 miRNAs that were co-downregulated in CP and CLP plasma samples.

miRNA Fold change in CP Fold change in CLP Mirbase_accession_No
hsa-miR-122-5p 0.014823713 0.330486 MIMAT0000421
hsa-miR-1237-3p 0.04279959 0.178651 MIMAT0005592
hsa-miR-1260a 0.42197306 0.188688 MIMAT0005911
hsa-miR-1281 0.292045994 0.361803 MIMAT0005939
hsa-miR-1304-3p 0.022515879 0.021418 MIMAT0022720
hsa-miR-1825 0.305341608 0.315741 MIMAT0006765
hsa-miR-191-3p 0.210027916 0.202306 MIMAT0001618
hsa-miR-193a-5p 0.425119659 0.1725 MIMAT0004614
hsa-miR-221-3p 0.38537867 0.317683 MIMAT0000278
hsa-miR-3187-3p 0.013885943 0.013209 MIMAT0015069
hsa-miR-338-3p 0.04279959 0.214453 MIMAT0000763
hsa-miR-4286 0.481616926 0.148742 MIMAT0016916
hsa-miR-4290 0.04279959 0.040714 MIMAT0016921
hsa-miR-4428 0.354170872 0.170319 MIMAT0018943
hsa-miR-4433a-5p 0.207866953 0.084119 MIMAT0020956
hsa-miR-4455 0.422613454 0.365739 MIMAT0018977
hsa-miR-4649-3p 0.200187171 0.091757 MIMAT0019712
hsa-miR-4668-5p 0.389545483 0.170031 MIMAT0019745
hsa-miR-4728-5p 0.471992677 0.412945 MIMAT0019849
hsa-miR-4738-3p 0.356057291 0.151948 MIMAT0019867
hsa-miR-4749-3p 0.431131255 0.458677 MIMAT0019886
hsa-miR-4769-3p 0.299500434 0.328468 MIMAT0019923
hsa-miR-483-3p 0.025346248 0.25173 MIMAT0002173
hsa-miR-494-3p 0.421072365 0.074374 MIMAT0002816
hsa-miR-497-5p 0.26762079 0.401373 MIMAT0002820
hsa-miR-574-3p 0.032438225 0.165143 MIMAT0003239
hsa-miR-574-5p 0.398577728 0.302998 MIMAT0004795
hsa-miR-636 0.405198518 0.173279 MIMAT0003306
hsa-miR-6508-5p 0.021489753 0.091996 MIMAT0025472
hsa-miR-6515-3p 0.233030948 0.255185 MIMAT0025487
hsa-miR-6732-3p 0.029496564 0.129832 MIMAT0027366
hsa-miR-6751-3p 0.096354218 0.272251 MIMAT0027403
hsa-miR-6776-5p 0.023690732 0.022536 MIMAT0027452
hsa-miR-6785-5p 0.292436258 0.215104 MIMAT0027470
hsa-miR-6797-3p 0.486474133 0.356753 MIMAT0027495
hsa-miR-6800-3p 0.207096926 0.209525 MIMAT0027501
hsa-miR-6813-3p 0.295100077 0.244702 MIMAT0027527
hsa-miR-6851-3p 0.02840522 0.109846 MIMAT0027603
hsa-miR-6861-3p 0.02773473 0.119237 MIMAT0027624
hsa-miR-6870-3p 0.04279959 0.040714 MIMAT0027641
hsa-miR-6873-3p 0.022068915 0.020993 MIMAT0027647
hsa-miR-6880-3p 0.04279959 0.040714 MIMAT0027661
hsa-miR-7111-3p 0.02234751 0.227218 MIMAT0028120
hsa-miR-7114-3p 0.04279959 0.180041 MIMAT0028126
hsa-miR-7641 0.466679177 0.29544 MIMAT0029782
hsa-miR-766-3p 0.248388555 0.280315 MIMAT0003888
hsa-miR-7975 0.443053554 0.153591 MIMAT0031178
hsa-miR-7977 0.425407891 0.15303 MIMAT0031180
hsa-miR-8073 0.04279959 0.462327 MIMAT0031000

Expression validation of selected miRNAs using Bulge-Loop™ qRT-PCR analysis

Among the 63 upregulated and 49 downregulated miRNAs in both the CP and CLP plasma samples, six miRNAs, namely miR-340–5p, miR-877–5p, miR-3648, miR-1260a, miR-494–3p, and miR-1304–3p, were selected for expression validation (Table 3). The selection standards were the same as we previously reported [11] and were as follows: infrequently reported in the literature, present in both samples and higher deep sequencing reads in miRBase. Bulge-Loop™ qRT-PCR was performed to validate these six differentially expressed miRNAs found in the miRNA microarray analysis. RNA was isolated from 57 plasma samples, including samples from 16 CP, 33 CLP and 8 healthy children, using the mirVana PARIS kit (Ambion, Carlsbad, CA, USA). The results showed that three miRNAs, miR-340–5p, miR-877–5p and miR-3648, were significantly upregulated in both the CP and CLP plasma samples (Figure 2). In contrast, three miRNAs, miR-1260a, miR-494–3p and miR-1304–3p, were significantly downregulated in both the CP and CLP plasma samples (Figure 2). Therefore, a similar pattern of upregulation and downregulation was observed in both the microarray and qRT-PCR samples for the 6 miRNAs assessed (Table 3, Figure 2). Therefore, our microarray data were reliable and stable.

Table 3. Selected six miRNAs’ basic characteristics.

miRNA Fold change in CP Fold change in CLP Dysregulation miRBase deep sequencing reads
miR-340-5p 4.435901 10.44386 up 329
miR-877-5p 36.42551 38.19915 up 2840
miR-3648 2.844126 7.529149 up 650
miR-1260a 0.42197306 0.188688 down 29
miR-494-3p 0.421072365 0.074374 down 153
miR-1304-3p 0.022515879 0.021418 down 4

Figure 2. Relative expression level (2−ΔΔCt) of miR-340-5p, miR-877-5p, miR-3648, miR-1260a, miR-494-3p, and miR-1304-3p expression in the plasma of cleft palate patients (n =16), cleft lip with cleft palate patients (n = 33) and controls (n = 8) (Mann-Whitney U test).

Figure 2

P values are listed above the data dot.

Functional analysis of potential target genes of co-expressed dysregulated miRNAs

To explore whether overlapping biological processes were found in both CLP and CPO, we performed gene ontology (GO) and KEGG pathway analysis for the predicted targets of the differentially expressed miRNAs, including the 63 upregulated and 49 downregulated miRNAs in both CP and CLP, which produced 4227 and 2684 predictive target genes, respectively. The top ten enriched GO terms for the upregulated and downregulated are listed in Figure 3A and 3B, respectively. The analysis revealed that the potential target genes of the 63 miRNAs upregulated in both CP and CLP were associated with glutamate secretion, aorta or palate development, positive regulation of axonogenesis, cardiac septum development, artery development, the canonical Wnt signalling pathway, and the ephrin receptor signalling pathway (Figure 3A). Conversely, the potential target genes of the 49 miRNAs downregulated in both CP and CLP were associated with the generation of contraction-related action potentials in cardiac muscle cells, cardiac muscle cell contraction, columnar/cuboidal epithelial cell development, cardiac conduction, positive regulation of axonogenesis, and the ephrin receptor signalling pathway (Figure 3B). Pathway enrichment analysis revealed that the potential target genes of the miRNAs dysregulated in both CP and CLP were involved in p53 signalling, Wnt signalling, circadian rhythm, insulin resistance, and the AMPK signalling pathway (Figure 3C, 3D).

Figure 3. GO and KEGG pathway analyses show the associated function of the target genes of the miRNAs dysregulated in both CP and CLP.

Figure 3

(A) The top 10 enriched GO terms for the 4227 target genes of the 63 miRNAs upregulated in both CP and CLP. (B) The top 10 enriched GO terms for the 2684 target genes of the 49 miRNAs downregulated in both CP and CLP. (C) The top 30 enriched pathways for the 4227 target genes of the 63 miRNAs upregulated in both CP and CLP. (D) The top 30 enriched pathways for the 2684 target genes of the 49 miRNAs downregulated in both CP and CLP. (C-D) The enrichment P values were calculated using Fisher's exact test. The term/pathway on the vertical axis was drawn according to the first letter of the pathway name in descending order. The horizontal axis represents the enrichment factor, i.e., (the number of dysregulated genes in a pathway/the total number of dysregulated genes)/(the number of genes in a pathway in the database/the total number of genes in the database). The top 30 enriched pathways were selected according to the enrichment factor value. The selection standards were the number of genes in a pathway ≥ 4 and p < 0.05. The different colours from green to red represent the p-value. The different sizes of the round shapes represent the gene count number in a pathway. *indicates the pathway is similarly regulated in both CP and CLP. # means the pathway is regulated in both CP and CLP but the miRNA target genes may be upregulated or downregulated.

Function analysis of the potential target genes of the miRNAs dysregulated in CP

To explore whether distinct biological processes regulated CLP and CPO, we performed gene ontology (GO) and KEGG analysis of the predicted targets of the differentially expressed miRNAs in CP, including 14 upregulated miRNAs producing 1057 predictive target genes and 6 downregulated miRNAs yielding 363 potential target genes. The top ten enriched GO terms are listed in Figure 4A and 4B, respectively. The analysis revealed that the potential target genes of the 14 miRNAs upregulated in CP were associated with hippo signalling, dendrite morphogenesis or development, positive regulation of smooth muscle cell proliferation, neural tube formation and developmental cell growth (Figure 4A). Conversely, the potential target genes of the 6 miRNAs downregulated in CP were associated with modulation of synaptic transmission, blood vessel or tissue morphogenesis, regulation of cell morphogenesis or cell differentiation and neurogenesis (Figure 4B). Surprisingly, 1057 of the predictive target genes of the 14 miRNAs upregulated in CP did not display KEGG enrichment results using the SBC analysis system. For the 363 potential target genes of the 6 miRNAs downregulateCP, the pathway enrichment results showed that those genes were mainly involved in thyroid cancer, the Notch signalling pathway, fatty acid metabolism, adherens junctions and amphetamine addiction (Figure 4C).

Figure 4. GO and KEGG pathway analyses show the associated function of the target genes of the miRNAs dysregulated in CP.

Figure 4

(A) The top 10 enriched GO terms for the 1057 target genes of the 14 miRNAs upregulated in CP. (B) The top 10 enriched GO terms for the 363 target genes of the 6 miRNAs downregulated in CP. (C) Top 30 enriched pathways for the 363 target genes of the 6 miRNAs downregulated in CP. The enrichment P values were calculated using Fisher's exact test. The term/pathway on the vertical axis was drawn according to the first letter of the pathway name in descending order. The horizontal axis represents the enrichment factor, i.e., (the number of dysregulated genes in a pathway/the total number of dysregulated gene)/(the number of genes in a pathway in the database/the total number of genes in the database). Top 30 enriched pathways were selected according to the enrichment factor value. The selection standards were the number of genes in a pathway ≥ 4 and p < 0.05. The different colours from green to red represent the p value. The different sizes of the round shapes represent the gene count number in a pathway. * indicates the pathway is similarly regulated in both CP and CLP.

Function analysis of the potential target genes of the miRNAs dysregulated in CLP

Additionally, we performed gene ontology (GO) and KEGG analysis of the predicted targets of the differentially expressed miRNAs in CLP, including 75 upregulated miRNAs yielding 3505 possible target genes and 34 downregulated miRNAs creating 1666 possible target genes. The top ten enriched GO terms are listed in Figure 5A and 5B, respectively. The analysis revealed that the potential target genes of the 75 miRNAs upregulated in CLP were associated with the regulation of axon extension during axon guidance, the semaphorin-plexin signalling pathway, the epidermal growth factor receptor signalling pathway, the regulation of axon extension, histone methylation and the extent of cell growth (Figure 5A). Conversely, the potential target genes of the 34 miRNAs downregulated in CLP were associated with glutamate secretion, the positive regulation of axon extension, multicellular organism growth, the extent of cell growth, neurotransmitter transport and the regulation of synaptic plasticity (Figure 5B). Pathway enrichment analysis indicated that the potential target genes of the miRNAs dysregulated in CLP were associated with specific pathways, including the thyroid hormone signalling pathway, the GnRH signalling pathway, the Hedgehog signalling pathway, and the longevity regulating pathway (Figure 5C, 5D).

Figure 5. GO and KEGG pathway analyses show the associated function of the target genes of the miRNAs dysregulated in CLP.

Figure 5

(A) The top 10 enriched GO terms for the 3505 target genes of the 75 miRNAs upregulated in CLP. (B) The top 10 enriched GO terms for the 1666 target genes of the 34 miRNAs downregulated in CLP. (C) Top 30 enriched pathways for the 3505 target genes of the 75 miRNAs upregulated in CLP. (D) Top 30 enriched pathways for the 1666 target genes of the 34 miRNAs downregulated in CLP. (C–D) The enrichment P values were calculated using Fisher's exact test. The term/pathway on the vertical axis was drawn according to the first letter of the pathway name in descending order. The horizontal axis represents the enrichment factor, i.e., (the number of dysregulated genes in a pathway/the total number of dysregulated genes)/(the number of genes in a pathway in the database/the total number of genes in the database). The top 30 enriched pathways were selected according to the enrichment factor value. The selection standards were the number of genes in a pathway ≥ 4 and p < 0.05. The different colours from green to red represent the p value. The different sizes of the round shapes represent the gene count number in a pathway. * indicates the pathway is similarly regulated in both CLP and CP.

DISCUSSION

Plasma miRNAs are highly stabile under handling and storage conditions [12]. They play crucial roles in many diseases including cancer [13, 14] and respiratory diseases [15]. Nonsyndromic cleft palate and nonsyndromic cleft lip with cleft palate are two types of oral clefting that occur without other developmental syndromes. Research on the roles of differentially expressed plasma miRNAs in NSCP and NSCLP patients will improve our knowledge, diagnosis and management of the two diseases. This study, for the first time, has used microarray profiling to evaluate the differential expression of miRNAs in plasma samples from NSCP and NSCLP patients compared with healthy children. Six miRNAs, namely miR-340–5p, miR-877–5p, miR-3648, miR-1260a, miR-494–3p, and miR-1304–3p, were found to be differentially expressed in both the NSCP and NSCLP plasma samples.

Venn diagrams have been widely used to visualize the relationship between complex genetic data sets [16, 17]. Based on our miRNA microarray data, we found the intersection of differentially expressed miRNAs in CP and CLP. Using Bulge-Loop™ qRT-PCR analysis, we demonstrated that the upregulated and downregulated miRNAs were consistent with the results of the miRNA microarray assay. Therefore, we investigated the key genes and pathways associated with CP and/or CLP using bioinformatics analysis according to the microarray data. In mammals, miRNAs could regulate 30% of the protein-coding genes through posttranscriptional silencing; therefore, the dysregulation of miRNAs in NSCP or NSCLP patients could have a profound influence on various biological functions.

Based on the GO analysis, the predicted target genes of the upregulated and downregulated miRNAs in both CP and CLP mainly participated in glutamate secretion, aorta or palate development, cardiac muscle cell contraction, and the ephrin receptor signalling pathway. Examining the top 30 enriched pathways showed that the target genes were solely associated with several pathways, including the AGE-RAGE signalling pathway in diabetic complications, retrograde endocannabinoid signalling, signalling pathways regulating the pluripotency of stem cells, the adipocytokine signalling pathway, arrhythmogenic right ventricular cardiomyopathy (ARVC), cocaine addiction, dilated cardiomyopathy, gastric acid secretion, glycerolipid metabolism, glycosaminoglycan biosynthesis-chondroitin sulphate/dermatan sulphate, glycosaminoglycan biosynthesis-keratan sulphate, hypertrophic cardiomyopathy (HCM), the p53 signalling pathway and mucin type O-Glycan biosynthesis. Consistent with this prediction, cleft palate and cleft lip with cleft palate may be associated with a wide range of signalling molecules, including transforming growth factors (TGFs) [18], bone morphogenetic proteins (BMPs) [19], and fibroblast growth factors (FGFs) [20]. In addition, various developmental transcription factors belonging to the paired box (PAX) [21], distal-less homeobox (DLX) [22], msh homeobox (MSX) [23], and T-Box (TBX) gene families [24] are also involved in CP and CLP.

Recent studies suggest that isolated cleft palate only (CPO) has independent genetic causes and should be evaluated separately [25, 26]. Therefore, we analysed the predictive target genes of nonoverlapping miRNAs in CP and CLP using GO and KEGG pathway analysis. Interestingly, the top 10 enriched GO terms showed that Hippo signalling, dendrite morphogenesis, modulation of synaptic transmission and tissue morphogenesis were related to CP. Thirteen pathways of the top 30 enriched pathways were uniquely associated with CP including 2-oxocarboxylic acid metabolism, adrenergic signalling in cardiomyocytes, breast cancer, the cAMP signalling pathway, colorectal cancer, the oestrogen signalling pathway, fatty acid metabolism, gap junctions, HTLV-I infection, the MAPK signalling pathway, pathways in cancer, the ras signalling pathway, and tight junctions. In contrast, 14 pathways of the top 30 enriched pathways were only linked with CLP including dopaminergic synapses, endocytosis, the GnRH signalling pathway, morphine addiction, non-small cell lung cancer, renin secretion, acute myeloid leukaemia, bile secretion, dorso-ventral axis formation, endometrial cancer, renal cell carcinoma, shigellosis, SNARE interactions in vesicular transport, and Sphingolipid metabolism.

NSCP and NSCLP are developmental defects. The differentially expressed plasma miRNAs identified in the affected infants could be from their mother's blood. Further elucidation of the sources of the plasma miRNAs in human tissues and their roles in the pathogenesis of cleft palate or cleft lip with cleft palate, particularly in palate development or the regulation of cranial neural crest (CNC) cells that give rise to craniofacial structures, needs to be further explored. In addition, larger samples are needed to perform receiver operating characteristic (ROC) curve analysis to prove that some of the children plasma microRNAs are promising biomarkers.

Taken together, based on the Bulge-Loop™ qRT-PCR analysis and miRNA microarray assay, we uncovered a differential plasma miRNA expression profile in NSCP and NSCLP patients compared with healthy children. Using GO and KEGG pathway analysis, we found distinct and overlapping biological processes or signalling pathways involved in CP and CLP. Further studies should examine the mechanism of the potential target genes, which may provide new avenues for future clinical prevention and therapy.

MATERIALS AND METHODS

Sample collection

The study protocol was approved by the Institutional Review Board of Nanjing Medical University (2014–10–16). The plasma samples were collected according to previously described methods [11]. Nonsyndromic cleft palate (NSCP) and nonsyndromic cleft lip with cleft palate (NSCLP) patients who underwent surgery and healthy children (normal face but who underwent surgery for hydrocele) participated in this study with their parent's consent at the Department of Burns and Plastic Surgery, Nanjing Children's Hospital Affiliated to Nanjing Medical University in Nanjing, China. All NSCP, NSCLP and healthy children were between 2 and 12 months old. The patient information is listed in Table 4. Briefly, peripheral blood was collected into EDTAK2 tubes (regular type), and then immediately centrifuged at 1000 g for 15 min. The supernatant plasma was transferred to RNase-free tubes and centrifuged at 12000 g for 10 min to pellet any remaining cellular debris. Aliquots of the supernatant were transferred to fresh tubes and immediately stored at –80°C.

Table 4. Patient characteristics.

Group Age (m, months; d, days) Gender Sample use
Control 9m12d female Array
Control 3m30d male Array
Control 10m male Array
Control 6m female qRT-PCR
Control 9m female qRT-PCR
Control 12m female qRT-PCR
Control 3m male qRT-PCR
Control 9m male qRT-PCR
Control 3m male qRT-PCR
Control 6m male qRT-PCR
Control 5m male qRT-PCR
CP 3m female Array
CP 6m female Array
CP 5m male Array
CP 10m7d female qRT-PCR
CP 12m female qRT-PCR
CP 3m female qRT-PCR
CP 8m28d female qRT-PCR
CP 9m20d female qRT-PCR
CP 10m9d female qRT-PCR
CP 11m13d female qRT-PCR
CP 8m female qRT-PCR
CP 12m female qRT-PCR
CP 12m female qRT-PCR
CLP 7m30d male Array
CLP 4m male Array
CLP 4m21d male Array
CLP 2m22d female qRT-PCR
CLP 4m17d female qRT-PCR
CLP 12m female qRT-PCR
CLP 10m25d female qRT-PCR
CLP 12m female qRT-PCR
CLP 10m24d female qRT-PCR
CLP 5m female qRT-PCR
CLP 7m female qRT-PCR
CLP 12m female qRT-PCR
CLP 11m female qRT-PCR
CLP 3m21d female qRT-PCR
CLP 5m30d female qRT-PCR
CLP 4m12d male qRT-PCR
CLP 3m12d male qRT-PCR
CLP 4m23d male qRT-PCR
CLP 3m20d male qRT-PCR
CLP 2m10d male qRT-PCR
CLP 2m male qRT-PCR
CLP 9m29d male qRT-PCR
CLP 12m male qRT-PCR
CLP 12m male qRT-PCR
CLP 11m3d male qRT-PCR
CLP 3m male qRT-PCR
CLP 6m male qRT-PCR
CLP 9m10d male qRT-PCR
CLP 12m male qRT-PCR
CLP 11m male qRT-PCR
CLP 4m21d male qRT-PCR
CLP 3m30d male qRT-PCR
CLP 11m12d male qRT-PCR

CP, cleft palate; CLP, cleft lip with palate.

Total RNA isolation from human plasma samples

Total RNA was isolated from 400 μl of human plasma samples using the mirVana PARIS kit (Ambion, Carlsbad, CA, USA) according to the manufacturer's instructions. After infusing an equal volume of 2x denaturing solution to the plasma samples to inactivate RNases, the denatured samples were mixed with Synthetic Caenorhabditis elegans miRNA cel-miR-39 (GenePharma, Shanghai, China), to normalize the variation in RNA isolation from the different samples. RNA was eluted using 100 μl of elution solution.

Mature miRNA microarray analysis

Nine samples, which included three NSCP plasma samples (mixed as the CP group), three NSCLP plasma samples (mixed as the CLP group) and three normal plasma samples (mixed as the Control group), were analysed using Agilent human miRNA microarray chips (8*60K) v21.0 (ShanghaiBio Corporation, Shanghai, China). The raw data were normalized using the quantile algorithm in GeneSpring Software 12.6 (Agilent Technologies, Santa Clara, CA, USA). The differentially expressed miRNAs that showed a two-fold or greater change were screened. Venn diagrams were draw online (http://bioinformatics.psb.ugent.be/webtools/Venn/).

Validation of the microarray data using Bulge-Loop™ miRNA qRT-PCR

To confirm the microarray data, six selected miRNAs with two-fold or greater changes were further validated in samples from an additional 16 CP, 33 CLP and 8 healthy children using Bulge-Loop™ qRT-PCR according to the manufacturer's protocol (RIBOBIO, Guangzhou, China) with SYBR green on an Applied Biosystems ViiA™ 7 Dx (Life Technologies, USA). The expression levels of the miRNAs were normalized to C. elegans control miRNA cel-39 using the 2(–△△Ct) method [27].

Statistical analysis

The validation results from the qRT-PCR analysis are displayed as the mean ± SD. Statistical significance was assessed using the Mann-Whitney U test in Graphpad Prism 6 (Graphpad software, CA, USA). A P < 0.05 was considered statistically significant.

GO and pathway enrichment analyses

The differentially expressed miRNAs were further analysed for predicted gene targets simultaneously using at least two of the following five databases: TARGETMINER, miRDB, microRNA, TarBase, and RNA22 through the ShanghaiBio Corporation (SBC) analysis system (http://sas.ebioservice.com). GO enrichment analyses were performed online (http://geneontology.org/). KEGG pathway enrichment analyses of the predicted targets of the differentially expressed miRNAs were performed according to previously described methods [11]. KEGG pathway enrichment analyses were performed by the ShanghaiBio Corporation (SBC) analysis system, which uses clusterProfiler data from R/bioconductor software (http://www.r-project.org and http://www.bioconductor.org/) with public databases that include NCBI Entrez Gene (http://www.ncbi.nlm.nih.gov/gene), GO (http://www.geneontology.org), KEGG (http://www.genome.jp/kegg), and Biocarta (http://www.biocarta.com). The enrichment P-values of both the GO and pathway enrichment analyses were calculated using the Fisher's exact test [28], which was corrected using enrichment q-values (the false discovery rate) that were calculated using John Storey's method [29].

Acknowledgments

This study was supported by the National Natural Science Foundation of China (81501672), the Natural Science Foundation of Jiangsu Province (BK20140083), the Nanjing Medical Science and Technique Development Foundation (YKK15159), and the Nanjing Science and Technology project (201503047).

Footnotes

CONFLICTS OF INTEREST

The authors declare that they have no conflicts of interests.

Authors’ contributions

Jun Li planned the experiment and conducted the data analyses. Jingyun Li and Jijun Zou performed the sample preparation and validation of the specific plasma miRNAs. Qian Li and Ling Chen performed the bioinformatics analyses. Yanli Gao, Hui Yan, and Bei Zhou performed the statistical analyses. Jingyun Li and Jun Li wrote and edited the manuscript.

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