Upper respiratory airways are lined by pseudostratified columnar epithelium, composed of ciliated cells, goblet cells, and basal cells. The airway epithelium functions as a barrier that protects the lungs from inhaled pathogens and environmental particles [
Damage to the airway epithelium might result in loss of barrier function and mucosal activation [
MicroRNAs are small, noncoding RNA molecules that regulate gene expression either by degrading the target mRNA or by acting as translational enhancers or repressors. Several studies have shown their importance as regulators in development [
NHBE cell line (Lonza) from one nonsmoking donor was cultured in a bronchial epithelium growth medium (BEBM medium with SingleQuot Kit Supplements and Growth Factors, Lonza) on 75 cm2 until 85% confluent. Subsequently, the cells were passaged (3 × 105 cells on 1.2 cm5) onto collagen-coated transwell inserts in a 12-well culture plate (Costar, Corning) and were cultivated until confluent. Afterwards, the basolateral chamber medium was switched to the air-liquid interface (ALI) (1 : 1, BEBM : DMEM, 3.5 g/L D-glucose with SingleQuots), supplemented with 100 nM retinoic acid (Sigma-Aldrich). The apical surface of the cell culture was then exposed to air. The medium was replaced three times per week. Any mucus that has appeared on the top of cells was systematically removed. Cilia were observed 3–4 weeks posttransition to ALI. Differentiated cells were scratched by P200 Gilson pipette tip. Cell debris was removed, and a fresh medium was added to the basolateral chamber.
Time-lapse images were collected for 48 hours until complete wound closure at 15-minute intervals on the Olympus IX81 microscope, using xCellence software. The chamber was maintained at 36 ± 1°C and 5% CO2 atmosphere. The wound area and the ongoing repair process were analysed using ImageJ software [
RNA was isolated from NHBE cells with the use of miRCURY RNA Isolation Kit—Cell and Plant (Exiqon), according to the manufacturer’s instructions. Three biological replicates were collected at five consecutive time points: baseline (before wounding), 8, 16, 24, and 48 h after wounding. The amount of starting material was around 0.7 × 105 cells per well. Samples were stored at −70°C until the microarray experiment could be performed. The total RNA concentration was measured using NanoDrop 2000 spectrophotometer.
For profiling, we used the TaqMan Array Human MicroRNA Cards A and B that contain 754 human microRNAs. Reverse transcription was done using Megaplex Primer Pools (Human Pools A v.2.1 and B v.3.0, Thermo Fisher Scientific) and TaqMan® MicroRNA Reverse Transcription Kit (Thermo Fisher Scientific) according to the manufacturer’s protocol. TaqMan Universal PCR Master Mix, No AmpErase UNG (Thermo Fisher Scientific) was combined with diluted cDNA and loaded into TaqMan Array Human MicroRNA Card A v.2.0 or Card B v.3.0 and centrifuged. Quantitative real-time PCR was conducted in the 7900HT Fast Real-Time PCR System (Applied Biosystems). The reaction was performed in triplicate for each sample. Raw expression data were acquired from SDS 2.4 software (Applied Biosystems) and further analysed with RQ Manager 1.2.1 (Applied Biosystems). The comparative analysis of obtained datasets between baseline and each time point was accomplished in DataAssist v.3.01 software (Applied Biosystems). Undetermined values were considered as equal to the maximum allowable Ct value (37). In order to reduce background noise, we have excluded miRNAs that were not expressed in 90% of the samples. Outliers were removed from the analysis after applying a refined Grubbs’ outlier test. Each Ct value of target miRNA was normalized against the mean of the selected endogenous control, U6 snRNA-001973. Normalized miRNA expression was assessed against the baseline using the 2−ΔCt method. All up- or downregulated miRNAs with a fold expression ≥2 and
The clusters of miRNAs with similar expression profile over time were identified by cluster analysis in STEM (Short Time series Expression Miner) software available at
We have performed pathway enrichment analysis to identify common biological pathways for miRNAs with similar expression profile. For each miRNA, we have identified the best predicted mRNA target genes using miRNA BodyMap tool (
The list containing the best predictions was then analysed with the use of the Database for Annotation, Visualization and Integrated Discovery (DAVID) v.6.7 [
Based on available literature, target prediction results, and gene function, we have chosen the genes encoding isoforms of transforming growth factor
Sequences of the primers used for target gene expression analysis.
Gene | Direction | Sequence |
---|---|---|
TGFB1 | F | TTCAACACATCAGAGCTCC |
R | GCTGTATTTCTGGTACAGCT | |
TGFB3 | F | CAAATTCAAAGGCGTGGAC |
R | ATTAGATGAGGGTTGTGGTG | |
TGFBR1 | F | GAATCCTTCAAACGTGCTG |
R | TCATGAATTCCACCAATGGA | |
TGFBR2 | F | GCTGTATGGAGAAAGAATGAC |
R | CAGAATAAAGTCATGGTAGGG | |
TGFBR3 | F | TGATAATGGATTTCCGGGAG |
R | CTGCAATTAAACACCACGA | |
PPIA | F | AGACAAGGTCCCAAAGAC |
R | ACCACCCTGACACATAAA |
Based on the representative images from time-lapse microscopy, we have selected the following time points for miRNA profile analysis: the baseline immediately before injury (Figure
Representative images of wound repair at different time points: (a) 0 hrs, (b) 8 hrs, (c) 16 hrs, (d) 24 hrs, and (e) 48 hrs postwounding.
After normalization, we found out that 230 miRNAs from Card A and 3 miRNAs from Card B were expressed in normal bronchial epithelium. Analysis of miRNA expression has revealed a large number of genes with significantly increased or decreased expression at different time points (fold change above 2.0,
Volcano plots for different time points. T8: 8 h after wounding; T16: 16 h after wounding; T24: 24 h after wounding; T48: 48 h after wounding (reference: baseline; fold-change boundary: 2.0;
To investigate if miRNA genes share a common expression profile during epithelial repair, we performed cluster analysis using the STEM algorithm. This calculation revealed that, out of 40 model profiles, two profiles (profiles 9 and 17) showed significant enrichment during repair (
Profile 9 of differentially expressed miRNAs with similar expression pattern during wound repair.
Profile 17 of differentially expressed miRNAs with similar expression pattern during wound repair.
Our next step was to investigate whether miRNAs with the same expression patterns during airway epithelial repair may regulate target genes from the same biological pathways. Firstly, we have created a list of the best target mRNAs for each miRNA gene (8292 genes in total for profile 9 and 173939 genes for profile 17) and analysed these genes with DAVID online database. We have found several significantly enriched pathways for each profile that remained significant after correction for multiple testing (Tables
The results of pathway analysis of predicted target genes for profile 9 in DAVID database.
Category | Pathway | Enrichment score | No. of genes |
||
---|---|---|---|---|---|
KEGG | Pathways in cancer | 1.4 | 259 | 5.70 |
1.40 |
KEGG | PI3K-Akt signaling pathway | 1.4 | 221 | 1.90 |
2.60 |
KEGG | MAPK signaling pathway | 1.5 | 170 | 7.80 |
1.00 |
KEGG | Focal adhesion | 1.5 | 141 | 3.40 |
4.50 |
KEGG | Axon guidance | 1.6 | 92 | 1.50 |
2.00 |
KEGG | Ras signaling pathway | 1.4 | 146 | 7.30 |
9.70 |
KEGG | cGMP-PKG signaling pathway | 1.5 | 111 | 3.90 |
5.20 |
KEGG | Rap1 signaling pathway | 1.4 | 135 | 4.50 |
6.00 |
KEGG | Proteoglycans in cancer | 1.4 | 129 | 7.00 |
9.40 |
KEGG | FoxO signaling pathway | 1.5 | 92 | 1.00 |
1.40 |
KEGG | Regulation of actin cytoskeleton | 1.4 | 133 | 3.10 |
4.10 |
KEGG | Signaling pathways regulating pluripotency of stem cells | 1.5 | 94 | 3.70 |
4.90 |
KEGG | T cell receptor signaling pathway | 1.6 | 73 | 3.90 |
5.30 |
KEGG | Adrenergic signaling in cardiomyocytes | 1.5 | 97 | 5.00 |
6.60 |
KEGG | Melanoma | 1.7 | 54 | 5.00 |
6.70 |
KEGG | cAMP signaling pathway | 1.4 | 125 | 6.50 |
8.70 |
KEGG | Glutamatergic synapse | 1.5 | 78 | 1.40 |
1.90 |
KEGG | Wnt signaling pathway | 1.4 | 91 | 1.90 |
2.60 |
KEGG | HTLV-I infection | 1.3 | 154 | 2.10 |
2.80 |
KEGG | Dopaminergic synapse | 1.5 | 85 | 2.90 |
3.90 |
KEGG | Renal cell carcinoma | 1.7 | 49 | 3.00 |
3.90 |
KEGG | Neurotrophin signaling pathway | 1.5 | 80 | 4.70 |
6.30 |
KEGG | TNF signaling pathway | 1.5 | 72 | 5.70 |
7.60 |
KEGG | ErbB signaling pathway | 1.5 | 61 | 7.30 |
9.80 |
KEGG | Sphingolipid signaling pathway | 1.4 | 79 | 1.10 |
1.50 |
KEGG | Prostate cancer | 1.5 | 61 | 1.30 |
1.70 |
KEGG | Insulin signaling pathway | 1.4 | 88 | 2.10 |
2.80 |
KEGG | Inflammatory mediator regulation of TRP channels | 1.5 | 66 | 2.30 |
3.00 |
KEGG | Dorsoventral axis formation | 2 | 24 | 2.30 |
3.10 |
KEGG | Pancreatic cancer | 1.6 | 47 | 3.00 |
4.10 |
KEGG | Glioma | 1.6 | 47 | 3.00 |
4.10 |
KEGG | Oxytocin signaling pathway | 1.4 | 98 | 3.50 |
4.70 |
KEGG | Platelet activation | 1.4 | 83 | 3.50 |
4.70 |
KEGG | Insulin resistance | 1.4 | 71 | 3.60 |
4.80 |
The results of pathway analysis of predicted target genes for profile 17 in DAVID database.
Category | Pathway | Enrichment score | No. of genes |
||
---|---|---|---|---|---|
KEGG | Pathways in cancer | 1.5 | 128 | 4.40 |
5.90 |
KEGG | Proteoglycans in cancer | 1.7 | 74 | 1.10 |
1.40 |
KEGG | MAPK signaling pathway | 1.6 | 88 | 2.80 |
3.70 |
KEGG | Inflammatory mediator regulation of TRP channels | 2 | 42 | 5.80 |
7.80 |
KEGG | PI3K-Akt signaling pathway | 1.5 | 110 | 9.60 |
1.30 |
KEGG | GnRH signaling pathway | 2 | 39 | 1.30 |
1.80 |
KEGG | Rap1 signaling pathway | 1.6 | 72 | 3.20 |
4.30 |
Analysis of six targets from TGF
ANOVA analysis of TGF-
The most important finding of our study is the involvement of miRNA genes in the
Epithelial wound repair in the airways
The cluster analysis of time series miRNA expression data from one donor revealed distinct expression patterns of miRNA gene clusters during wound repair. The relationship between these miRNAs, their putative targets, and changes in individual protein levels during airway epithelial wound repair needs to be validated in future studies. However, the observed changes in the expression of the whole cluster of miRNA genes seem to be a consequence of the injury. For both profiles, we identified several signaling pathways responsible for the regulation of wound repair, some of which were also significant during the repair in undifferentiated 16HBE14o- cell line: the ErbB signaling pathway, MAPK signaling pathway, pathways in cancer, and neurotrophin signaling pathway [
Only the expression of one miRNA gene, miR-455-3p, was found to be significantly altered during the wound repair. MiR-455-3p was previously described as a tumour suppressor in several human cancers [
Martinez-Anton et al. [
TGF-
The main limitation of our study was the use of biological replicates from only one donor. While changes in miRNA expression during wound repair were observed in cells from one donor and in other airway epithelium cell line, we cannot state for certain that this will be the case for other donors, due to donor-to-donor variability. Additional experiments including more donors, as well as the confirmation of the miR-455-3p involvement in
In conclusion, we showed that the expression of multiple miRNAs is altered during airway epithelial repair in differentiated cells from one donor, suggesting their importance in the regulation of this process. We also observed two common expression profiles for several miRNA genes, and in silico analysis of predicted mRNA target genes has shown that they coordinate signaling pathways involved in the wound repair. We also found out that the
The access to the data can be made after contacting the Laboratory of Molecular and Cell Biology, Beata Narożna, e-mail:
The authors declare that they have no conflict of interest.
BN was a recipient of an EAACI Research Fellowship 2015. We thank the personnel from the Biomedical Imaging Unit and Primary Ciliary Dyskinesia (PCD) Diagnostic and Research Team, University of Southampton, for the assistance and technical support. Target gene expression analysis was supported by Poznan University of Medical Sciences grant no. 502-14-01105122-10347; cluster expression analysis and pathways prediction were financed by Polish National Science Centre grant no. 2017/25/N/NZ3/00332, whereas the rest of the study was funded by Polish National Science Centre grant no. 2011/01/M/NZ3/02906.
MiRNA genes assigned to each expression profile during wound repair (values given for each time point represent expression change after normalization in STEM software).